Documentation | PredictLeads

1.
Introduction

PredictLeads provides company intelligence datasets that are indicative of company performance.

Tracked are some 70+ million companies globally for signals like: News Events, Job Openings, Technographics, Key Customers data and more.

Use cases include:

Sales Enablement platfom offering enrichment
Using data in Market Intelligence solutions to identify companies in their buy mode
Identifying growing companies and sectors for investment purposes

Our data can be delivered through API Endpoints, in Flat File format or via Webhooks.

2.
Data Models

1.1.
CompaniesDataset

Companies dataset includes additional information about the company eg.: normalized company location, ticker, meta title, meta description...

CompaniesDataset	object
data	array
Company	object
id	string PredictLeads ID of the Company object. Format: uuid
type	string Type of the Company object. Constant: "company"
attributes	object
domain	string Company domain name.
ticker	string nullable Company stock symbol (ticker) if available.
company_name	string nullable Company name if available.
friendly_company_name	string Company friendly name.
location	string Company location.
location_data	array
LocationData	object
city	string nullable
state	string nullable
zip_code	string nullable
country	string nullable
region	string nullable
continent	string nullable
fuzzy_match	boolean nullable
language	string Company website's language.
meta_title	string Company website's meta title.
meta_description	string Company website's meta title.
relationships	object Contains references to related objects listed in property "included".
parent_company	object
data	object
id	string PredictLeads ID of the included CompanyLite object. Format: uuid
type	string Type of the included CompanyLite object. Constant: "company"
redirects_to	object
data	object
id	string PredictLeads ID of the included CompanyLite object. Format: uuid
type	string Type of the included CompanyLite object. Constant: "company"
included	array Contains objects referenced in property "relationships".
CompanyLite	object
id	string PredictLeads ID of the CompanyLite object. Format: uuid
type	string Type of the CompanyLite object. Constant: "company"
attributes	object
domain	string Company domain name.
ticker	string nullable Company stock symbol (ticker) if available.
company_name	string nullable Company name if available.
meta	object Meta information about response. Only shown in API responses.
count (optional)	integer Total number of results. NOTE: For performance reasons it will be omitted from response if "page" request parameter is not provided.
message (optional)	string Additional information about response.
message_type (optional)	string Type of the message. One of the values: "accepted"
redirects_to_domain	string nullable Domain that this company domain redirects to.
parent_company_domain	string nullable Parent company domain.

Example of CompaniesDataset object

{
 "title": "CompaniesDataset",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "description": "Companies dataset includes additional information about the company eg.: normalized company location,\nticker, meta title, meta description...\n",
 "type": "object",
 "required": [
 "data",
 "included"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "title": "Company",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes",
 "relationships"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the Company object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the Company object.",
 "const": "company"
 },
 "attributes": {
 "type": "object",
 "required": [
 "domain",
 "ticker",
 "company_name",
 "friendly_company_name",
 "location",
 "location_data",
 "language",
 "meta_title",
 "meta_description"
 ],
 "properties": {
 "domain": {
 "description": "Company domain name.",
 "type": "string"
 },
 "ticker": {
 "description": "Company stock symbol (ticker) if available.",
 "type": [
 "string",
 "null"
 ]
 },
 "company_name": {
 "description": "Company name if available.",
 "type": [
 "string",
 "null"
 ]
 },
 "friendly_company_name": {
 "description": "Company friendly name.",
 "type": "string"
 },
 "location": {
 "description": "Company location.",
 "type": "string"
 },
 "location_data": {
 "type": "array",
 "items": {
 "title": "LocationData",
 "type": "object",
 "required": [
 "city",
 "state",
 "zip_code",
 "country",
 "region",
 "continent",
 "fuzzy_match"
 ],
 "properties": {
 "city": {
 "type": [
 "string",
 "null"
 ]
 },
 "state": {
 "type": [
 "string",
 "null"
 ]
 },
 "zip_code": {
 "type": [
 "string",
 "null"
 ]
 },
 "country": {
 "type": [
 "string",
 "null"
 ]
 },
 "region": {
 "type": [
 "string",
 "null"
 ]
 },
 "continent": {
 "type": [
 "string",
 "null"
 ]
 },
 "fuzzy_match": {
 "type": [
 "boolean",
 "null"
 ]
 }
 }
 }
 },
 "language": {
 "description": "Company website's language.",
 "type": "string"
 },
 "meta_title": {
 "description": "Company website's meta title.",
 "type": "string"
 },
 "meta_description": {
 "description": "Company website's meta title.",
 "type": "string"
 }
 }
 },
 "relationships": {
 "description": "Contains references to related objects listed in property \"included\".",
 "type": "object",
 "properties": {
 "parent_company": {
 "title": "RelationshipCompanyLite",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included CompanyLite object.",
 "const": "company"
 }
 }
 }
 }
 },
 "redirects_to": {
 "title": "RelationshipCompanyLite",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included CompanyLite object.",
 "const": "company"
 }
 }
 }
 }
 }
 }
 }
 }
 }
 },
 "included": {
 "description": "Contains objects referenced in property \"relationships\".",
 "type": "array",
 "items": {
 "title": "CompanyLite",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the CompanyLite object.",
 "const": "company"
 },
 "attributes": {
 "type": "object",
 "required": [
 "domain",
 "ticker",
 "company_name"
 ],
 "properties": {
 "domain": {
 "description": "Company domain name.",
 "type": "string"
 },
 "ticker": {
 "description": "Company stock symbol (ticker) if available.",
 "type": [
 "string",
 "null"
 ]
 },
 "company_name": {
 "description": "Company name if available.",
 "type": [
 "string",
 "null"
 ]
 }
 }
 }
 }
 }
 },
 "meta": {
 "description": "Meta information about response. Only shown in API responses.\n",
 "type": "object",
 "properties": {
 "count": {
 "description": "Total number of results.\n \n**NOTE:** For performance reasons it will be omitted from response if \"page\" request parameter is not\nprovided.\n",
 "type": "integer",
 "example": 344
 },
 "message": {
 "description": "Additional information about response.\n",
 "type": "string"
 },
 "message_type": {
 "description": "Type of the message.\n",
 "enum": [
 "accepted"
 ]
 },
 "redirects_to_domain": {
 "description": "Domain that this company domain redirects to.\n",
 "type": [
 "string",
 "null"
 ],
 "example": "ibm.com"
 },
 "parent_company_domain": {
 "description": "Parent company domain.\n",
 "type": [
 "string",
 "null"
 ],
 "example": "ibm.com"
 }
 },
 "example": {
 "count": 344,
 "message": "...",
 "message_type": "...",
 "redirects_to_domain": "google.com",
 "parent_company_domain": "google.com"
 },
 "required": [
 "redirects_to_domain",
 "parent_company_domain"
 ]
 }
 },
 "example": {
 "data": [
 {
 "id": "4d621799-d317-58ba-a967-d09e07a5391f",
 "type": "company",
 "attributes": {
 "domain": "hubspot.com",
 "company_name": "HubSpot, Inc.",
 "friendly_company_name": "HubSpot",
 "ticker": "NYSE:HUBS",
 "meta_title": "HubSpot | Software, Tools, Resources for Your Business",
 "meta_description": "HubSpot's CRM platform contains the marketing, sales, service, operations, and website-building software you need to grow your business.",
 "language": "en",
 "location": "Cambridge, Massachusetts, United States",
 "location_data": [
 {
 "city": "Cambridge",
 "state": "Massachusetts",
 "zip_code": null,
 "country": "United States",
 "region": null,
 "continent": null,
 "fuzzy_match": null
 }
 ]
 },
 "relationships": {
 "parent_company": {
 "data": {
 "id": "1147dc74-fdbd-5c75-b376-53ec3047a7a6",
 "type": "company"
 }
 }
 }
 }
 ],
 "included": [
 {
 "id": "1147dc74-fdbd-5c75-b376-53ec3047a7a6",
 "type": "company",
 "attributes": {
 "domain": "google.com",
 "company_name": "Google",
 "ticker": "NASDAQ:GOOGL"
 }
 }
 ]
 }
}

1.2.
ConnectionsDataset

Company Connections describe relationships between companies.

It is sourced from Case Study pages, Testimonials and more. Connections are categorized into 8 categories such as vendor, partner, investor, parent...

Connections data can be used to get insight about the quality of company network, to identify partners in common, measure its progress over time and more.

ConnectionsDataset
data	array
Connection	object
id	string PredictLeads ID of the Connection object. Format: uuid
type	string Type of the Connection object. Constant: "connection"
attributes	object
category	string Connection category. One of the values: "other", "partner", "vendor", "integration", "investor", "sponsor", "parent", "rebranding", "published_in"
source_category	string Describes source where the category was found. One of the values: "undefined", "partner_page", "vendor_page", "integration_page", "investor_page", "sponsor_page", "parent_page", "case_study_page", "testimonial_page", "social_page", "manual_input", "domain_redirect", "same_subpage", "partner_section", "vendor_section", "integration_section", "investor_section", "sponsor_section", "parent_section", "case_study_section", "testimonial_section", "social_section", "published_in_section", "vendor_inverse_section", "investor_inverse_section", "parent_inverse_section", "footer", "header", "vendor_inverse_page", "cookie_section"
source_url	string nullable If the connection was found on a subpage, source_url points to that subpage. Format: uri
individual_source_url (optional)	string nullable Some connections contain additional information on their individual subpages. individual_source_url points to that subpage. Format: uri
context (optional)	string Text found near the connection. Often contains additional relationship information between the two companies.
first_seen_at	string Time & date (ISO 8601) of when the connection was first seen. Format: date-time
last_seen_at	string Time & date (ISO 8601) of when the connection was last seen. Format: date-time
relationships	object Contains references to related objects listed in property "included". Order of companies is guaranteed and necessary to preserve the meaning of the "category". For example:... Contains references to related objects listed in property "included". Order of companies is guaranteed and necessary to preserve the meaning of the "category". For example: Company1 is a vendor to Company2.
company1	object
data	object
id	string PredictLeads ID of the included CompanyLite object. Format: uuid
type	string Type of the included CompanyLite object. Constant: "company"
company2	object
data	object
id	string PredictLeads ID of the included CompanyLite object. Format: uuid
type	string Type of the included CompanyLite object. Constant: "company"
included	array Contains objects referenced in property "relationships".
CompanyLite	object
id	string PredictLeads ID of the CompanyLite object. Format: uuid
type	string Type of the CompanyLite object. Constant: "company"
attributes	object
domain	string Company domain name.
ticker	string nullable Company stock symbol (ticker) if available.
company_name	string nullable Company name if available.
meta	object Meta information about response. Only shown in API responses.
count (optional)	integer Total number of results. NOTE: For performance reasons it will be omitted from response if "page" request parameter is not provided.
message (optional)	string Additional information about response.
message_type (optional)	string Type of the message. One of the values: "accepted"
redirects_to_domain	string nullable Domain that this company domain redirects to.
parent_company_domain	string nullable Parent company domain.

Example of ConnectionsDataset object

{
 "title": "ConnectionsDataset",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "description": "Company Connections describe relationships between companies.\n \nIt is sourced from Case Study pages, Testimonials and more. Connections are categorized into 8 categories\nsuch as vendor, partner, investor, parent...\n \nConnections data can be used to get insight about the quality of company network, to identify partners in\ncommon, measure its progress over time and more.\n",
 "required": [
 "data",
 "included"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "title": "Connection",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes",
 "relationships"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the Connection object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the Connection object.",
 "const": "connection"
 },
 "attributes": {
 "type": "object",
 "required": [
 "category",
 "source_category",
 "source_url",
 "first_seen_at",
 "last_seen_at"
 ],
 "properties": {
 "category": {
 "description": "Connection category.",
 "type": "string",
 "enum": [
 "other",
 "partner",
 "vendor",
 "integration",
 "investor",
 "sponsor",
 "parent",
 "rebranding",
 "published_in"
 ]
 },
 "source_category": {
 "description": "Describes source where the category was found.",
 "enum": [
 "undefined",
 "partner_page",
 "vendor_page",
 "integration_page",
 "investor_page",
 "sponsor_page",
 "parent_page",
 "case_study_page",
 "testimonial_page",
 "social_page",
 "manual_input",
 "domain_redirect",
 "same_subpage",
 "partner_section",
 "vendor_section",
 "integration_section",
 "investor_section",
 "sponsor_section",
 "parent_section",
 "case_study_section",
 "testimonial_section",
 "social_section",
 "published_in_section",
 "vendor_inverse_section",
 "investor_inverse_section",
 "parent_inverse_section",
 "footer",
 "header",
 "vendor_inverse_page",
 "cookie_section"
 ]
 },
 "source_url": {
 "description": "If the connection was found on a subpage, source_url points to that subpage.",
 "type": [
 "null",
 "string"
 ],
 "format": "uri"
 },
 "individual_source_url": {
 "description": "Some connections contain additional information on their individual subpages. individual_source_url points to that subpage.",
 "type": [
 "null",
 "string"
 ],
 "format": "uri"
 },
 "context": {
 "description": "Text found near the connection. Often contains additional relationship information between the two companies.",
 "type": "string"
 },
 "first_seen_at": {
 "description": "Time & date (ISO 8601) of when the connection was first seen.",
 "type": "string",
 "format": "date-time"
 },
 "last_seen_at": {
 "description": "Time & date (ISO 8601) of when the connection was last seen.",
 "type": "string",
 "format": "date-time"
 }
 }
 },
 "relationships": {
 "description": "Contains references to related objects listed in property \"included\". Order of companies is guaranteed and necessary to preserve the meaning of the \"category\". For example: Company1 is a vendor to Company2.",
 "type": "object",
 "required": [
 "company1",
 "company2"
 ],
 "properties": {
 "company1": {
 "title": "RelationshipCompanyLite",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included CompanyLite object.",
 "const": "company"
 }
 }
 }
 }
 },
 "company2": {
 "title": "RelationshipCompanyLite",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included CompanyLite object.",
 "const": "company"
 }
 }
 }
 }
 }
 }
 }
 }
 }
 },
 "included": {
 "description": "Contains objects referenced in property \"relationships\".",
 "type": "array",
 "items": {
 "title": "CompanyLite",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the CompanyLite object.",
 "const": "company"
 },
 "attributes": {
 "type": "object",
 "required": [
 "domain",
 "ticker",
 "company_name"
 ],
 "properties": {
 "domain": {
 "description": "Company domain name.",
 "type": "string"
 },
 "ticker": {
 "description": "Company stock symbol (ticker) if available.",
 "type": [
 "string",
 "null"
 ]
 },
 "company_name": {
 "description": "Company name if available.",
 "type": [
 "string",
 "null"
 ]
 }
 }
 }
 }
 }
 },
 "meta": {
 "description": "Meta information about response. Only shown in API responses.\n",
 "type": "object",
 "properties": {
 "count": {
 "description": "Total number of results.\n \n**NOTE:** For performance reasons it will be omitted from response if \"page\" request parameter is not\nprovided.\n",
 "type": "integer",
 "example": 344
 },
 "message": {
 "description": "Additional information about response.\n",
 "type": "string"
 },
 "message_type": {
 "description": "Type of the message.\n",
 "enum": [
 "accepted"
 ]
 },
 "redirects_to_domain": {
 "description": "Domain that this company domain redirects to.\n",
 "type": [
 "string",
 "null"
 ],
 "example": "ibm.com"
 },
 "parent_company_domain": {
 "description": "Parent company domain.\n",
 "type": [
 "string",
 "null"
 ],
 "example": "ibm.com"
 }
 },
 "example": {
 "count": 344,
 "message": "...",
 "message_type": "...",
 "redirects_to_domain": "google.com",
 "parent_company_domain": "google.com"
 },
 "required": [
 "redirects_to_domain",
 "parent_company_domain"
 ]
 }
 },
 "example": {
 "data": [
 {
 "id": "fffffffa-3e46-434b-8b6d-f365c060c291",
 "type": "connection",
 "attributes": {
 "category": "other",
 "source_category": "undefined",
 "source_url": "https://graphixinfotech.com",
 "individual_source_url": null,
 "context": null,
 "first_seen_at": "2022-11-19T09:30:37Z",
 "last_seen_at": "2022-11-19T09:30:37Z"
 },
 "relationships": {
 "company1": {
 "data": {
 "id": "dd131b11-4730-5dfc-86ba-8a23e0a80103",
 "type": "company"
 }
 },
 "company2": {
 "data": {
 "id": "abb06037-6649-5831-a265-0e2fca150f94",
 "type": "company"
 }
 }
 }
 }
 ],
 "included": [
 {
 "id": "dd131b11-4730-5dfc-86ba-8a23e0a80103",
 "type": "company",
 "attributes": {
 "domain": "graphixinfotech.com",
 "company_name": "Graphixinfotech",
 "ticker": null
 }
 },
 {
 "id": "abb06037-6649-5831-a265-0e2fca150f94",
 "type": "company",
 "attributes": {
 "domain": "graphixinfotech.in",
 "company_name": null,
 "ticker": null
 }
 }
 ]
 }
}

1.3.
FinancingEventsDataset

Financing Events are events extracted from News Events data.

These include e.g. Venture capital rounds, Private Equity investments etc.

Financing events are grouped by date of investment and include investment amount, names of investors and more.

FinancingEventsDataset	object
data	array
FinancingEvent	object
id	string PredictLeads ID of the FinancingEvent object. Format: uuid
type	string Type of the FinancingEvent object. Constant: "financing_event"
attributes	object
effective_date	string nullable ISO 8601 date when the event has happened. Format: date-time
found_at	string ISO 8601 date when the event was discovered and created. Format: date-time
categories	array Example(s): [ "private_equity", "series_e", "venture" ]
Items	string One of the values: "private_equity", "equity_crowdfunding", "product_crowdfunding", "convertible_note", "debt_financing", "donation", "grant", "post_ipo_debt", "post_ipo_equity", "post_ipo_secondary", "post_ipo", "initial_public_offering", "initial_coin_offering", "initial_dex_offering", "venture", "pre_angel", "angel", "angel1", "angel2", "angel3", "pre_seed", "seed", "seed1", "seed2", "seed3", "pre_series_a", "series_a", "series_a1", "series_a2", "series_a3", "pre_series_b", "series_b", "series_b1", "series_b2", "series_b3", "pre_series_c", "series_c", "series_c1", "series_c2", "series_c3", "pre_series_d", "series_d", "series_d1", "series_d2", "series_d3", "pre_series_e", "series_e", "series_e1", "series_e2", "series_e3", "pre_series_f", "series_f", "series_f1", "series_f2", "series_f3", "pre_series_g", "series_g", "series_g1", "series_g2", "series_g3", "pre_series_h", "series_h", "series_h1", "series_h2", "series_h3", "pre_series_i", "series_i", "series_i1", "series_i2", "series_i3", "pre_series_j", "series_j", "series_j1", "series_j2", "series_j3"
financing_type	string nullable A financing type this event is mentioning. Example(s): "Series E funding"
financing_type_normalized	string nullable A normalized value of "financing_type" property, where possible. One of the values: "pre_angel", "angel", "angel1", "angel2", "angel3", "pre_seed", "seed", "seed1", "seed2", "seed3", "pre_series_a", "series_a", "series_a1", "series_a2", "series_a3", "pre_series_b", "series_b", "series_b1", "series_b2", "series_b3", "pre_series_c", "series_c", "series_c1", "series_c2", "series_c3", "pre_series_d", "series_d", "series_d1", "series_d2", "series_d3", "pre_series_e", "series_e", "series_e1", "series_e2", "series_e3", "pre_series_f", "series_f", "series_f1", "series_f2", "series_f3", "pre_series_g", "series_g", "series_g1", "series_g2", "series_g3", "pre_series_h", "series_h", "series_h1", "series_h2", "series_h3", "pre_series_i", "series_i", "series_i1", "series_i2", "series_i3", "pre_series_j", "series_j", "series_j1", "series_j2", "series_j3", null Example(s): "series_e"
amount	string nullable This is a string of a value as extracted from text. Example(s): "$555.5 million"
amount_normalized	integer nullable Integer value of "amount" property. Example(s): 555500000
source_urls	array
Items	string Format: uri Example(s): [ "https://www.chinamoneynetwork.com/2018/10... [ "https://www.chinamoneynetwork.com/2018/10/10/yunfeng-advantech-lead-300m-round-in-chinas-airbnb-copycat-xiaozhu-com" ]
relationships	object Contains references to related objects listed in property "included".
company	object
data	object
id	string PredictLeads ID of the included CompanyLite object. Format: uuid
type	string Type of the included CompanyLite object. Constant: "company"
investors	object
data	array
Items	object
id	string PredictLeads ID of the included CompanyLite object. Format: uuid
type	string Type of the included CompanyLite object. Constant: "company"
included	array Contains objects referenced in property "relationships".
CompanyLite	object
id	string PredictLeads ID of the CompanyLite object. Format: uuid
type	string Type of the CompanyLite object. Constant: "company"
attributes	object
domain	string Company domain name.
ticker	string nullable Company stock symbol (ticker) if available.
company_name	string nullable Company name if available.
meta	object Meta information about response. Only shown in API responses.
count (optional)	integer Total number of results. NOTE: For performance reasons it will be omitted from response if "page" request parameter is not provided.
message (optional)	string Additional information about response.
message_type (optional)	string Type of the message. One of the values: "accepted"
redirects_to_domain	string nullable Domain that this company domain redirects to.
parent_company_domain	string nullable Parent company domain.

Example of FinancingEventsDataset object

{
 "title": "FinancingEventsDataset",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "description": "Financing Events are events extracted from News Events data.\n \nThese include e.g. Venture capital rounds, Private Equity investments etc.\n \nFinancing events are grouped by date of investment and include investment amount, names of investors and\nmore.\n",
 "type": "object",
 "required": [
 "data",
 "included"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "title": "FinancingEvent",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes",
 "relationships"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the FinancingEvent object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the FinancingEvent object.",
 "const": "financing_event"
 },
 "attributes": {
 "type": "object",
 "required": [
 "effective_date",
 "found_at",
 "categories",
 "financing_type",
 "financing_type_normalized",
 "amount",
 "amount_normalized",
 "source_urls"
 ],
 "properties": {
 "effective_date": {
 "description": "ISO 8601 date when the event has happened.",
 "type": [
 "string",
 "null"
 ],
 "format": "date-time"
 },
 "found_at": {
 "description": "ISO 8601 date when the event was discovered and created.",
 "type": "string",
 "format": "date-time"
 },
 "categories": {
 "type": "array",
 "items": {
 "enum": [
 "private_equity",
 "equity_crowdfunding",
 "product_crowdfunding",
 "convertible_note",
 "debt_financing",
 "donation",
 "grant",
 "post_ipo_debt",
 "post_ipo_equity",
 "post_ipo_secondary",
 "post_ipo",
 "initial_public_offering",
 "initial_coin_offering",
 "initial_dex_offering",
 "venture",
 "pre_angel",
 "angel",
 "angel1",
 "angel2",
 "angel3",
 "pre_seed",
 "seed",
 "seed1",
 "seed2",
 "seed3",
 "pre_series_a",
 "series_a",
 "series_a1",
 "series_a2",
 "series_a3",
 "pre_series_b",
 "series_b",
 "series_b1",
 "series_b2",
 "series_b3",
 "pre_series_c",
 "series_c",
 "series_c1",
 "series_c2",
 "series_c3",
 "pre_series_d",
 "series_d",
 "series_d1",
 "series_d2",
 "series_d3",
 "pre_series_e",
 "series_e",
 "series_e1",
 "series_e2",
 "series_e3",
 "pre_series_f",
 "series_f",
 "series_f1",
 "series_f2",
 "series_f3",
 "pre_series_g",
 "series_g",
 "series_g1",
 "series_g2",
 "series_g3",
 "pre_series_h",
 "series_h",
 "series_h1",
 "series_h2",
 "series_h3",
 "pre_series_i",
 "series_i",
 "series_i1",
 "series_i2",
 "series_i3",
 "pre_series_j",
 "series_j",
 "series_j1",
 "series_j2",
 "series_j3"
 ]
 },
 "examples": [
 [
 "private_equity",
 "series_e",
 "venture"
 ]
 ]
 },
 "financing_type": {
 "description": "A financing type this event is mentioning.",
 "examples": [
 "Series E funding"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "financing_type_normalized": {
 "description": "A normalized value of \"financing_type\" property, where possible.",
 "enum": [
 "pre_angel",
 "angel",
 "angel1",
 "angel2",
 "angel3",
 "pre_seed",
 "seed",
 "seed1",
 "seed2",
 "seed3",
 "pre_series_a",
 "series_a",
 "series_a1",
 "series_a2",
 "series_a3",
 "pre_series_b",
 "series_b",
 "series_b1",
 "series_b2",
 "series_b3",
 "pre_series_c",
 "series_c",
 "series_c1",
 "series_c2",
 "series_c3",
 "pre_series_d",
 "series_d",
 "series_d1",
 "series_d2",
 "series_d3",
 "pre_series_e",
 "series_e",
 "series_e1",
 "series_e2",
 "series_e3",
 "pre_series_f",
 "series_f",
 "series_f1",
 "series_f2",
 "series_f3",
 "pre_series_g",
 "series_g",
 "series_g1",
 "series_g2",
 "series_g3",
 "pre_series_h",
 "series_h",
 "series_h1",
 "series_h2",
 "series_h3",
 "pre_series_i",
 "series_i",
 "series_i1",
 "series_i2",
 "series_i3",
 "pre_series_j",
 "series_j",
 "series_j1",
 "series_j2",
 "series_j3",
 null
 ],
 "examples": [
 "series_e"
 ]
 },
 "amount": {
 "description": "This is a string of a value as extracted from text.",
 "examples": [
 "$555.5 million"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "amount_normalized": {
 "description": "Integer value of \"amount\" property.",
 "examples": [
 555500000
 ],
 "type": [
 "integer",
 "null"
 ]
 },
 "source_urls": {
 "type": "array",
 "items": {
 "type": "string",
 "format": "uri",
 "examples": [
 [
 "https://www.chinamoneynetwork.com/2018/10/10/yunfeng-advantech-lead-300m-round-in-chinas-airbnb-copycat-xiaozhu-com"
 ]
 ]
 }
 }
 }
 },
 "relationships": {
 "description": "Contains references to related objects listed in property \"included\".",
 "type": "object",
 "required": [
 "company",
 "investors"
 ],
 "properties": {
 "company": {
 "title": "RelationshipCompanyLite",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included CompanyLite object.",
 "const": "company"
 }
 }
 }
 }
 },
 "investors": {
 "title": "RelationshipCompanyLite",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included CompanyLite object.",
 "const": "company"
 }
 }
 }
 }
 }
 }
 }
 }
 }
 }
 },
 "included": {
 "description": "Contains objects referenced in property \"relationships\".",
 "type": "array",
 "items": {
 "title": "CompanyLite",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the CompanyLite object.",
 "const": "company"
 },
 "attributes": {
 "type": "object",
 "required": [
 "domain",
 "ticker",
 "company_name"
 ],
 "properties": {
 "domain": {
 "description": "Company domain name.",
 "type": "string"
 },
 "ticker": {
 "description": "Company stock symbol (ticker) if available.",
 "type": [
 "string",
 "null"
 ]
 },
 "company_name": {
 "description": "Company name if available.",
 "type": [
 "string",
 "null"
 ]
 }
 }
 }
 }
 }
 },
 "meta": {
 "description": "Meta information about response. Only shown in API responses.\n",
 "type": "object",
 "properties": {
 "count": {
 "description": "Total number of results.\n \n**NOTE:** For performance reasons it will be omitted from response if \"page\" request parameter is not\nprovided.\n",
 "type": "integer",
 "example": 344
 },
 "message": {
 "description": "Additional information about response.\n",
 "type": "string"
 },
 "message_type": {
 "description": "Type of the message.\n",
 "enum": [
 "accepted"
 ]
 },
 "redirects_to_domain": {
 "description": "Domain that this company domain redirects to.\n",
 "type": [
 "string",
 "null"
 ],
 "example": "ibm.com"
 },
 "parent_company_domain": {
 "description": "Parent company domain.\n",
 "type": [
 "string",
 "null"
 ],
 "example": "ibm.com"
 }
 },
 "example": {
 "count": 344,
 "message": "...",
 "message_type": "...",
 "redirects_to_domain": "google.com",
 "parent_company_domain": "google.com"
 },
 "required": [
 "redirects_to_domain",
 "parent_company_domain"
 ]
 }
 },
 "example": {
 "data": [
 {
 "id": "e9a08a0c-bcb3-4536-a766-cac013869aac",
 "type": "financing_event",
 "attributes": {
 "effective_date": "2024-07-30T02:00:00+02:00",
 "found_at": "2024-07-30T02:00:00+02:00",
 "categories": [
 "series_c",
 "venture"
 ],
 "financing_type": "Series C",
 "financing_type_normalized": "series_c",
 "amount": "$200M",
 "amount_normalized": 200000000,
 "source_urls": [
 "https://www.prnewswire.com/news-releases/flo-health-secures-more-than-200m-investment-from-general-atlantic-to-revolutionize-womens-health-first-purely-digital-consumer-womens-health-app-to-achieve-unicorn-status-302208728.html",
 "https://techcrunch.com/2024/07/30/fertility-tracking-app-flo-health-raises-200m-at-a-1b-valuation"
 ]
 },
 "relationships": {
 "company": {
 "data": {
 "id": "a6ea5ea7-7301-50f8-ba9f-36d34cbd2d76",
 "type": "company"
 }
 },
 "investors": {
 "data": [
 {
 "id": "b0147ffb-df06-5349-b236-e195b61ac340",
 "type": "company"
 }
 ]
 }
 }
 }
 ],
 "included": [
 {
 "id": "b0147ffb-df06-5349-b236-e195b61ac340",
 "type": "company",
 "attributes": {
 "domain": "generalatlantic.com",
 "company_name": "General Atlantic",
 "ticker": null
 }
 },
 {
 "id": "a6ea5ea7-7301-50f8-ba9f-36d34cbd2d76",
 "type": "company",
 "attributes": {
 "domain": "flo.health",
 "company_name": "Flo",
 "ticker": null
 }
 }
 ]
 }
}

1.4.
JobOpeningsDataset

Job Openings are found on companies' career sites and job boards. Job Openings and other descriptions can be used to identify company investment areas, current priorities and future developments.

Provided are fields like Job Category (marketing, sales, HR, support...), job description, salary, location and many more.

JobOpeningsDataset	object
data	array
JobOpening	object
id	string PredictLeads ID of the JobOpening object. Format: uuid
type	string Type of the JobOpening object. Constant: "job_opening"
attributes	object
title	string Job title.
description	string nullable Job opening description.
url	string URL of the web page where the data was found. Format: uri
first_seen_at	string Time & date (ISO 8601) of when the job opening was first seen. Format: date-time
last_seen_at	string Time & date (ISO 8601) of when the job opening was last seen. Format: date-time
last_processed_at	string Time & date (ISO 8601) of when the job opening was last sent to processing. Format: date-time
contract_types	array Zero or more types describing the job opening. Example(s): [ "full time", "remote" ]
Items	string
categories	array Zero or more organizational categories of the job opening. Example(s): [ "finance", "data_analysis" ]
Items	string
onet_data	object Inferred ONET data for this job opening. ONET is a source and standardization of occupational information. More info on the O*NET data: onetonline.org.
code	string nullable O*NET category code representing this job opening. Example(s): "29-1021.00"
family	string nullable O*NET family name matching the code for this job opening. Example(s): "Healthcare Practitioners and Technical"
occupation_name	string nullable O*NET occupation name matching the code for this job opening. Example(s): "Dentists, General"
salary	string nullable Raw salary string. Example(s): "£40000 - £50000 GBP"
salary_data	object Parsed raw salary data.
salary_low	number nullable Salary range lower bound. If salary is not a range both low and high values will be the same.
salary_high	number nullable Salary range upper bound.
salary_currency	string nullable Salary currency.
salary_low_usd	number nullable Salary range lower bound, converted to USD.
salary_high_usd	number nullable Salary range upper bound, converted to USD.
salary_time_unit	string nullable Salary time unit. One of the values: "hour", "day", "week", "month", "year", null
seniority	string nullable Seniority inferred from the job opening title.
status	string nullable null or "closed" if keyword "job no longer available", "vacancy listing expired", "position filled", etc. is found on job opening site. One of the values: "closed", null
language	string nullable Language in which a job opening description is written.
location	string nullable Raw location string.
location_data	array
LocationData	object
city	string nullable
state	string nullable
zip_code	string nullable
country	string nullable
region	string nullable
continent	string nullable
fuzzy_match	boolean nullable
tags	array Includes keywords found under job opening description.
Items	string
relationships	object Contains references to related objects listed in property "included".
company	object
data	object
id	string PredictLeads ID of the included CompanyLite object. Format: uuid
type	string Type of the included CompanyLite object. Constant: "company"
included	array Contains objects referenced in property "relationships".
CompanyLite	object
id	string PredictLeads ID of the CompanyLite object. Format: uuid
type	string Type of the CompanyLite object. Constant: "company"
attributes	object
domain	string Company domain name.
ticker	string nullable Company stock symbol (ticker) if available.
company_name	string nullable Company name if available.
meta	object Meta information about response. Only shown in API responses.
count (optional)	integer Total number of results. NOTE: For performance reasons it will be omitted from response if "page" request parameter is not provided.
message (optional)	string Additional information about response.
message_type (optional)	string Type of the message. One of the values: "accepted"
redirects_to_domain	string nullable Domain that this company domain redirects to.
parent_company_domain	string nullable Parent company domain.

Example of JobOpeningsDataset object

{
  "data": [
    {
      "id": "4d5ac23c-5824-427d-96c6-4e8d50a4241a",
      "type": "job_opening",
      "attributes": {
        "title": "AI Engineer",
        "description": "**AI Engineer**\n\n\n**$50K - $120K** /  0.10% - 0.50%  **Location** London, England, GB  **Job Type** Full-time  **Experience** Any (new grads ok)   Apply to Terra and hundreds of other fast-growing YC startups with a single profile.    ****\n\nAPI for developers to connect to health data from wearables and…\n\n\n**About the role**\n\nWe work in super small teams, with extremely high velocity.\n\nWe are building multiple AI products, such as OdinAI, Graph API, and many more\n\nAs an AI engineer, you'll work in a small team, with maximum autonomy.\n\n* Be behind the curtain, creating, testing, and implementing AI models\n* Engage in data analysis, machine learning, and natural language processing.\n* Fine-tuning the AI's performance and many more\n\nPreferred background:\n\nBachelor's or Master's degree in STEM. Strong technical skills (e.g. familiarity with a programming language such as Python, JavaScript, or Ruby) Experience with growth marketing, product development, and user acquisition/retention\n\n\n**Must haves.**\n\n-Must have done this before, or be willing to learn fast.\n\n-Must be fast. We operate at 200mph, and so do you.\n\n-Must be relentless. We never stop, so you are even faster than us.\n\n-Must have customer driven. We think customers first and so should you.\n\n-Must be driven and go above and beyond.\n\n-Must be able to explain and communicate clearly.\n\n-Must be prepared to challenge us and disagree. We are thinking and acting as a team.\n\n-Must be able to work autonomously.\n\n-Must want to work hard.\n\n-Last but not least, you are genuinely a nice person.\n\n**Big plus** You are an athlete, or care about quantifying your data\n\n\n**About Terra**\n\n**What we do**\n\nTerra is an API that makes it easy for apps to connect to wearables. Currently, apps and developers in the fitness, wellness, sleep, and other health spaces are using us. Terra was launched in early 2021, and since then we’ve been growing like crazy. But this is just the beginning.\n\n\n**The goal and vision**\n\nThink if Spotify and Netflix create music and movies based on your heart rate, and stress levels, in real time. We want to enable apps to achieve that reality, through our super easy to use API.\n\n\n**Funding**\n\nTo achieve our grand goal, we couldn’t have made it without the support from some incredible investors. We are lucky enough to be supported by , ,  and we were part of  batch.\n\n, and learn who we are \n\nLocation: London, United Kingdom",
        "url": "https://www.ycombinator.com/companies/terra/jobs/0f5CP0r-ai-engineer",
        "first_seen_at": "2024-09-09T07:07:33Z",
        "last_seen_at": "2024-09-11T07:17:31Z",
        "last_processed_at": "2024-09-11T07:38:43Z",
        "contract_types": [
          "full time"
        ],
        "categories": [
          "engineering"
        ],
        "onet_data": {
          "code": "15-1252.00",
          "family": "Computer and Mathematical",
          "occupation_name": "Software Developers"
        },
        "salary": "$50000 - $120000",
        "salary_data": {
          "salary_low": 50000.0,
          "salary_high": 120000.0,
          "salary_currency": "USD",
          "salary_low_usd": 50000.0,
          "salary_high_usd": 120000.0,
          "salary_time_unit": "year"
        },
        "seniority": "non_manager",
        "status": "closed",
        "language": "en",
        "location": "London, United Kingdom",
        "location_data": [
          {
            "city": "London",
            "state": null,
            "zip_code": null,
            "country": "United Kingdom",
            "region": null,
            "continent": null,
            "fuzzy_match": false
          }
        ],
        "tags": [
          "Growth",
          "Python",
          "Ruby",
          "JavaScript"
        ]
      },
      "relationships": {
        "company": {
          "data": {
            "id": "eb383981-96cf-5d24-8e09-15f71f35879e",
            "type": "company"
          }
        }
      }
    }
  ],
  "included": [
    {
      "id": "eb383981-96cf-5d24-8e09-15f71f35879e",
      "type": "company",
      "attributes": {
        "domain": "tryterra.co",
        "company_name": "Terra Enabling Developers, Ltd.",
        "ticker": null
      }
    }
  ]
}

{
 "title": "JobOpeningsDataset",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "description": "Job Openings are found on companies' career sites and job boards. Job Openings and other descriptions can\nbe used to identify company investment areas, current priorities and future developments.\n \nProvided are fields like Job Category (marketing, sales, HR, support...), job description, salary, location\nand many more.\n",
 "type": "object",
 "required": [
 "data",
 "included"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "title": "JobOpening",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes",
 "relationships"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the JobOpening object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the JobOpening object.",
 "const": "job_opening"
 },
 "attributes": {
 "type": "object",
 "required": [
 "title",
 "description",
 "url",
 "first_seen_at",
 "last_seen_at",
 "last_processed_at",
 "contract_types",
 "categories",
 "onet_data",
 "salary",
 "salary_data",
 "seniority",
 "status",
 "language",
 "location",
 "location_data",
 "tags"
 ],
 "properties": {
 "title": {
 "description": "Job title.",
 "type": "string"
 },
 "description": {
 "description": "Job opening description.",
 "type": [
 "string",
 "null"
 ]
 },
 "url": {
 "description": "URL of the web page where the data was found.",
 "type": "string",
 "format": "uri"
 },
 "first_seen_at": {
 "description": "Time & date (ISO 8601) of when the job opening was first seen.",
 "type": "string",
 "format": "date-time"
 },
 "last_seen_at": {
 "description": "Time & date (ISO 8601) of when the job opening was last seen.",
 "type": "string",
 "format": "date-time"
 },
 "last_processed_at": {
 "description": "Time & date (ISO 8601) of when the job opening was last sent to processing.",
 "type": "string",
 "format": "date-time"
 },
 "contract_types": {
 "description": "Zero or more types describing the job opening.",
 "examples": [
 [
 "full time",
 "remote"
 ]
 ],
 "type": "array",
 "items": [
 {
 "type": "string"
 }
 ]
 },
 "categories": {
 "description": "Zero or more organizational categories of the job opening.",
 "examples": [
 [
 "finance",
 "data_analysis"
 ]
 ],
 "type": "array",
 "items": [
 {
 "type": "string"
 }
 ]
 },
 "onet_data": {
 "description": "Inferred O\\*NET data for this job opening. O\\*NET is a source and standardization of occupational information. More info on the O\\*NET data: onetonline.org.",
 "type": "object",
 "required": [
 "code",
 "family",
 "occupation_name"
 ],
 "properties": {
 "code": {
 "description": "O\\*NET category code representing this job opening.",
 "examples": [
 "29-1021.00"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "family": {
 "description": "O\\*NET family name matching the code for this job opening.",
 "examples": [
 "Healthcare Practitioners and Technical"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "occupation_name": {
 "description": "O\\*NET occupation name matching the code for this job opening.",
 "examples": [
 "Dentists, General"
 ],
 "type": [
 "string",
 "null"
 ]
 }
 }
 },
 "salary": {
 "description": "Raw salary string.",
 "examples": [
 "£40000 - £50000 GBP"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "salary_data": {
 "description": "Parsed raw salary data.",
 "type": "object",
 "required": [
 "salary_low",
 "salary_high",
 "salary_currency",
 "salary_low_usd",
 "salary_high_usd",
 "salary_time_unit"
 ],
 "properties": {
 "salary_low": {
 "description": "Salary range lower bound. If salary is not a range both low and high values will be the same.",
 "type": [
 "number",
 "null"
 ]
 },
 "salary_high": {
 "description": "Salary range upper bound.",
 "type": [
 "number",
 "null"
 ]
 },
 "salary_currency": {
 "description": "Salary currency.",
 "type": [
 "string",
 "null"
 ]
 },
 "salary_low_usd": {
 "description": "Salary range lower bound, converted to USD.",
 "type": [
 "number",
 "null"
 ]
 },
 "salary_high_usd": {
 "description": "Salary range upper bound, converted to USD.",
 "type": [
 "number",
 "null"
 ]
 },
 "salary_time_unit": {
 "description": "Salary time unit.",
 "enum": [
 "hour",
 "day",
 "week",
 "month",
 "year",
 null
 ]
 }
 }
 },
 "seniority": {
 "description": "Seniority inferred from the job opening title.",
 "type": [
 "string",
 "null"
 ]
 },
 "status": {
 "description": "null or \"closed\" if keyword \"job no longer available\", \"vacancy listing expired\", \"position filled\", etc. is found on job opening site.",
 "enum": [
 "closed",
 null
 ]
 },
 "language": {
 "description": "Language in which a job opening description is written.",
 "type": [
 "string",
 "null"
 ]
 },
 "location": {
 "description": "Raw location string.",
 "type": [
 "string",
 "null"
 ]
 },
 "location_data": {
 "type": "array",
 "items": {
 "title": "LocationData",
 "type": "object",
 "required": [
 "city",
 "state",
 "zip_code",
 "country",
 "region",
 "continent",
 "fuzzy_match"
 ],
 "properties": {
 "city": {
 "type": [
 "string",
 "null"
 ]
 },
 "state": {
 "type": [
 "string",
 "null"
 ]
 },
 "zip_code": {
 "type": [
 "string",
 "null"
 ]
 },
 "country": {
 "type": [
 "string",
 "null"
 ]
 },
 "region": {
 "type": [
 "string",
 "null"
 ]
 },
 "continent": {
 "type": [
 "string",
 "null"
 ]
 },
 "fuzzy_match": {
 "type": [
 "boolean",
 "null"
 ]
 }
 }
 }
 },
 "tags": {
 "description": "Includes keywords found under job opening description.",
 "type": "array",
 "items": [
 {
 "type": "string"
 }
 ]
 }
 }
 },
 "relationships": {
 "description": "Contains references to related objects listed in property \"included\".",
 "type": "object",
 "required": [
 "company"
 ],
 "properties": {
 "company": {
 "title": "RelationshipCompanyLite",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included CompanyLite object.",
 "const": "company"
 }
 }
 }
 }
 }
 }
 }
 }
 }
 },
 "included": {
 "description": "Contains objects referenced in property \"relationships\".",
 "type": "array",
 "items": {
 "title": "CompanyLite",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the CompanyLite object.",
 "const": "company"
 },
 "attributes": {
 "type": "object",
 "required": [
 "domain",
 "ticker",
 "company_name"
 ],
 "properties": {
 "domain": {
 "description": "Company domain name.",
 "type": "string"
 },
 "ticker": {
 "description": "Company stock symbol (ticker) if available.",
 "type": [
 "string",
 "null"
 ]
 },
 "company_name": {
 "description": "Company name if available.",
 "type": [
 "string",
 "null"
 ]
 }
 }
 }
 }
 }
 },
 "meta": {
 "description": "Meta information about response. Only shown in API responses.\n",
 "type": "object",
 "properties": {
 "count": {
 "description": "Total number of results.\n \n**NOTE:** For performance reasons it will be omitted from response if \"page\" request parameter is not\nprovided.\n",
 "type": "integer",
 "example": 344
 },
 "message": {
 "description": "Additional information about response.\n",
 "type": "string"
 },
 "message_type": {
 "description": "Type of the message.\n",
 "enum": [
 "accepted"
 ]
 },
 "redirects_to_domain": {
 "description": "Domain that this company domain redirects to.\n",
 "type": [
 "string",
 "null"
 ],
 "example": "ibm.com"
 },
 "parent_company_domain": {
 "description": "Parent company domain.\n",
 "type": [
 "string",
 "null"
 ],
 "example": "ibm.com"
 }
 },
 "example": {
 "count": 344,
 "message": "...",
 "message_type": "...",
 "redirects_to_domain": "google.com",
 "parent_company_domain": "google.com"
 },
 "required": [
 "redirects_to_domain",
 "parent_company_domain"
 ]
 }
 },
 "example": {
 "data": [
 {
 "id": "4d5ac23c-5824-427d-96c6-4e8d50a4241a",
 "type": "job_opening",
 "attributes": {
 "title": "AI Engineer",
 "description": "**AI Engineer**\n\n\n**$50K - $120K** / 0.10% - 0.50% **Location** London, England, GB **Job Type** Full-time **Experience** Any (new grads ok) Apply to Terra and hundreds of other fast-growing YC startups with a single profile. ****\n\nAPI for developers to connect to health data from wearables and…\n\n\n**About the role**\n\nWe work in super small teams, with extremely high velocity.\n\nWe are building multiple AI products, such as OdinAI, Graph API, and many more\n\nAs an AI engineer, you'll work in a small team, with maximum autonomy.\n\n* Be behind the curtain, creating, testing, and implementing AI models\n* Engage in data analysis, machine learning, and natural language processing.\n* Fine-tuning the AI's performance and many more\n\nPreferred background:\n\nBachelor's or Master's degree in STEM. Strong technical skills (e.g. familiarity with a programming language such as Python, JavaScript, or Ruby) Experience with growth marketing, product development, and user acquisition/retention\n\n\n**Must haves.**\n\n-Must have done this before, or be willing to learn fast.\n\n-Must be fast. We operate at 200mph, and so do you.\n\n-Must be relentless. We never stop, so you are even faster than us.\n\n-Must have customer driven. We think customers first and so should you.\n\n-Must be driven and go above and beyond.\n\n-Must be able to explain and communicate clearly.\n\n-Must be prepared to challenge us and disagree. We are thinking and acting as a team.\n\n-Must be able to work autonomously.\n\n-Must want to work hard.\n\n-Last but not least, you are genuinely a nice person.\n\n**Big plus** You are an athlete, or care about quantifying your data\n\n\n**About Terra**\n\n**What we do**\n\nTerra is an API that makes it easy for apps to connect to wearables. Currently, apps and developers in the fitness, wellness, sleep, and other health spaces are using us. Terra was launched in early 2021, and since then we’ve been growing like crazy. But this is just the beginning.\n\n\n**The goal and vision**\n\nThink if Spotify and Netflix create music and movies based on your heart rate, and stress levels, in real time. We want to enable apps to achieve that reality, through our super easy to use API.\n\n\n**Funding**\n\nTo achieve our grand goal, we couldn’t have made it without the support from some incredible investors. We are lucky enough to be supported by , , and we were part of batch.\n\n, and learn who we are \n\nLocation: London, United Kingdom",
 "url": "https://www.ycombinator.com/companies/terra/jobs/0f5CP0r-ai-engineer",
 "first_seen_at": "2024-09-09T07:07:33Z",
 "last_seen_at": "2024-09-11T07:17:31Z",
 "last_processed_at": "2024-09-11T07:38:43Z",
 "contract_types": [
 "full time"
 ],
 "categories": [
 "engineering"
 ],
 "onet_data": {
 "code": "15-1252.00",
 "family": "Computer and Mathematical",
 "occupation_name": "Software Developers"
 },
 "salary": "$50000 - $120000",
 "salary_data": {
 "salary_low": 50000.0,
 "salary_high": 120000.0,
 "salary_currency": "USD",
 "salary_low_usd": 50000.0,
 "salary_high_usd": 120000.0,
 "salary_time_unit": "year"
 },
 "seniority": "non_manager",
 "status": "closed",
 "language": "en",
 "location": "London, United Kingdom",
 "location_data": [
 {
 "city": "London",
 "state": null,
 "zip_code": null,
 "country": "United Kingdom",
 "region": null,
 "continent": null,
 "fuzzy_match": false
 }
 ],
 "tags": [
 "Growth",
 "Python",
 "Ruby",
 "JavaScript"
 ]
 },
 "relationships": {
 "company": {
 "data": {
 "id": "eb383981-96cf-5d24-8e09-15f71f35879e",
 "type": "company"
 }
 }
 }
 }
 ],
 "included": [
 {
 "id": "eb383981-96cf-5d24-8e09-15f71f35879e",
 "type": "company",
 "attributes": {
 "domain": "tryterra.co",
 "company_name": "Terra Enabling Developers, Ltd.",
 "ticker": null
 }
 }
 ]
 }
}

1.5.
NewsEventsDataset

News Events are categorized events found in News Articles from various sources such as press releases, industry news, blogs and PR sites.

All events are provided in a structured format for all categories and always include company domain name to which the News Event belongs.

Because of the structured nature the News Events are better deduplicated and there is high signal to noise ratio.

News Event signals also include News Article title, body, author and more.

NewsEventsDataset	object
data	array
NewsEvent	object
id	string PredictLeads ID of the NewsEvent object. Format: uuid
type	string Type of the NewsEvent object. Constant: "news_event"
attributes	object
summary	string A short, human readable excerpt of this event data.
category	string Name of the category this event represents. One of the values: "hires", "retires_from", "leaves", "promotes", "receives_financing", "goes_public", "invests_into", "acquires", "opens_new_location", "recognized_as", "receives_award", "partners_with", "merges_with", "launches", "is_developing", "integrates_with", "expands_offices_to", "expands_offices_in", "signs_new_client", "sells_assets_to", "invests_into_assets", "expands_facilities", "increases_headcount_by", "decreases_headcount_by", "closes_offices_in", "files_suit_against", "has_issues_with", "identified_as_competitor_of" Example(s): "invests_into"
found_at	string ISO 8601 date when the event was discovered and created.
confidence	number Floating point number of confidence between 0 and 1. Currently PredictLeads takes care of limiting the News Events so as to have a good balance between match rate and accuracy. Thus we do not... Floating point number of confidence between 0 and 1. Currently PredictLeads takes care of limiting the News Events so as to have a good balance between match rate and accuracy. Thus we do not recommend filtering News Events further based on this confidence.
article_sentence	string Sentence in the News Article where event information was found.
human_approved	boolean If true, this event has been manually verified by a human.
planning	boolean When true, the event is planned to happen. E.g. the company might plan to launch a product in December.
amount	string nullable This is a string of a value as extracted from text. Example(s): "$1.8 million"
amount_normalized	integer nullable Integer value of "amount" property. Example(s): 18000000
assets	string nullable Assets found in the News Event. Example(s): "hydrogen production facility"
assets_tags	array An array of assets tags extracted from "assets" property. Example(s): [ "production" ]
Items	string
award	string nullable Award type this event is announcing. Example(s): "Pierre Potier Award"
contact	string nullable Person name that the News Event mentions. Example(s): "Elon Musk"
conference	string nullable Conference name where the News Event was mentioned at. Example(s): "World Business Outlook Awards"
effective_date	string nullable ISO 8601 date (without time) the News Event is mentioning, in string format. Example(s): "2024-04-05"
division	string nullable Whether event concerns a specific division.
financing_type	string nullable A financing type this event is mentioning. Example(s): "Series B funding"
financing_type_normalized	string nullable A normalized value of "financing_type" property, where possible. One of the values: "pre_angel", "angel", "angel1", "angel2", "angel3", "pre_seed", "seed", "seed1", "seed2", "seed3", "pre_series_a", "series_a", "series_a1", "series_a2", "series_a3", "pre_series_b", "series_b", "series_b1", "series_b2", "series_b3", "pre_series_c", "series_c", "series_c1", "series_c2", "series_c3", "pre_series_d", "series_d", "series_d1", "series_d2", "series_d3", "pre_series_e", "series_e", "series_e1", "series_e2", "series_e3", "pre_series_f", "series_f", "series_f1", "series_f2", "series_f3", "pre_series_g", "series_g", "series_g1", "series_g2", "series_g3", "pre_series_h", "series_h", "series_h1", "series_h2", "series_h3", "pre_series_i", "series_i", "series_i1", "series_i2", "series_i3", "pre_series_j", "series_j", "series_j1", "series_j2", "series_j3", null Example(s): "series_b"
financing_type_tags	array An array of financing type categories. Example(s): [ "equity" ]
Items	string
headcount	integer nullable A number of people mentioned organization is involved with. Example(s): 200
job_title	string nullable Job title this event is mentioning. Example(s): "Director of Media and Analytics"
job_title_tags	array An array of job title tags. Example(s): [ "marketing", "directors" ]
Items	string
location	string nullable Location of where the event happened or where the organization has expanded/relocated. Example(s): "Menlo Park, California, United States"
location_data	array
LocationData	object
city	string nullable
state	string nullable
zip_code	string nullable
country	string nullable
region	string nullable
continent	string nullable
fuzzy_match	boolean nullable
product	string nullable Name of the product as mentioned by this event. Example(s): "Credit Pass"
product_data	object
full_text	string nullable Full product name as recognized by Named Entity Recognition, without further cleaning of the product name. Example(s): "Plant Control System", "3D printing service... "Plant Control System", "3D printing service for spare parts"
name	string nullable Cleaned name of the product referenced in the News Event. Example(s): "VideoPoet"
release_type	string nullable Product release type. Example(s): "major"
release_version	string nullable Product release version. Example(s): "v3.0"
fuzzy_match	boolean nullable When true we might not have extracted the product name cleanly. When false exact product name should be extracted well.
product_tags	array List of tags regarding the product. Example(s): [ "mobile", "online_technology" ]
Items	string
recognition	string nullable Name of the recognition the company received. Example(s): "50 Smartest Companies"
vulnerability	string nullable Company issue. Example(s): "account suspension errors"
relationships	object Contains references to related objects listed in property "included".
company1	object
data	object
id	string PredictLeads ID of the included CompanyLite object. Format: uuid
type	string Type of the included CompanyLite object. Constant: "company"
company2	object
data	object
id	string PredictLeads ID of the included CompanyLite object. Format: uuid
type	string Type of the included CompanyLite object. Constant: "company"
most_relevant_source	object
data	object
id	string PredictLeads ID of the included NewsArticleLite object. Format: uuid
type	string Type of the included NewsArticleLite object. Constant: "news_article"
included	array Contains objects referenced in property "relationships".
Items
One of	Discriminator property name: "type"
CompanyLite	object
id	string PredictLeads ID of the CompanyLite object. Format: uuid
type	string Type of the CompanyLite object. Constant: "company"
attributes	object
domain	string Company domain name.
ticker	string nullable Company stock symbol (ticker) if available.
company_name	string nullable Company name if available.
or
NewsArticleLite	object
id	string PredictLeads ID of the NewsArticleLite object. Format: uuid
type	string Type of the NewsArticleLite object. Constant: "news_article"
attributes	object
url	string News Article source URL. Format: uri
title	string Prettified News Article title or heading.
author	string nullable News Article author name.
image_url	string nullable News Article most relevant image URL.
published_at	string ISO 8601 date when News Article was published. Format: date-time
body	string News Article text (article content).
meta	object Meta information about response. Only shown in API responses.
count (optional)	integer Total number of results. NOTE: For performance reasons it will be omitted from response if "page" request parameter is not provided.
message (optional)	string Additional information about response.
message_type (optional)	string Type of the message. One of the values: "accepted"
redirects_to_domain	string nullable Domain that this company domain redirects to.
parent_company_domain	string nullable Parent company domain.

Example of NewsEventsDataset object

{
  "data": [
    {
      "id": "f096ea55-f81b-4f6a-83bd-d5c1cdc10143",
      "type": "news_event",
      "attributes": {
        "summary": "Klasha Inc. expands offices in Ghana.",
        "category": "expands_offices_in",
        "found_at": "2021-10-19T22:00:00Z",
        "confidence": 0.6872,
        "article_sentence": "San Francisco and Lagos-based cross-border technology company Klasha is further expanding its presence in Africa with the launch of its services in Ghana.",
        "human_approved": false,
        "planning": false,
        "amount": null,
        "amount_normalized": null,
        "assets": null,
        "assets_tags": [

],
        "award": null,
        "contact": null,
        "conference": null,
        "division": null,
        "effective_date": null,
        "financing_type": null,
        "financing_type_normalized": null,
        "financing_type_tags": [

],
        "headcount": null,
        "job_title": null,
        "job_title_tags": [

],
        "location": "Ghana",
        "location_data": [
          {
            "city": null,
            "state": null,
            "zip_code": null,
            "country": "Ghana",
            "region": null,
            "continent": null,
            "fuzzy_match": false
          }
        ],
        "product": null,
        "product_data": {
          "full_text": null,
          "name": null,
          "release_type": null,
          "release_version": null,
          "fuzzy_match": null
        },
        "product_tags": [

],
        "recognition": null,
        "vulnerability": null
      },
      "relationships": {
        "company1": {
          "data": {
            "id": "0db9ccc3-0c5f-5481-89da-b08e2a4195b8",
            "type": "company"
          }
        },
        "most_relevant_source": {
          "data": {
            "id": "797aa0af-c7f3-4b77-8a04-3f8bb39b315a",
            "type": "news_article"
          }
        }
      }
    }
  ],
  "included": [
    {
      "id": "0db9ccc3-0c5f-5481-89da-b08e2a4195b8",
      "type": "company",
      "attributes": {
        "domain": "klasha.com",
        "company_name": "Klasha Inc.",
        "ticker": null
      }
    },
    {
      "id": "797aa0af-c7f3-4b77-8a04-3f8bb39b315a",
      "type": "news_article",
      "attributes": {
        "author": null,
        "body": "Payfare, which offers banking solutions for gig workforce, has announced that it is integrating with Plaid, a data transfer network that powers fintech and digital finance products. The integration will… Technology company TraQiQ has introduced its online payment solution 'TraQPayments' in Latin America. TraQPayments is… American Express has partnered with Goldman Sachs Transaction Banking (TxB) to offer an integrated payments solution to large corporate clients. Barclaycard Payments and SAP is expanding their cooperation in a bid to boost payment acceptance capabilities and enhance procure-to-pay processes. UK-based fintech firm Previse has tapped Mastercard to provide instant payments to suppliers in more than 100 markets globally. Latin America-based payment processor PayRetailers has entered into an alliance with European payment giant Worldline. Buy now pay later (BNPL) firm Klarna has introduced ‘pay now’ option in the UK. Western Union International Bank (WUIB) has expanded its real-time payment capabilities in Europe by joining the Single Euro Payments Area (SEPA) Instant Credit Transfer scheme (SCT Inst). San Francisco and Lagos-based cross-border technology company Klasha is further expanding its presence in Africa with the launch of its services in Ghana.",
        "image_url": null,
        "url": "https://www.electronicpaymentsinternational.com/news/company-news/",
        "published_at": "2021-10-19T04:00:00Z",
        "title": "Company news Archives"
      }
    }
  ]
}

{
 "title": "NewsEventsDataset",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "description": "News Events are categorized events found in News Articles from various sources such as press releases, industry news, blogs and PR sites.\n \nAll events are provided in a structured format for all categories and always include company domain name to which the News Event belongs.\n \nBecause of the structured nature the News Events are better deduplicated and there is high signal to noise ratio.\n \nNews Event signals also include News Article title, body, author and more.\n",
 "type": "object",
 "required": [
 "data",
 "included"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "title": "NewsEvent",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes",
 "relationships"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the NewsEvent object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the NewsEvent object.",
 "const": "news_event"
 },
 "attributes": {
 "type": "object",
 "required": [
 "summary",
 "category",
 "found_at",
 "confidence",
 "article_sentence",
 "human_approved",
 "planning",
 "amount",
 "amount_normalized",
 "assets",
 "assets_tags",
 "award",
 "contact",
 "conference",
 "effective_date",
 "division",
 "financing_type",
 "financing_type_normalized",
 "financing_type_tags",
 "headcount",
 "job_title",
 "job_title_tags",
 "location",
 "location_data",
 "product",
 "product_data",
 "product_tags",
 "recognition",
 "vulnerability"
 ],
 "properties": {
 "summary": {
 "description": "A short, human readable excerpt of this event data.",
 "type": "string"
 },
 "category": {
 "description": "Name of the category this event represents.",
 "examples": [
 "invests_into"
 ],
 "type": "string",
 "enum": [
 "hires",
 "retires_from",
 "leaves",
 "promotes",
 "receives_financing",
 "goes_public",
 "invests_into",
 "acquires",
 "opens_new_location",
 "recognized_as",
 "receives_award",
 "partners_with",
 "merges_with",
 "launches",
 "is_developing",
 "integrates_with",
 "expands_offices_to",
 "expands_offices_in",
 "signs_new_client",
 "sells_assets_to",
 "invests_into_assets",
 "expands_facilities",
 "increases_headcount_by",
 "decreases_headcount_by",
 "closes_offices_in",
 "files_suit_against",
 "has_issues_with",
 "identified_as_competitor_of"
 ]
 },
 "found_at": {
 "description": "ISO 8601 date when the event was discovered and created.",
 "type": "string"
 },
 "confidence": {
 "description": "Floating point number of confidence between 0 and 1. Currently PredictLeads takes care of limiting the News Events so as to have a good balance between match rate and accuracy. Thus we do not recommend filtering News Events further based on this confidence.",
 "type": [
 "number"
 ]
 },
 "article_sentence": {
 "description": "Sentence in the News Article where event information was found.",
 "type": "string"
 },
 "human_approved": {
 "description": "If true, this event has been manually verified by a human.",
 "type": "boolean"
 },
 "planning": {
 "description": "When true, the event is planned to happen. E.g. the company might plan to launch a product in December.",
 "type": [
 "boolean"
 ]
 },
 "amount": {
 "description": "This is a string of a value as extracted from text.",
 "examples": [
 "$1.8 million"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "amount_normalized": {
 "description": "Integer value of \"amount\" property.",
 "examples": [
 18000000
 ],
 "type": [
 "integer",
 "null"
 ]
 },
 "assets": {
 "description": "Assets found in the News Event.",
 "examples": [
 "hydrogen production facility"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "assets_tags": {
 "description": "An array of assets tags extracted from \"assets\" property.",
 "examples": [
 [
 "production"
 ]
 ],
 "type": "array",
 "items": {
 "type": "string"
 }
 },
 "award": {
 "description": "Award type this event is announcing.",
 "examples": [
 "Pierre Potier Award"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "contact": {
 "description": "Person name that the News Event mentions.",
 "examples": [
 "Elon Musk"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "conference": {
 "description": "Conference name where the News Event was mentioned at.",
 "examples": [
 "World Business Outlook Awards"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "effective_date": {
 "description": "ISO 8601 date (without time) the News Event is mentioning, in string format.",
 "examples": [
 "2024-04-05"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "division": {
 "description": "Whether event concerns a specific division.",
 "type": [
 "string",
 "null"
 ]
 },
 "financing_type": {
 "description": "A financing type this event is mentioning.",
 "examples": [
 "Series B funding"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "financing_type_normalized": {
 "description": "A normalized value of \"financing_type\" property, where possible.",
 "enum": [
 "pre_angel",
 "angel",
 "angel1",
 "angel2",
 "angel3",
 "pre_seed",
 "seed",
 "seed1",
 "seed2",
 "seed3",
 "pre_series_a",
 "series_a",
 "series_a1",
 "series_a2",
 "series_a3",
 "pre_series_b",
 "series_b",
 "series_b1",
 "series_b2",
 "series_b3",
 "pre_series_c",
 "series_c",
 "series_c1",
 "series_c2",
 "series_c3",
 "pre_series_d",
 "series_d",
 "series_d1",
 "series_d2",
 "series_d3",
 "pre_series_e",
 "series_e",
 "series_e1",
 "series_e2",
 "series_e3",
 "pre_series_f",
 "series_f",
 "series_f1",
 "series_f2",
 "series_f3",
 "pre_series_g",
 "series_g",
 "series_g1",
 "series_g2",
 "series_g3",
 "pre_series_h",
 "series_h",
 "series_h1",
 "series_h2",
 "series_h3",
 "pre_series_i",
 "series_i",
 "series_i1",
 "series_i2",
 "series_i3",
 "pre_series_j",
 "series_j",
 "series_j1",
 "series_j2",
 "series_j3",
 null
 ],
 "examples": [
 "series_b"
 ]
 },
 "financing_type_tags": {
 "description": "An array of financing type categories.",
 "examples": [
 [
 "equity"
 ]
 ],
 "type": "array",
 "items": {
 "type": "string"
 }
 },
 "headcount": {
 "description": "A number of people mentioned organization is involved with.",
 "examples": [
 200
 ],
 "type": [
 "integer",
 "null"
 ]
 },
 "job_title": {
 "description": "Job title this event is mentioning.",
 "examples": [
 "Director of Media and Analytics"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "job_title_tags": {
 "description": "An array of job title tags.",
 "examples": [
 [
 "marketing",
 "directors"
 ]
 ],
 "type": "array",
 "items": {
 "type": "string"
 }
 },
 "location": {
 "description": "Location of where the event happened or where the organization has expanded/relocated.",
 "examples": [
 "Menlo Park, California, United States"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "location_data": {
 "type": "array",
 "items": {
 "title": "LocationData",
 "type": "object",
 "required": [
 "city",
 "state",
 "zip_code",
 "country",
 "region",
 "continent",
 "fuzzy_match"
 ],
 "properties": {
 "city": {
 "type": [
 "string",
 "null"
 ]
 },
 "state": {
 "type": [
 "string",
 "null"
 ]
 },
 "zip_code": {
 "type": [
 "string",
 "null"
 ]
 },
 "country": {
 "type": [
 "string",
 "null"
 ]
 },
 "region": {
 "type": [
 "string",
 "null"
 ]
 },
 "continent": {
 "type": [
 "string",
 "null"
 ]
 },
 "fuzzy_match": {
 "type": [
 "boolean",
 "null"
 ]
 }
 }
 }
 },
 "product": {
 "description": "Name of the product as mentioned by this event.",
 "examples": [
 "Credit Pass"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "product_data": {
 "type": "object",
 "required": [
 "full_text",
 "name",
 "release_type",
 "release_version",
 "fuzzy_match"
 ],
 "properties": {
 "full_text": {
 "description": "Full product name as recognized by Named Entity Recognition, without further cleaning of the product name.",
 "examples": [
 "Plant Control System",
 "3D printing service for spare parts"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "name": {
 "description": "Cleaned name of the product referenced in the News Event.",
 "examples": [
 "VideoPoet"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "release_type": {
 "description": "Product release type.",
 "examples": [
 "major"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "release_version": {
 "description": "Product release version.",
 "examples": [
 "v3.0"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "fuzzy_match": {
 "description": "When true we might not have extracted the product name cleanly. When false exact product name should be extracted well.",
 "type": [
 "boolean",
 "null"
 ]
 }
 }
 },
 "product_tags": {
 "description": "List of tags regarding the product.",
 "examples": [
 [
 "mobile",
 "online_technology"
 ]
 ],
 "type": "array",
 "items": {
 "type": "string"
 }
 },
 "recognition": {
 "description": "Name of the recognition the company received.",
 "examples": [
 "50 Smartest Companies"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "vulnerability": {
 "description": "Company issue.",
 "examples": [
 "account suspension errors"
 ],
 "type": [
 "string",
 "null"
 ]
 }
 }
 },
 "relationships": {
 "description": "Contains references to related objects listed in property \"included\".",
 "type": "object",
 "required": [
 "most_relevant_source"
 ],
 "properties": {
 "company1": {
 "title": "RelationshipCompanyLite",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included CompanyLite object.",
 "const": "company"
 }
 }
 }
 }
 },
 "company2": {
 "title": "RelationshipCompanyLite",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included CompanyLite object.",
 "const": "company"
 }
 }
 }
 }
 },
 "most_relevant_source": {
 "title": "RelationshipNewsArticleLite",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included NewsArticleLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included NewsArticleLite object.",
 "const": "news_article"
 }
 }
 }
 }
 }
 }
 }
 }
 }
 },
 "included": {
 "description": "Contains objects referenced in property \"relationships\".",
 "type": "array",
 "items": {
 "oneOf": [
 {
 "title": "CompanyLite",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the CompanyLite object.",
 "const": "company"
 },
 "attributes": {
 "type": "object",
 "required": [
 "domain",
 "ticker",
 "company_name"
 ],
 "properties": {
 "domain": {
 "description": "Company domain name.",
 "type": "string"
 },
 "ticker": {
 "description": "Company stock symbol (ticker) if available.",
 "type": [
 "string",
 "null"
 ]
 },
 "company_name": {
 "description": "Company name if available.",
 "type": [
 "string",
 "null"
 ]
 }
 }
 }
 }
 },
 {
 "title": "NewsArticleLite",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the NewsArticleLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the NewsArticleLite object.",
 "const": "news_article"
 },
 "attributes": {
 "type": "object",
 "required": [
 "url",
 "title",
 "author",
 "image_url",
 "published_at",
 "body"
 ],
 "properties": {
 "url": {
 "description": "News Article source URL.",
 "type": "string",
 "format": "uri"
 },
 "title": {
 "description": "Prettified News Article title or heading.",
 "type": "string"
 },
 "author": {
 "description": "News Article author name.",
 "type": [
 "string",
 "null"
 ]
 },
 "image_url": {
 "description": "News Article most relevant image URL.",
 "type": [
 "string",
 "null"
 ]
 },
 "published_at": {
 "description": "ISO 8601 date when News Article was published.",
 "type": "string",
 "format": "date-time"
 },
 "body": {
 "description": "News Article text (article content).",
 "type": "string"
 }
 }
 }
 }
 }
 ],
 "discriminator": {
 "propertyName": "type"
 }
 }
 },
 "meta": {
 "description": "Meta information about response. Only shown in API responses.\n",
 "type": "object",
 "properties": {
 "count": {
 "description": "Total number of results.\n \n**NOTE:** For performance reasons it will be omitted from response if \"page\" request parameter is not\nprovided.\n",
 "type": "integer",
 "example": 344
 },
 "message": {
 "description": "Additional information about response.\n",
 "type": "string"
 },
 "message_type": {
 "description": "Type of the message.\n",
 "enum": [
 "accepted"
 ]
 },
 "redirects_to_domain": {
 "description": "Domain that this company domain redirects to.\n",
 "type": [
 "string",
 "null"
 ],
 "example": "ibm.com"
 },
 "parent_company_domain": {
 "description": "Parent company domain.\n",
 "type": [
 "string",
 "null"
 ],
 "example": "ibm.com"
 }
 },
 "example": {
 "count": 344,
 "message": "...",
 "message_type": "...",
 "redirects_to_domain": "google.com",
 "parent_company_domain": "google.com"
 },
 "required": [
 "redirects_to_domain",
 "parent_company_domain"
 ]
 }
 },
 "example": {
 "data": [
 {
 "id": "f096ea55-f81b-4f6a-83bd-d5c1cdc10143",
 "type": "news_event",
 "attributes": {
 "summary": "Klasha Inc. expands offices in Ghana.",
 "category": "expands_offices_in",
 "found_at": "2021-10-19T22:00:00Z",
 "confidence": 0.6872,
 "article_sentence": "San Francisco and Lagos-based cross-border technology company Klasha is further expanding its presence in Africa with the launch of its services in Ghana.",
 "human_approved": false,
 "planning": false,
 "amount": null,
 "amount_normalized": null,
 "assets": null,
 "assets_tags": [

],
          "award": null,
          "contact": null,
          "conference": null,
          "division": null,
          "effective_date": null,
          "financing_type": null,
          "financing_type_normalized": null,
          "financing_type_tags": [

],
          "headcount": null,
          "job_title": null,
          "job_title_tags": [

],
          "location": "Ghana",
          "location_data": [
            {
              "city": null,
              "state": null,
              "zip_code": null,
              "country": "Ghana",
              "region": null,
              "continent": null,
              "fuzzy_match": false
            }
          ],
          "product": null,
          "product_data": {
            "full_text": null,
            "name": null,
            "release_type": null,
            "release_version": null,
            "fuzzy_match": null
          },
          "product_tags": [

],
          "recognition": null,
          "vulnerability": null
        },
        "relationships": {
          "company1": {
            "data": {
              "id": "0db9ccc3-0c5f-5481-89da-b08e2a4195b8",
              "type": "company"
            }
          },
          "most_relevant_source": {
            "data": {
              "id": "797aa0af-c7f3-4b77-8a04-3f8bb39b315a",
              "type": "news_article"
            }
          }
        }
      }
    ],
    "included": [
      {
        "id": "0db9ccc3-0c5f-5481-89da-b08e2a4195b8",
        "type": "company",
        "attributes": {
          "domain": "klasha.com",
          "company_name": "Klasha Inc.",
          "ticker": null
        }
      },
      {
        "id": "797aa0af-c7f3-4b77-8a04-3f8bb39b315a",
        "type": "news_article",
        "attributes": {
          "author": null,
          "body": "Payfare, which offers banking solutions for gig workforce, has announced that it is integrating with Plaid, a data transfer network that powers fintech and digital finance products. The integration will… Technology company TraQiQ has introduced its online payment solution 'TraQPayments' in Latin America. TraQPayments is… American Express has partnered with Goldman Sachs Transaction Banking (TxB) to offer an integrated payments solution to large corporate clients. Barclaycard Payments and SAP is expanding their cooperation in a bid to boost payment acceptance capabilities and enhance procure-to-pay processes. UK-based fintech firm Previse has tapped Mastercard to provide instant payments to suppliers in more than 100 markets globally. Latin America-based payment processor PayRetailers has entered into an alliance with European payment giant Worldline. Buy now pay later (BNPL) firm Klarna has introduced ‘pay now’ option in the UK. Western Union International Bank (WUIB) has expanded its real-time payment capabilities in Europe by joining the Single Euro Payments Area (SEPA) Instant Credit Transfer scheme (SCT Inst). San Francisco and Lagos-based cross-border technology company Klasha is further expanding its presence in Africa with the launch of its services in Ghana.",
          "image_url": null,
          "url": "https://www.electronicpaymentsinternational.com/news/company-news/",
          "published_at": "2021-10-19T04:00:00Z",
          "title": "Company news Archives"
        }
      }
    ]
  }
}

1.6.
TechnologiesDataset

Technologies are detected on websites and jobs related posts.

TechnologiesDataset	object
data	array
Technology	object
id	string PredictLeads ID of the Technology object. Format: uuid
type	string Type of the Technology object. Constant: "technology"
attributes	object
name	string
description	string nullable
categories	array
Items	string
parent_categories	array
Items	string
domain	string nullable
url	string nullable Format: uri
pricing_data	object
min_usd	number nullable
max_usd	number nullable
interval	string nullable
tags	array
Items	string
created_at	string Format: date-time
relationships	object Contains references to related objects listed in property "included".
company	object
data	object
id	string PredictLeads ID of the included CompanyLite object. Format: uuid
type	string Type of the included CompanyLite object. Constant: "company"
included	array Contains objects referenced in property "relationships".
CompanyLite	object
id	string PredictLeads ID of the CompanyLite object. Format: uuid
type	string Type of the CompanyLite object. Constant: "company"
attributes	object
domain	string Company domain name.
ticker	string nullable Company stock symbol (ticker) if available.
company_name	string nullable Company name if available.
meta (optional)	object Meta information about response. Only shown in API responses.
count (optional)	integer Total number of results. NOTE: For performance reasons it will be omitted from response if "page" request parameter is not provided.
message (optional)	string Additional information about response.
message_type (optional)	string Type of the message. One of the values: "accepted"

Example of TechnologiesDataset object

{
 "title": "TechnologiesDataset",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "description": "Technologies are detected on websites and jobs related posts.\n",
 "type": "object",
 "required": [
 "data",
 "included"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "title": "Technology",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes",
 "relationships"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the Technology object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the Technology object.",
 "const": "technology"
 },
 "attributes": {
 "type": "object",
 "required": [
 "name",
 "description",
 "categories",
 "parent_categories",
 "domain",
 "url",
 "pricing_data",
 "created_at"
 ],
 "properties": {
 "name": {
 "type": "string"
 },
 "description": {
 "type": [
 "null",
 "string"
 ]
 },
 "categories": {
 "type": "array",
 "items": {
 "type": "string"
 }
 },
 "parent_categories": {
 "type": "array",
 "items": {
 "type": "string"
 }
 },
 "domain": {
 "type": [
 "null",
 "string"
 ]
 },
 "url": {
 "type": [
 "null",
 "string"
 ],
 "format": "uri"
 },
 "pricing_data": {
 "type": "object",
 "required": [
 "interval",
 "max_usd",
 "min_usd",
 "tags"
 ],
 "properties": {
 "min_usd": {
 "type": [
 "null",
 "number"
 ]
 },
 "max_usd": {
 "type": [
 "null",
 "number"
 ]
 },
 "interval": {
 "type": [
 "null",
 "string"
 ]
 },
 "tags": {
 "type": "array",
 "items": {
 "type": "string"
 }
 }
 }
 },
 "created_at": {
 "type": "string",
 "format": "date-time"
 }
 }
 },
 "relationships": {
 "description": "Contains references to related objects listed in property \"included\".",
 "type": "object",
 "required": [
 "company"
 ],
 "properties": {
 "company": {
 "title": "RelationshipCompanyLite",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included CompanyLite object.",
 "const": "company"
 }
 }
 }
 }
 }
 }
 }
 }
 }
 },
 "included": {
 "description": "Contains objects referenced in property \"relationships\".",
 "type": "array",
 "items": {
 "title": "CompanyLite",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the CompanyLite object.",
 "const": "company"
 },
 "attributes": {
 "type": "object",
 "required": [
 "domain",
 "ticker",
 "company_name"
 ],
 "properties": {
 "domain": {
 "description": "Company domain name.",
 "type": "string"
 },
 "ticker": {
 "description": "Company stock symbol (ticker) if available.",
 "type": [
 "string",
 "null"
 ]
 },
 "company_name": {
 "description": "Company name if available.",
 "type": [
 "string",
 "null"
 ]
 }
 }
 }
 }
 }
 },
 "meta": {
 "description": "Meta information about response. Only shown in API responses.\n",
 "type": "object",
 "properties": {
 "count": {
 "description": "Total number of results.\n \n**NOTE:** For performance reasons it will be omitted from response if \"page\" request parameter is not\nprovided.\n",
 "type": "integer",
 "example": 344
 },
 "message": {
 "description": "Additional information about response.\n",
 "type": "string"
 },
 "message_type": {
 "description": "Type of the message.\n",
 "enum": [
 "accepted"
 ]
 }
 },
 "example": {
 "count": 344,
 "message": "...",
 "message_type": "..."
 }
 }
 },
 "example": {
 "data": [
 {
 "id": "70392f20-b531-53cc-afa6-26416c0d582d",
 "type": "technology",
 "attributes": {
 "name": "Salesforce",
 "description": "Salesforce is a cloud computing service software (SaaS) that specializes in customer relationship management (CRM).",
 "categories": [
 "customer_relationship_management"
 ],
 "domain": "salesforce.com",
 "url": "https://www.salesforce.com",
 "pricing_data": {
 "min_usd": 300.0,
 "max_usd": 1000000.0,
 "interval": "yearly",
 "tags": [
 "low",
 "mid",
 "high",
 "enterprise",
 "saas"
 ]
 },
 "created_at": "2017-08-24T22:36:09Z"
 },
 "relationships": {
 "company": {
 "data": {
 "id": "9b638313-3fe3-5f5e-a19d-42e122cf06f9",
 "type": "company"
 }
 }
 }
 }
 ],
 "included": [
 {
 "id": "9b638313-3fe3-5f5e-a19d-42e122cf06f9",
 "type": "company",
 "attributes": {
 "domain": "salesforce.com",
 "company_name": "Salesforce, Inc.",
 "ticker": "NYSE:CRM"
 }
 }
 ]
 }
}

1.7.
TechnologyDetectionsDataset

Technology detections provide insight into tech stacks of companies.

Detections are sourced from company websites and from job opening descriptions.

Each detection is also enriched with company and technology information which includes technology description, technology category, pricing and more.

TechnologyDetectionsDataset	object
data	array
TechnologyDetection	object
id	string PredictLeads ID of the TechnologyDetection object. Format: uuid
type	string Type of the TechnologyDetection object. Constant: "technology_detection"
attributes	object
first_seen_at	string Format: date-time
last_seen_at	string Format: date-time
behind_firewall	boolean
score	number
relationships	object Contains references to related objects listed in property "included".
company	object
data	object
id	string PredictLeads ID of the included CompanyLite object. Format: uuid
type	string Type of the included CompanyLite object. Constant: "company"
seen_on_job_openings	object
data	array
Items	object
id	string PredictLeads ID of the included JobOpeningLite object. Format: uuid
type	string Type of the included JobOpeningLite object. Constant: "job_opening"
seen_on_subpages	object
data	array
Items	object
id	string PredictLeads ID of the included DetectionOnSubpage object. Format: uuid
type	string Type of the included DetectionOnSubpage object. Constant: "detection_on_subpage"
technology	object
data	object
id	string PredictLeads ID of the included TechnologyLite object. Format: uuid
type	string Type of the included TechnologyLite object. Constant: "technology"
included	array Contains objects referenced in property "relationships".
Items
One of	Discriminator property name: "type"
CompanyLite	object
id	string PredictLeads ID of the CompanyLite object. Format: uuid
type	string Type of the CompanyLite object. Constant: "company"
attributes	object
domain	string Company domain name.
ticker	string nullable Company stock symbol (ticker) if available.
company_name	string nullable Company name if available.
or
TechnologyLite	object
id	string PredictLeads ID of the TechnologyLite object. Format: uuid
type	string Type of the TechnologyLite object. Constant: "technology"
attributes	object
name	string
or
DetectionOnSubpage	object
id	string PredictLeads ID of the DetectionOnSubpage object. Format: uuid
type	string Type of the DetectionOnSubpage object. Constant: "detection_on_subpage"
attributes	object
first_seen_at	string Format: date-time
last_seen_at	string Format: date-time
subpage_url	string Format: uri
or
JobOpeningLite	object
id	string PredictLeads ID of the JobOpeningLite object. Format: uuid
type	string Type of the JobOpeningLite object. Constant: "job_opening"
attributes	object
first_seen_at	string Time & date (ISO 8601) of when the job opening was first seen. Format: date-time
last_seen_at	string Time & date (ISO 8601) of when the job opening was last seen. Format: date-time
url	string URL of the web page where the data was found. Format: uri
meta	object Meta information about response. Only shown in API responses.
count (optional)	integer Total number of results. NOTE: For performance reasons it will be omitted from response if "page" request parameter is not provided.
message (optional)	string Additional information about response.
message_type (optional)	string Type of the message. One of the values: "accepted"
redirects_to_domain	string nullable Domain that this company domain redirects to.
parent_company_domain	string nullable Parent company domain.

Example of TechnologyDetectionsDataset object

{
  "data": [
    {
      "id": "344f5e7c-4dae-4814-8f45-ded70b2baa6a",
      "type": "technology_detection",
      "attributes": {
        "first_seen_at": "2022-12-08T10:40:41Z",
        "last_seen_at": "2024-09-11T14:23:41Z",
        "behind_firewall": false,
        "score": 0.75
      },
      "relationships": {
        "company": {
          "data": {
            "id": "9e0bf4ad-0792-58ba-94a0-cb12a5a853a2",
            "type": "company"
          }
        },
        "seen_on_job_openings": {
          "data": [

]
        },
        "seen_on_subpages": {
          "data": [
            {
              "id": "0635626f-de95-4c06-ae84-b96123929607",
              "type": "detection_on_subpage"
            },
            {
              "id": "fc8ca4d4-2a57-4588-9dcd-84fe46ae2578",
              "type": "detection_on_subpage"
            }
          ]
        },
        "technology": {
          "data": {
            "id": "aa139d7c-b2fd-5b76-8dac-97c9ba844c73",
            "type": "technology"
          }
        }
      }
    }
  ],
  "included": [
    {
      "id": "0635626f-de95-4c06-ae84-b96123929607",
      "type": "detection_on_subpage",
      "attributes": {
        "first_seen_at": "2024-06-03T01:57:52Z",
        "last_seen_at": "2024-08-03T07:55:37Z",
        "subpage_url": "https://support.madkudu.com/hc/en-us"
      }
    },
    {
      "id": "fc8ca4d4-2a57-4588-9dcd-84fe46ae2578",
      "type": "detection_on_subpage",
      "attributes": {
        "first_seen_at": "2022-12-08T10:40:47Z",
        "last_seen_at": "2024-09-11T14:23:41Z",
        "subpage_url": "https://www.madkudu.com"
      }
    },
    {
      "id": "aa139d7c-b2fd-5b76-8dac-97c9ba844c73",
      "type": "technology",
      "attributes": {
        "name": "Google Hosted Libraries"
      }
    },
    {
      "id": "9e0bf4ad-0792-58ba-94a0-cb12a5a853a2",
      "type": "company",
      "attributes": {
        "domain": "madkudu.com",
        "company_name": "MadKudu Inc.",
        "ticker": null
      }
    }
  ]
}

{
 "title": "TechnologyDetectionsDataset",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "description": "Technology detections provide insight into tech stacks of companies.\n \nDetections are sourced from company websites and from job opening descriptions.\n \nEach detection is also enriched with company and technology information which includes technology\ndescription, technology category, pricing and more.\n",
 "type": "object",
 "required": [
 "data",
 "included"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "title": "TechnologyDetection",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes",
 "relationships"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the TechnologyDetection object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the TechnologyDetection object.",
 "const": "technology_detection"
 },
 "attributes": {
 "type": "object",
 "required": [
 "first_seen_at",
 "last_seen_at",
 "behind_firewall",
 "score"
 ],
 "properties": {
 "first_seen_at": {
 "type": "string",
 "format": "date-time"
 },
 "last_seen_at": {
 "type": "string",
 "format": "date-time"
 },
 "behind_firewall": {
 "type": "boolean"
 },
 "score": {
 "type": "number"
 }
 }
 },
 "relationships": {
 "description": "Contains references to related objects listed in property \"included\".",
 "type": "object",
 "required": [
 "company",
 "seen_on_job_openings",
 "seen_on_subpages",
 "technology"
 ],
 "properties": {
 "company": {
 "title": "RelationshipCompanyLite",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included CompanyLite object.",
 "const": "company"
 }
 }
 }
 }
 },
 "seen_on_job_openings": {
 "title": "RelationshipJobOpeningLite",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included JobOpeningLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included JobOpeningLite object.",
 "const": "job_opening"
 }
 }
 }
 }
 }
 },
 "seen_on_subpages": {
 "title": "RelationshipDetectionOnSubpage",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included DetectionOnSubpage object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included DetectionOnSubpage object.",
 "const": "detection_on_subpage"
 }
 }
 }
 }
 }
 },
 "technology": {
 "title": "RelationshipTechnologyLite",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included TechnologyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included TechnologyLite object.",
 "const": "technology"
 }
 }
 }
 }
 }
 }
 }
 }
 }
 },
 "included": {
 "description": "Contains objects referenced in property \"relationships\".",
 "type": "array",
 "items": {
 "oneOf": [
 {
 "title": "CompanyLite",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the CompanyLite object.",
 "const": "company"
 },
 "attributes": {
 "type": "object",
 "required": [
 "domain",
 "ticker",
 "company_name"
 ],
 "properties": {
 "domain": {
 "description": "Company domain name.",
 "type": "string"
 },
 "ticker": {
 "description": "Company stock symbol (ticker) if available.",
 "type": [
 "string",
 "null"
 ]
 },
 "company_name": {
 "description": "Company name if available.",
 "type": [
 "string",
 "null"
 ]
 }
 }
 }
 }
 },
 {
 "title": "TechnologyLite",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the TechnologyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the TechnologyLite object.",
 "const": "technology"
 },
 "attributes": {
 "type": "object",
 "required": [
 "name"
 ],
 "properties": {
 "name": {
 "type": "string"
 }
 }
 }
 }
 },
 {
 "title": "DetectionOnSubpage",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the DetectionOnSubpage object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the DetectionOnSubpage object.",
 "const": "detection_on_subpage"
 },
 "attributes": {
 "type": "object",
 "required": [
 "first_seen_at",
 "last_seen_at",
 "subpage_url"
 ],
 "properties": {
 "first_seen_at": {
 "type": "string",
 "format": "date-time"
 },
 "last_seen_at": {
 "type": "string",
 "format": "date-time"
 },
 "subpage_url": {
 "type": "string",
 "format": "uri"
 }
 }
 }
 }
 },
 {
 "title": "JobOpeningLite",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the JobOpeningLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the JobOpeningLite object.",
 "const": "job_opening"
 },
 "attributes": {
 "type": "object",
 "required": [
 "first_seen_at",
 "last_seen_at",
 "url"
 ],
 "properties": {
 "first_seen_at": {
 "description": "Time & date (ISO 8601) of when the job opening was first seen.",
 "type": "string",
 "format": "date-time"
 },
 "last_seen_at": {
 "description": "Time & date (ISO 8601) of when the job opening was last seen.",
 "type": "string",
 "format": "date-time"
 },
 "url": {
 "description": "URL of the web page where the data was found.",
 "type": "string",
 "format": "uri"
 }
 }
 }
 }
 }
 ],
 "discriminator": {
 "propertyName": "type"
 }
 }
 },
 "meta": {
 "description": "Meta information about response. Only shown in API responses.\n",
 "type": "object",
 "properties": {
 "count": {
 "description": "Total number of results.\n \n**NOTE:** For performance reasons it will be omitted from response if \"page\" request parameter is not\nprovided.\n",
 "type": "integer",
 "example": 344
 },
 "message": {
 "description": "Additional information about response.\n",
 "type": "string"
 },
 "message_type": {
 "description": "Type of the message.\n",
 "enum": [
 "accepted"
 ]
 },
 "redirects_to_domain": {
 "description": "Domain that this company domain redirects to.\n",
 "type": [
 "string",
 "null"
 ],
 "example": "ibm.com"
 },
 "parent_company_domain": {
 "description": "Parent company domain.\n",
 "type": [
 "string",
 "null"
 ],
 "example": "ibm.com"
 }
 },
 "example": {
 "count": 344,
 "message": "...",
 "message_type": "...",
 "redirects_to_domain": "google.com",
 "parent_company_domain": "google.com"
 },
 "required": [
 "redirects_to_domain",
 "parent_company_domain"
 ]
 }
 },
 "example": {
 "data": [
 {
 "id": "344f5e7c-4dae-4814-8f45-ded70b2baa6a",
 "type": "technology_detection",
 "attributes": {
 "first_seen_at": "2022-12-08T10:40:41Z",
 "last_seen_at": "2024-09-11T14:23:41Z",
 "behind_firewall": false,
 "score": 0.75
 },
 "relationships": {
 "company": {
 "data": {
 "id": "9e0bf4ad-0792-58ba-94a0-cb12a5a853a2",
 "type": "company"
 }
 },
 "seen_on_job_openings": {
 "data": [

]
          },
          "seen_on_subpages": {
            "data": [
              {
                "id": "0635626f-de95-4c06-ae84-b96123929607",
                "type": "detection_on_subpage"
              },
              {
                "id": "fc8ca4d4-2a57-4588-9dcd-84fe46ae2578",
                "type": "detection_on_subpage"
              }
            ]
          },
          "technology": {
            "data": {
              "id": "aa139d7c-b2fd-5b76-8dac-97c9ba844c73",
              "type": "technology"
            }
          }
        }
      }
    ],
    "included": [
      {
        "id": "0635626f-de95-4c06-ae84-b96123929607",
        "type": "detection_on_subpage",
        "attributes": {
          "first_seen_at": "2024-06-03T01:57:52Z",
          "last_seen_at": "2024-08-03T07:55:37Z",
          "subpage_url": "https://support.madkudu.com/hc/en-us"
        }
      },
      {
        "id": "fc8ca4d4-2a57-4588-9dcd-84fe46ae2578",
        "type": "detection_on_subpage",
        "attributes": {
          "first_seen_at": "2022-12-08T10:40:47Z",
          "last_seen_at": "2024-09-11T14:23:41Z",
          "subpage_url": "https://www.madkudu.com"
        }
      },
      {
        "id": "aa139d7c-b2fd-5b76-8dac-97c9ba844c73",
        "type": "technology",
        "attributes": {
          "name": "Google Hosted Libraries"
        }
      },
      {
        "id": "9e0bf4ad-0792-58ba-94a0-cb12a5a853a2",
        "type": "company",
        "attributes": {
          "domain": "madkudu.com",
          "company_name": "MadKudu Inc.",
          "ticker": null
        }
      }
    ]
  }
}

1.8.
WebsiteEvolutionDataset

Provides information on how a given website has been evolving.

This dataset tracks when certain subpages like "about us", "blog", "careers", "api docs", "customer support"... pages have been added. The more subpages a given company adds in a shorter time frame, normally the faster its progress. One could also segment companies based on the categories of subpages they have.

WebsiteEvolutionDataset	object
data	array
Subpage	object
id	string PredictLeads ID of the Subpage object. Format: uuid
type	string Type of the Subpage object. Constant: "subpage"
attributes	object
url	string Subpage URL. Format: uri
first_seen_at	string Time & date (ISO 8601) of when the subpage was first seen. Format: date-time
last_seen_at	string Time & date (ISO 8601) of when the subpage was last seen. Format: date-time
category	string Subpage category.
text_content	string nullable Text content from the subpage. Sentences are contextually separated with new lines (\n).
relationships	object Contains references to related objects listed in property "included".
company	object
data	object
id	string PredictLeads ID of the included CompanyLite object. Format: uuid
type	string Type of the included CompanyLite object. Constant: "company"
included	array Contains objects referenced in property "relationships".
CompanyLite	object
id	string PredictLeads ID of the CompanyLite object. Format: uuid
type	string Type of the CompanyLite object. Constant: "company"
attributes	object
domain	string Company domain name.
ticker	string nullable Company stock symbol (ticker) if available.
company_name	string nullable Company name if available.
meta	object Meta information about response. Only shown in API responses.
count (optional)	integer Total number of results. NOTE: For performance reasons it will be omitted from response if "page" request parameter is not provided.
message (optional)	string Additional information about response.
message_type (optional)	string Type of the message. One of the values: "accepted"
redirects_to_domain	string nullable Domain that this company domain redirects to.
parent_company_domain	string nullable Parent company domain.

Example of WebsiteEvolutionDataset object

{
  "data": [
    {
      "id": "fc4f22ed-975e-4826-9e21-31ec98da19a3",
      "type": "subpage",
      "attributes": {
        "category": "solutions",
        "url": "https://nonamesecurity.com/platform/security-testing",
        "text_content": "Leave no API untested and Shift Left with API security testing. Add security into your CI/CD pipeline without sacrificing speed and ensure that APIs aren't implemented with security vulnerabilities.\nNoname Security Active Testing is a purpose-built API security testing solution that understands your unique business logic and provides comprehensive coverage of API-specific vulnerabilities. Active Testing helps you shift left and bake API security testing into every phase of development.\nReduce the risk of successful attacks without any changes to production infrastructure.\nAccelerate remediation and lower remediation costs by up to 100x by finding and fixing issues earlier.\nImprove compliance and avoid regulatory fines and reputational damage from incidents.\nImprove security without sacrificing velocity. Deliver secure code without having to become a security expert.\nReduce redundant pentesting and other third-party security testing costs with a proven solution.\nIncrease your organization's confidence in APIs with continuous testing throughout the SDLC.\nA holistic API Security strategy should account for both pre- and post-production. Testing during the API development process allows developers to catch vulnerabilities and flaws early.\nWow. We've worked with a lot of partners, but the speed Noname is moving at has been impressive. Active Testing is huge for any company that cares about their APIs. They continue to push the envelope and we're proud to partner with them.\nBy implementing Active Testing by Noname Security, we are able to provide developers with the tools they need to bridge the gap and secure APIs before production, and without having to become security experts.\nEmpower developers with best-in-class usability such as simple setup & automation, in-line test results, and contextual guidance for request failure mitigation.\nLeave no API untested with a unique ability to find and test every API based on an understanding of the application's business logic.\nActive Testing fully integrates with your existing continuous integration/continuous delivery (CI/CD) pipelines and tools, such as Jenkins and Postman, as well as all your ticketing and workflow tools such as ServiceNow, Slack, and Jira.\nTeams get dynamic API visibility across multiple states and environments throughout the CI/CD process. Import APIs from a wide range of sources with dynamic updates. Compare Swagger files to assess conformance, based on real implementation results.\nStreamline testing with role-based access controls so only the right teams can access APIs for testing.\nWhat is API security testing?\nAPI security testing is an important part of ensuring the safety and reliability of your web applications. It involves testing application programming interfaces (APIs) during development to identify vulnerabilities and potential threats. The goal is to ensure that APIs are protected from malicious attacks, data breaches, and other security incidents. Through API security testing, organizations can make sure that their APIs are secure and compliant with data privacy regulations.\nHow does API security testing protect me from attacks?\nAPI security testing can identify vulnerabilities in your APIs before they're exploited by attackers, helping you prevent data breaches. APIs are often used to access sensitive data, and by testing the APIs regularly, your organization can ensure that your environment is secure and protected from potential threats.\nWhat is Shift-Left security testing?\nShift Left is an approach of moving a variety of tasks earlier in the development process. This means that tasks that are traditionally done at a later stage of the operations should instead be performed at earlier stages-particularly those related to API security and software testing.\nShift-Left vs Shift-Right security testing?\nShift-left security approach moves testing to the left on the timeline, so the team performs tests earlier and more often in the life cycle. In contrast, a shift-right approach considers testing in production with real users to be more useful.\nShould we use Active Testing with other Noname products?\nYes, Active Testing should be used in parallel with the other modules from the Noname API Security platform. Active Testing is a tool to help uncover vulnerabilities with APIs pre-production, whereas our API Discovery, Posture Management, and Runtime Protection modules are for protecting APIs in production.\nIs Active Testing right for my needs?\nThe earlier you catch security vulnerabilities, the better. From both a cost perspective and remediation angle, it is much easier to correct issues during the development process of the API than after it has been released into production and is being actively used. Active Testing allows organizations to more confidently and efficiently deliver applications to the business and remain competitive securely.\nOrganizations now have an average of 15,000 APIs to secure, and this number is growing rapidly as thousands of new applications hit the market...\nAs the number and complexity of APIs continue to grow, companies face increasing challenges when securing their APIs.\nFocus on finding and remediating API security vulnerabilities during the development phase.\nExperience the speed, scale, and security that only Noname can provide. You'll never look at APIs the same way again.",
        "first_seen_at": "2023-08-20T01:06:12Z",
        "last_seen_at": "2024-09-08T08:08:45Z"
      },
      "relationships": {
        "company": {
          "data": {
            "id": "47d3f4f2-e119-50cb-93f8-49a5fac024e2",
            "type": "company"
          }
        }
      }
    }
  ],
  "included": [
    {
      "id": "47d3f4f2-e119-50cb-93f8-49a5fac024e2",
      "type": "company",
      "attributes": {
        "domain": "nonamesecurity.com",
        "company_name": "Noname Security Group",
        "ticker": null
      }
    }
  ]
}

{
 "title": "WebsiteEvolutionDataset",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "description": "Provides information on how a given website has been evolving.\n \nThis dataset tracks when certain subpages like \"about us\", \"blog\", \"careers\", \"api docs\",\n\"customer support\"... pages have been added. The more subpages a given company adds in a shorter time\nframe, normally the faster its progress. One could also segment companies based on the categories of\nsubpages they have.\n",
 "type": "object",
 "required": [
 "data",
 "included"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "title": "Subpage",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes",
 "relationships"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the Subpage object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the Subpage object.",
 "const": "subpage"
 },
 "attributes": {
 "type": "object",
 "required": [
 "url",
 "first_seen_at",
 "last_seen_at",
 "category",
 "text_content"
 ],
 "properties": {
 "url": {
 "description": "Subpage URL.",
 "type": "string",
 "format": "uri"
 },
 "first_seen_at": {
 "description": "Time & date (ISO 8601) of when the subpage was first seen.",
 "type": "string",
 "format": "date-time"
 },
 "last_seen_at": {
 "description": "Time & date (ISO 8601) of when the subpage was last seen.",
 "type": "string",
 "format": "date-time"
 },
 "category": {
 "description": "Subpage category.",
 "type": "string"
 },
 "text_content": {
 "description": "Text content from the subpage. Sentences are contextually separated with new lines (\\n).",
 "type": [
 "null",
 "string"
 ]
 }
 }
 },
 "relationships": {
 "description": "Contains references to related objects listed in property \"included\".",
 "type": "object",
 "required": [
 "company"
 ],
 "properties": {
 "company": {
 "title": "RelationshipCompanyLite",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included CompanyLite object.",
 "const": "company"
 }
 }
 }
 }
 }
 }
 }
 }
 }
 },
 "included": {
 "description": "Contains objects referenced in property \"relationships\".",
 "type": "array",
 "items": {
 "title": "CompanyLite",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the CompanyLite object.",
 "const": "company"
 },
 "attributes": {
 "type": "object",
 "required": [
 "domain",
 "ticker",
 "company_name"
 ],
 "properties": {
 "domain": {
 "description": "Company domain name.",
 "type": "string"
 },
 "ticker": {
 "description": "Company stock symbol (ticker) if available.",
 "type": [
 "string",
 "null"
 ]
 },
 "company_name": {
 "description": "Company name if available.",
 "type": [
 "string",
 "null"
 ]
 }
 }
 }
 }
 }
 },
 "meta": {
 "description": "Meta information about response. Only shown in API responses.\n",
 "type": "object",
 "properties": {
 "count": {
 "description": "Total number of results.\n \n**NOTE:** For performance reasons it will be omitted from response if \"page\" request parameter is not\nprovided.\n",
 "type": "integer",
 "example": 344
 },
 "message": {
 "description": "Additional information about response.\n",
 "type": "string"
 },
 "message_type": {
 "description": "Type of the message.\n",
 "enum": [
 "accepted"
 ]
 },
 "redirects_to_domain": {
 "description": "Domain that this company domain redirects to.\n",
 "type": [
 "string",
 "null"
 ],
 "example": "ibm.com"
 },
 "parent_company_domain": {
 "description": "Parent company domain.\n",
 "type": [
 "string",
 "null"
 ],
 "example": "ibm.com"
 }
 },
 "example": {
 "count": 344,
 "message": "...",
 "message_type": "...",
 "redirects_to_domain": "google.com",
 "parent_company_domain": "google.com"
 },
 "required": [
 "redirects_to_domain",
 "parent_company_domain"
 ]
 }
 },
 "example": {
 "data": [
 {
 "id": "fc4f22ed-975e-4826-9e21-31ec98da19a3",
 "type": "subpage",
 "attributes": {
 "category": "solutions",
 "url": "https://nonamesecurity.com/platform/security-testing",
 "text_content": "Leave no API untested and Shift Left with API security testing. Add security into your CI/CD pipeline without sacrificing speed and ensure that APIs aren't implemented with security vulnerabilities.\nNoname Security Active Testing is a purpose-built API security testing solution that understands your unique business logic and provides comprehensive coverage of API-specific vulnerabilities. Active Testing helps you shift left and bake API security testing into every phase of development.\nReduce the risk of successful attacks without any changes to production infrastructure.\nAccelerate remediation and lower remediation costs by up to 100x by finding and fixing issues earlier.\nImprove compliance and avoid regulatory fines and reputational damage from incidents.\nImprove security without sacrificing velocity. Deliver secure code without having to become a security expert.\nReduce redundant pentesting and other third-party security testing costs with a proven solution.\nIncrease your organization's confidence in APIs with continuous testing throughout the SDLC.\nA holistic API Security strategy should account for both pre- and post-production. Testing during the API development process allows developers to catch vulnerabilities and flaws early.\nWow. We've worked with a lot of partners, but the speed Noname is moving at has been impressive. Active Testing is huge for any company that cares about their APIs. They continue to push the envelope and we're proud to partner with them.\nBy implementing Active Testing by Noname Security, we are able to provide developers with the tools they need to bridge the gap and secure APIs before production, and without having to become security experts.\nEmpower developers with best-in-class usability such as simple setup & automation, in-line test results, and contextual guidance for request failure mitigation.\nLeave no API untested with a unique ability to find and test every API based on an understanding of the application's business logic.\nActive Testing fully integrates with your existing continuous integration/continuous delivery (CI/CD) pipelines and tools, such as Jenkins and Postman, as well as all your ticketing and workflow tools such as ServiceNow, Slack, and Jira.\nTeams get dynamic API visibility across multiple states and environments throughout the CI/CD process. Import APIs from a wide range of sources with dynamic updates. Compare Swagger files to assess conformance, based on real implementation results.\nStreamline testing with role-based access controls so only the right teams can access APIs for testing.\nWhat is API security testing?\nAPI security testing is an important part of ensuring the safety and reliability of your web applications. It involves testing application programming interfaces (APIs) during development to identify vulnerabilities and potential threats. The goal is to ensure that APIs are protected from malicious attacks, data breaches, and other security incidents. Through API security testing, organizations can make sure that their APIs are secure and compliant with data privacy regulations.\nHow does API security testing protect me from attacks?\nAPI security testing can identify vulnerabilities in your APIs before they're exploited by attackers, helping you prevent data breaches. APIs are often used to access sensitive data, and by testing the APIs regularly, your organization can ensure that your environment is secure and protected from potential threats.\nWhat is Shift-Left security testing?\nShift Left is an approach of moving a variety of tasks earlier in the development process. This means that tasks that are traditionally done at a later stage of the operations should instead be performed at earlier stages-particularly those related to API security and software testing.\nShift-Left vs Shift-Right security testing?\nShift-left security approach moves testing to the left on the timeline, so the team performs tests earlier and more often in the life cycle. In contrast, a shift-right approach considers testing in production with real users to be more useful.\nShould we use Active Testing with other Noname products?\nYes, Active Testing should be used in parallel with the other modules from the Noname API Security platform. Active Testing is a tool to help uncover vulnerabilities with APIs pre-production, whereas our API Discovery, Posture Management, and Runtime Protection modules are for protecting APIs in production.\nIs Active Testing right for my needs?\nThe earlier you catch security vulnerabilities, the better. From both a cost perspective and remediation angle, it is much easier to correct issues during the development process of the API than after it has been released into production and is being actively used. Active Testing allows organizations to more confidently and efficiently deliver applications to the business and remain competitive securely.\nOrganizations now have an average of 15,000 APIs to secure, and this number is growing rapidly as thousands of new applications hit the market...\nAs the number and complexity of APIs continue to grow, companies face increasing challenges when securing their APIs.\nFocus on finding and remediating API security vulnerabilities during the development phase.\nExperience the speed, scale, and security that only Noname can provide. You'll never look at APIs the same way again.",
 "first_seen_at": "2023-08-20T01:06:12Z",
 "last_seen_at": "2024-09-08T08:08:45Z"
 },
 "relationships": {
 "company": {
 "data": {
 "id": "47d3f4f2-e119-50cb-93f8-49a5fac024e2",
 "type": "company"
 }
 }
 }
 }
 ],
 "included": [
 {
 "id": "47d3f4f2-e119-50cb-93f8-49a5fac024e2",
 "type": "company",
 "attributes": {
 "domain": "nonamesecurity.com",
 "company_name": "Noname Security Group",
 "ticker": null
 }
 }
 ]
 }
}

1.9.
GithubRepositoriesDataset

GitHub repositories gives insight into how frequently a website is contributing to its public GitHub repository.

GithubRepositoriesDataset	object
data	array
GithubRepository
attributes	object
url	string Url to GitHub repository. Format: uri
description	string nullable Description of GitHub repository.
first_seen_at	string ISO 8601 date when the GitHub repository was discovered. Format: date-time
relationships	object Contains references to related objects listed in property "included".
company	object
data	object
id	string PredictLeads ID of the included CompanyLite object. Format: uuid
type	string Type of the included CompanyLite object. Constant: "company"
timeseries_datapoints	object
data	array
Items	object
id	string PredictLeads ID of the included TimeseriesDatapointGithubRepository object. Format: uuid
type	string Type of the included TimeseriesDatapointGithubRepository object. Constant: "timeseries_datapoint"
included	array Contains objects referenced in property "relationships".
Items
One of	Discriminator property name: "type"
CompanyLite	object
id	string PredictLeads ID of the CompanyLite object. Format: uuid
type	string Type of the CompanyLite object. Constant: "company"
attributes	object
domain	string Company domain name.
ticker	string nullable Company stock symbol (ticker) if available.
company_name	string nullable Company name if available.
or
TimeseriesDatapointGithubRepository	object
id	string PredictLeads ID of the TimeseriesDatapointGithubRepository object. Format: uuid
type	string Type of the TimeseriesDatapointGithubRepository object. Constant: "timeseries_datapoint"
attributes	object
timestamp	string ISO 8601 date when data was detected. Format: date-time
data	object
forks	integer Number of times this repository was forked at the creation of this datapoint.
stars	integer Number of times this repository was starred at the creation of this datapoint.
watches	integer Number of GitHub profiles that were watching this repository at the creation of this datapoint.
last_update_at	string ISO 8601 date of the last update to the repository at the creation of this datapoint. Format: date-time
meta	object Meta information about response. Only shown in API responses.
count (optional)	integer Total number of results. NOTE: For performance reasons it will be omitted from response if "page" request parameter is not provided.
message (optional)	string Additional information about response.
message_type (optional)	string Type of the message. One of the values: "accepted"
redirects_to_domain	string nullable Domain that this company domain redirects to.
parent_company_domain	string nullable Parent company domain.

Example of GithubRepositoriesDataset object

{
  "data": [
    {
      "id": "1c2f062f-2a98-4094-b5f9-873da16f9466",
      "type": "github_repository",
      "attributes": {
        "url": "https://github.com/openmeterio/openmeter",
        "description": "Cloud Metering for AI, Billing and FinOps. Collect and aggregate millions of usage events in real-time.",
        "first_seen_at": "2023-06-21T05:28:35Z"
      },
      "relationships": {
        "company": {
          "data": {
            "id": "774930e8-6a14-5e6d-bac7-5df186711c52",
            "type": "company"
          }
        },
        "timeseries_datapoints": {
          "data": [
            {
              "id": "7e1b166f-5c76-43b0-bd05-5a339bbc6d67",
              "type": "timeseries_datapoint"
            },
            {
              "id": "78794042-40a9-424a-a049-0827d918791e",
              "type": "timeseries_datapoint"
            },
            {
              "id": "0598d12b-1526-48e8-8624-233a79acf842",
              "type": "timeseries_datapoint"
            },
            {
              "id": "315b14ba-c57a-43a0-ba1c-63d76daddd8d",
              "type": "timeseries_datapoint"
            },
            {
              "id": "da544be0-fd1a-4a6f-8d7e-c666f16b975a",
              "type": "timeseries_datapoint"
            },
            {
              "id": "0e1b5148-a022-4ed3-96f8-c9404980727a",
              "type": "timeseries_datapoint"
            },
            {
              "id": "424989d7-fb0c-4574-b0dc-b9b4389f3afe",
              "type": "timeseries_datapoint"
            },
            {
              "id": "5cf98ec1-b775-4a69-a74f-796c93a40603",
              "type": "timeseries_datapoint"
            },
            {
              "id": "8f38e1b3-5488-4fc9-a5be-28ca28a61de4",
              "type": "timeseries_datapoint"
            },
            {
              "id": "a471c2fb-19cd-451e-8d83-d01cccb906eb",
              "type": "timeseries_datapoint"
            },
            {
              "id": "534b84a5-e272-46c5-bd4e-bc26359ebf91",
              "type": "timeseries_datapoint"
            },
            {
              "id": "d1bad782-8532-402c-8f91-33560c66551b",
              "type": "timeseries_datapoint"
            },
            {
              "id": "c30ce065-ab4f-4076-b309-5b2e112c1bd7",
              "type": "timeseries_datapoint"
            },
            {
              "id": "69509321-ab79-4bf6-a997-ca64d6c9c987",
              "type": "timeseries_datapoint"
            },
            {
              "id": "72c7c725-6d69-4865-ad8f-6ae32d1aee58",
              "type": "timeseries_datapoint"
            },
            {
              "id": "4e521932-cbc7-41b5-b40e-f3cc54a40543",
              "type": "timeseries_datapoint"
            },
            {
              "id": "0dc772bf-ac85-4903-be41-fd5ac41c3f98",
              "type": "timeseries_datapoint"
            },
            {
              "id": "6340ad72-e92e-4405-bba5-710d91ffce29",
              "type": "timeseries_datapoint"
            },
            {
              "id": "651d12f9-e254-401b-a74a-0d5aa1d771ef",
              "type": "timeseries_datapoint"
            },
            {
              "id": "9f596991-e1dd-4b9b-89f4-dbdfba78e19a",
              "type": "timeseries_datapoint"
            },
            {
              "id": "3d18c7cc-373a-4200-9340-021d22526bbb",
              "type": "timeseries_datapoint"
            },
            {
              "id": "0176990c-3179-49fe-a554-3dba4e8753cf",
              "type": "timeseries_datapoint"
            },
            {
              "id": "51f78008-db13-42ee-a388-bde4d4bb90c0",
              "type": "timeseries_datapoint"
            },
            {
              "id": "6a9f5003-83f3-4f9f-b388-4eb06bdb2d77",
              "type": "timeseries_datapoint"
            },
            {
              "id": "150c58b8-e691-44cd-876f-a773cd8b75a1",
              "type": "timeseries_datapoint"
            },
            {
              "id": "cf8079bc-b8ed-45d7-9987-3bbce3a3b35f",
              "type": "timeseries_datapoint"
            },
            {
              "id": "9a93794c-3772-4396-9e88-e5e40a1f1c4f",
              "type": "timeseries_datapoint"
            },
            {
              "id": "36986e70-c55b-426a-be7d-1d5dcbccbb2b",
              "type": "timeseries_datapoint"
            },
            {
              "id": "0fdc8de1-d7d0-4020-92b2-f7723da86c3b",
              "type": "timeseries_datapoint"
            },
            {
              "id": "4f2c0926-e8f8-411c-b440-e23dcfb05c4f",
              "type": "timeseries_datapoint"
            },
            {
              "id": "406f1fb4-f840-4479-bf92-24024a3b7f2f",
              "type": "timeseries_datapoint"
            }
          ]
        }
      }
    }
  ],
  "included": [
    {
      "id": "7e1b166f-5c76-43b0-bd05-5a339bbc6d67",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2023-06-21 07:28:35 +0200",
        "data": {
          "forks": 5,
          "stars": 86,
          "watches": 3,
          "last_update_at": "2023-06-20T11:17:04Z"
        }
      }
    },
    {
      "id": "78794042-40a9-424a-a049-0827d918791e",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2023-06-21 07:28:35 +0200",
        "data": {
          "forks": 5,
          "stars": 86,
          "watches": 3,
          "last_update_at": "2023-06-20T11:17:04Z"
        }
      }
    },
    {
      "id": "0598d12b-1526-48e8-8624-233a79acf842",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2023-07-05 07:35:43 +0200",
        "data": {
          "forks": 12,
          "stars": 415,
          "watches": 6,
          "last_update_at": "2023-07-03T12:57:21Z"
        }
      }
    },
    {
      "id": "315b14ba-c57a-43a0-ba1c-63d76daddd8d",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2023-07-19 07:43:29 +0200",
        "data": {
          "forks": 15,
          "stars": 435,
          "watches": 6,
          "last_update_at": "2023-07-18T15:20:17Z"
        }
      }
    },
    {
      "id": "da544be0-fd1a-4a6f-8d7e-c666f16b975a",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2023-08-02 07:50:04 +0200",
        "data": {
          "forks": 15,
          "stars": 457,
          "watches": 6,
          "last_update_at": "2023-08-01T16:30:51Z"
        }
      }
    },
    {
      "id": "0e1b5148-a022-4ed3-96f8-c9404980727a",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2023-08-16 07:50:51 +0200",
        "data": {
          "forks": 16,
          "stars": 473,
          "watches": 6,
          "last_update_at": "2023-08-15T12:09:35Z"
        }
      }
    },
    {
      "id": "424989d7-fb0c-4574-b0dc-b9b4389f3afe",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2023-08-30 07:56:38 +0200",
        "data": {
          "forks": 16,
          "stars": 488,
          "watches": 6,
          "last_update_at": "2023-08-29T15:43:33Z"
        }
      }
    },
    {
      "id": "5cf98ec1-b775-4a69-a74f-796c93a40603",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2023-09-13 08:08:59 +0200",
        "data": {
          "forks": 19,
          "stars": 518,
          "watches": 6,
          "last_update_at": "2023-09-12T19:48:49Z"
        }
      }
    },
    {
      "id": "8f38e1b3-5488-4fc9-a5be-28ca28a61de4",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2023-09-27 08:15:02 +0200",
        "data": {
          "forks": 19,
          "stars": 552,
          "watches": 6,
          "last_update_at": "2023-09-26T23:33:33Z"
        }
      }
    },
    {
      "id": "a471c2fb-19cd-451e-8d83-d01cccb906eb",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2023-10-11 21:03:27 +0200",
        "data": {
          "forks": 20,
          "stars": 572,
          "watches": 7,
          "last_update_at": "2023-10-10T15:32:26Z"
        }
      }
    },
    {
      "id": "534b84a5-e272-46c5-bd4e-bc26359ebf91",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2023-10-26 04:40:09 +0200",
        "data": {
          "forks": 22,
          "stars": 589,
          "watches": 7,
          "last_update_at": "2023-10-21T09:35:39Z"
        }
      }
    },
    {
      "id": "d1bad782-8532-402c-8f91-33560c66551b",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2023-11-09 04:45:31 +0100",
        "data": {
          "forks": 23,
          "stars": 598,
          "watches": 7,
          "last_update_at": "2023-11-08T12:06:24Z"
        }
      }
    },
    {
      "id": "c30ce065-ab4f-4076-b309-5b2e112c1bd7",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2023-11-23 04:49:25 +0100",
        "data": {
          "forks": 24,
          "stars": 636,
          "watches": 7,
          "last_update_at": "2023-11-23T03:48:00Z"
        }
      }
    },
    {
      "id": "69509321-ab79-4bf6-a997-ca64d6c9c987",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2023-12-08 02:09:18 +0100",
        "data": {
          "forks": 25,
          "stars": 662,
          "watches": 7,
          "last_update_at": "2023-12-06T20:56:28Z"
        }
      }
    },
    {
      "id": "72c7c725-6d69-4865-ad8f-6ae32d1aee58",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2023-12-23 10:46:51 +0100",
        "data": {
          "forks": 26,
          "stars": 681,
          "watches": 8,
          "last_update_at": "2023-12-22T16:32:49Z"
        }
      }
    },
    {
      "id": "4e521932-cbc7-41b5-b40e-f3cc54a40543",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2024-01-07 03:54:34 +0100",
        "data": {
          "forks": 26,
          "stars": 708,
          "watches": 8,
          "last_update_at": "2024-01-06T14:38:59Z"
        }
      }
    },
    {
      "id": "0dc772bf-ac85-4903-be41-fd5ac41c3f98",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2024-01-21 08:22:54 +0100",
        "data": {
          "forks": 30,
          "stars": 722,
          "watches": 8,
          "last_update_at": "2024-01-19T10:55:47Z"
        }
      }
    },
    {
      "id": "6340ad72-e92e-4405-bba5-710d91ffce29",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2024-02-05 07:07:35 +0100",
        "data": {
          "forks": 31,
          "stars": 747,
          "watches": 8,
          "last_update_at": "2024-02-04T13:11:28Z"
        }
      }
    },
    {
      "id": "651d12f9-e254-401b-a74a-0d5aa1d771ef",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2024-02-19 10:41:17 +0100",
        "data": {
          "forks": 33,
          "stars": 767,
          "watches": 8,
          "last_update_at": "2024-02-18T09:54:41Z"
        }
      }
    },
    {
      "id": "9f596991-e1dd-4b9b-89f4-dbdfba78e19a",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2024-03-17 09:19:58 +0100",
        "data": {
          "forks": 37,
          "stars": 829,
          "watches": 8,
          "last_update_at": "2024-03-16T21:21:43Z"
        }
      }
    },
    {
      "id": "3d18c7cc-373a-4200-9340-021d22526bbb",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2024-03-22 11:13:22 +0100",
        "data": {
          "forks": 38,
          "stars": 834,
          "watches": 8,
          "last_update_at": "2024-03-21T18:15:37Z"
        }
      }
    },
    {
      "id": "0176990c-3179-49fe-a554-3dba4e8753cf",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2024-04-07 23:20:03 +0200",
        "data": {
          "forks": 41,
          "stars": 855,
          "watches": 8,
          "last_update_at": "2024-04-07T17:37:28Z"
        }
      }
    },
    {
      "id": "51f78008-db13-42ee-a388-bde4d4bb90c0",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2024-04-22 17:39:12 +0200",
        "data": {
          "forks": 41,
          "stars": 872,
          "watches": 8,
          "last_update_at": "2024-04-22T14:39:26Z"
        }
      }
    },
    {
      "id": "6a9f5003-83f3-4f9f-b388-4eb06bdb2d77",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2024-05-09 00:55:56 +0200",
        "data": {
          "forks": 45,
          "stars": 897,
          "watches": 8,
          "last_update_at": "2024-05-08T14:50:01Z"
        }
      }
    },
    {
      "id": "150c58b8-e691-44cd-876f-a773cd8b75a1",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2024-05-23 08:08:12 +0200",
        "data": {
          "forks": 47,
          "stars": 916,
          "watches": 9,
          "last_update_at": "2024-05-21T12:58:03Z"
        }
      }
    },
    {
      "id": "cf8079bc-b8ed-45d7-9987-3bbce3a3b35f",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2024-06-16 08:59:53 +0200",
        "data": {
          "forks": 50,
          "stars": 948,
          "watches": 10,
          "last_update_at": "2024-06-16T06:25:43Z"
        }
      }
    },
    {
      "id": "9a93794c-3772-4396-9e88-e5e40a1f1c4f",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2024-06-30 09:07:57 +0200",
        "data": {
          "forks": 51,
          "stars": 968,
          "watches": 10,
          "last_update_at": "2024-06-30T05:24:25Z"
        }
      }
    },
    {
      "id": "36986e70-c55b-426a-be7d-1d5dcbccbb2b",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2024-07-14 09:14:36 +0200",
        "data": {
          "forks": 55,
          "stars": 1013,
          "watches": 11,
          "last_update_at": "2024-07-13T19:57:41Z"
        }
      }
    },
    {
      "id": "0fdc8de1-d7d0-4020-92b2-f7723da86c3b",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2024-07-28 09:23:22 +0200",
        "data": {
          "forks": 56,
          "stars": 1027,
          "watches": 12,
          "last_update_at": "2024-07-26T12:47:28Z"
        }
      }
    },
    {
      "id": "4f2c0926-e8f8-411c-b440-e23dcfb05c4f",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2024-08-15 00:38:24 +0200",
        "data": {
          "forks": 57,
          "stars": 1041,
          "watches": 11,
          "last_update_at": "2024-08-14T19:02:47Z"
        }
      }
    },
    {
      "id": "406f1fb4-f840-4479-bf92-24024a3b7f2f",
      "type": "timeseries_datapoint",
      "attributes": {
        "timestamp": "2024-09-01 23:34:44 +0200",
        "data": {
          "forks": 59,
          "stars": 1063,
          "watches": 11,
          "last_update_at": "2024-08-31T09:00:01Z"
        }
      }
    },
    {
      "id": "774930e8-6a14-5e6d-bac7-5df186711c52",
      "type": "company",
      "attributes": {
        "domain": "openmeter.io",
        "company_name": "Openmeter",
        "ticker": null
      }
    }
  ]
}

{
 "title": "GithubRepositoriesDataset",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "description": "GitHub repositories gives insight into how frequently a website is contributing to its public GitHub\nrepository.\n",
 "type": "object",
 "required": [
 "data",
 "included"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "title": "GithubRepository",
 "required": [
 "id",
 "type",
 "attributes",
 "relationships"
 ],
 "properties": {
 "attributes": {
 "type": "object",
 "required": [
 "url",
 "description",
 "first_seen_at"
 ],
 "properties": {
 "url": {
 "description": "Url to GitHub repository.",
 "type": "string",
 "format": "uri"
 },
 "description": {
 "description": "Description of GitHub repository.",
 "type": [
 "string",
 "null"
 ]
 },
 "first_seen_at": {
 "description": "ISO 8601 date when the GitHub repository was discovered.",
 "type": "string",
 "format": "date-time"
 }
 }
 },
 "relationships": {
 "description": "Contains references to related objects listed in property \"included\".",
 "type": "object",
 "required": [
 "company",
 "timeseries_datapoints"
 ],
 "properties": {
 "company": {
 "title": "RelationshipCompanyLite",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included CompanyLite object.",
 "const": "company"
 }
 }
 }
 }
 },
 "timeseries_datapoints": {
 "title": "RelationshipTimeseriesDatapointGithubRepository",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the included TimeseriesDatapointGithubRepository object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included TimeseriesDatapointGithubRepository object.",
 "const": "timeseries_datapoint"
 }
 }
 }
 }
 }
 }
 }
 }
 }
 }
 },
 "included": {
 "description": "Contains objects referenced in property \"relationships\".",
 "type": "array",
 "items": {
 "oneOf": [
 {
 "title": "CompanyLite",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the CompanyLite object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the CompanyLite object.",
 "const": "company"
 },
 "attributes": {
 "type": "object",
 "required": [
 "domain",
 "ticker",
 "company_name"
 ],
 "properties": {
 "domain": {
 "description": "Company domain name.",
 "type": "string"
 },
 "ticker": {
 "description": "Company stock symbol (ticker) if available.",
 "type": [
 "string",
 "null"
 ]
 },
 "company_name": {
 "description": "Company name if available.",
 "type": [
 "string",
 "null"
 ]
 }
 }
 }
 }
 },
 {
 "title": "TimeseriesDatapointGithubRepository",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "PredictLeads ID of the TimeseriesDatapointGithubRepository object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the TimeseriesDatapointGithubRepository object.",
 "const": "timeseries_datapoint"
 },
 "attributes": {
 "type": "object",
 "required": [
 "timestamp",
 "data"
 ],
 "properties": {
 "timestamp": {
 "description": "ISO 8601 date when data was detected.",
 "type": "string",
 "format": "date-time"
 },
 "data": {
 "type": "object",
 "required": [
 "forks",
 "stars",
 "watches",
 "last_update_at"
 ],
 "properties": {
 "forks": {
 "description": "Number of times this repository was forked at the creation of this datapoint.",
 "type": "integer"
 },
 "stars": {
 "description": "Number of times this repository was starred at the creation of this datapoint.",
 "type": "integer"
 },
 "watches": {
 "description": "Number of GitHub profiles that were watching this repository at the creation of this datapoint.",
 "type": "integer"
 },
 "last_update_at": {
 "description": "ISO 8601 date of the last update to the repository at the creation of this datapoint.",
 "type": "string",
 "format": "date-time"
 }
 }
 }
 }
 }
 }
 }
 ],
 "discriminator": {
 "propertyName": "type"
 }
 }
 },
 "meta": {
 "description": "Meta information about response. Only shown in API responses.\n",
 "type": "object",
 "properties": {
 "count": {
 "description": "Total number of results.\n \n**NOTE:** For performance reasons it will be omitted from response if \"page\" request parameter is not\nprovided.\n",
 "type": "integer",
 "example": 344
 },
 "message": {
 "description": "Additional information about response.\n",
 "type": "string"
 },
 "message_type": {
 "description": "Type of the message.\n",
 "enum": [
 "accepted"
 ]
 },
 "redirects_to_domain": {
 "description": "Domain that this company domain redirects to.\n",
 "type": [
 "string",
 "null"
 ],
 "example": "ibm.com"
 },
 "parent_company_domain": {
 "description": "Parent company domain.\n",
 "type": [
 "string",
 "null"
 ],
 "example": "ibm.com"
 }
 },
 "example": {
 "count": 344,
 "message": "...",
 "message_type": "...",
 "redirects_to_domain": "google.com",
 "parent_company_domain": "google.com"
 },
 "required": [
 "redirects_to_domain",
 "parent_company_domain"
 ]
 }
 },
 "example": {
 "data": [
 {
 "id": "1c2f062f-2a98-4094-b5f9-873da16f9466",
 "type": "github_repository",
 "attributes": {
 "url": "https://github.com/openmeterio/openmeter",
 "description": "Cloud Metering for AI, Billing and FinOps. Collect and aggregate millions of usage events in real-time.",
 "first_seen_at": "2023-06-21T05:28:35Z"
 },
 "relationships": {
 "company": {
 "data": {
 "id": "774930e8-6a14-5e6d-bac7-5df186711c52",
 "type": "company"
 }
 },
 "timeseries_datapoints": {
 "data": [
 {
 "id": "7e1b166f-5c76-43b0-bd05-5a339bbc6d67",
 "type": "timeseries_datapoint"
 },
 {
 "id": "78794042-40a9-424a-a049-0827d918791e",
 "type": "timeseries_datapoint"
 },
 {
 "id": "0598d12b-1526-48e8-8624-233a79acf842",
 "type": "timeseries_datapoint"
 },
 {
 "id": "315b14ba-c57a-43a0-ba1c-63d76daddd8d",
 "type": "timeseries_datapoint"
 },
 {
 "id": "da544be0-fd1a-4a6f-8d7e-c666f16b975a",
 "type": "timeseries_datapoint"
 },
 {
 "id": "0e1b5148-a022-4ed3-96f8-c9404980727a",
 "type": "timeseries_datapoint"
 },
 {
 "id": "424989d7-fb0c-4574-b0dc-b9b4389f3afe",
 "type": "timeseries_datapoint"
 },
 {
 "id": "5cf98ec1-b775-4a69-a74f-796c93a40603",
 "type": "timeseries_datapoint"
 },
 {
 "id": "8f38e1b3-5488-4fc9-a5be-28ca28a61de4",
 "type": "timeseries_datapoint"
 },
 {
 "id": "a471c2fb-19cd-451e-8d83-d01cccb906eb",
 "type": "timeseries_datapoint"
 },
 {
 "id": "534b84a5-e272-46c5-bd4e-bc26359ebf91",
 "type": "timeseries_datapoint"
 },
 {
 "id": "d1bad782-8532-402c-8f91-33560c66551b",
 "type": "timeseries_datapoint"
 },
 {
 "id": "c30ce065-ab4f-4076-b309-5b2e112c1bd7",
 "type": "timeseries_datapoint"
 },
 {
 "id": "69509321-ab79-4bf6-a997-ca64d6c9c987",
 "type": "timeseries_datapoint"
 },
 {
 "id": "72c7c725-6d69-4865-ad8f-6ae32d1aee58",
 "type": "timeseries_datapoint"
 },
 {
 "id": "4e521932-cbc7-41b5-b40e-f3cc54a40543",
 "type": "timeseries_datapoint"
 },
 {
 "id": "0dc772bf-ac85-4903-be41-fd5ac41c3f98",
 "type": "timeseries_datapoint"
 },
 {
 "id": "6340ad72-e92e-4405-bba5-710d91ffce29",
 "type": "timeseries_datapoint"
 },
 {
 "id": "651d12f9-e254-401b-a74a-0d5aa1d771ef",
 "type": "timeseries_datapoint"
 },
 {
 "id": "9f596991-e1dd-4b9b-89f4-dbdfba78e19a",
 "type": "timeseries_datapoint"
 },
 {
 "id": "3d18c7cc-373a-4200-9340-021d22526bbb",
 "type": "timeseries_datapoint"
 },
 {
 "id": "0176990c-3179-49fe-a554-3dba4e8753cf",
 "type": "timeseries_datapoint"
 },
 {
 "id": "51f78008-db13-42ee-a388-bde4d4bb90c0",
 "type": "timeseries_datapoint"
 },
 {
 "id": "6a9f5003-83f3-4f9f-b388-4eb06bdb2d77",
 "type": "timeseries_datapoint"
 },
 {
 "id": "150c58b8-e691-44cd-876f-a773cd8b75a1",
 "type": "timeseries_datapoint"
 },
 {
 "id": "cf8079bc-b8ed-45d7-9987-3bbce3a3b35f",
 "type": "timeseries_datapoint"
 },
 {
 "id": "9a93794c-3772-4396-9e88-e5e40a1f1c4f",
 "type": "timeseries_datapoint"
 },
 {
 "id": "36986e70-c55b-426a-be7d-1d5dcbccbb2b",
 "type": "timeseries_datapoint"
 },
 {
 "id": "0fdc8de1-d7d0-4020-92b2-f7723da86c3b",
 "type": "timeseries_datapoint"
 },
 {
 "id": "4f2c0926-e8f8-411c-b440-e23dcfb05c4f",
 "type": "timeseries_datapoint"
 },
 {
 "id": "406f1fb4-f840-4479-bf92-24024a3b7f2f",
 "type": "timeseries_datapoint"
 }
 ]
 }
 }
 }
 ],
 "included": [
 {
 "id": "7e1b166f-5c76-43b0-bd05-5a339bbc6d67",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2023-06-21 07:28:35 +0200",
 "data": {
 "forks": 5,
 "stars": 86,
 "watches": 3,
 "last_update_at": "2023-06-20T11:17:04Z"
 }
 }
 },
 {
 "id": "78794042-40a9-424a-a049-0827d918791e",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2023-06-21 07:28:35 +0200",
 "data": {
 "forks": 5,
 "stars": 86,
 "watches": 3,
 "last_update_at": "2023-06-20T11:17:04Z"
 }
 }
 },
 {
 "id": "0598d12b-1526-48e8-8624-233a79acf842",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2023-07-05 07:35:43 +0200",
 "data": {
 "forks": 12,
 "stars": 415,
 "watches": 6,
 "last_update_at": "2023-07-03T12:57:21Z"
 }
 }
 },
 {
 "id": "315b14ba-c57a-43a0-ba1c-63d76daddd8d",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2023-07-19 07:43:29 +0200",
 "data": {
 "forks": 15,
 "stars": 435,
 "watches": 6,
 "last_update_at": "2023-07-18T15:20:17Z"
 }
 }
 },
 {
 "id": "da544be0-fd1a-4a6f-8d7e-c666f16b975a",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2023-08-02 07:50:04 +0200",
 "data": {
 "forks": 15,
 "stars": 457,
 "watches": 6,
 "last_update_at": "2023-08-01T16:30:51Z"
 }
 }
 },
 {
 "id": "0e1b5148-a022-4ed3-96f8-c9404980727a",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2023-08-16 07:50:51 +0200",
 "data": {
 "forks": 16,
 "stars": 473,
 "watches": 6,
 "last_update_at": "2023-08-15T12:09:35Z"
 }
 }
 },
 {
 "id": "424989d7-fb0c-4574-b0dc-b9b4389f3afe",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2023-08-30 07:56:38 +0200",
 "data": {
 "forks": 16,
 "stars": 488,
 "watches": 6,
 "last_update_at": "2023-08-29T15:43:33Z"
 }
 }
 },
 {
 "id": "5cf98ec1-b775-4a69-a74f-796c93a40603",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2023-09-13 08:08:59 +0200",
 "data": {
 "forks": 19,
 "stars": 518,
 "watches": 6,
 "last_update_at": "2023-09-12T19:48:49Z"
 }
 }
 },
 {
 "id": "8f38e1b3-5488-4fc9-a5be-28ca28a61de4",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2023-09-27 08:15:02 +0200",
 "data": {
 "forks": 19,
 "stars": 552,
 "watches": 6,
 "last_update_at": "2023-09-26T23:33:33Z"
 }
 }
 },
 {
 "id": "a471c2fb-19cd-451e-8d83-d01cccb906eb",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2023-10-11 21:03:27 +0200",
 "data": {
 "forks": 20,
 "stars": 572,
 "watches": 7,
 "last_update_at": "2023-10-10T15:32:26Z"
 }
 }
 },
 {
 "id": "534b84a5-e272-46c5-bd4e-bc26359ebf91",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2023-10-26 04:40:09 +0200",
 "data": {
 "forks": 22,
 "stars": 589,
 "watches": 7,
 "last_update_at": "2023-10-21T09:35:39Z"
 }
 }
 },
 {
 "id": "d1bad782-8532-402c-8f91-33560c66551b",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2023-11-09 04:45:31 +0100",
 "data": {
 "forks": 23,
 "stars": 598,
 "watches": 7,
 "last_update_at": "2023-11-08T12:06:24Z"
 }
 }
 },
 {
 "id": "c30ce065-ab4f-4076-b309-5b2e112c1bd7",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2023-11-23 04:49:25 +0100",
 "data": {
 "forks": 24,
 "stars": 636,
 "watches": 7,
 "last_update_at": "2023-11-23T03:48:00Z"
 }
 }
 },
 {
 "id": "69509321-ab79-4bf6-a997-ca64d6c9c987",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2023-12-08 02:09:18 +0100",
 "data": {
 "forks": 25,
 "stars": 662,
 "watches": 7,
 "last_update_at": "2023-12-06T20:56:28Z"
 }
 }
 },
 {
 "id": "72c7c725-6d69-4865-ad8f-6ae32d1aee58",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2023-12-23 10:46:51 +0100",
 "data": {
 "forks": 26,
 "stars": 681,
 "watches": 8,
 "last_update_at": "2023-12-22T16:32:49Z"
 }
 }
 },
 {
 "id": "4e521932-cbc7-41b5-b40e-f3cc54a40543",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2024-01-07 03:54:34 +0100",
 "data": {
 "forks": 26,
 "stars": 708,
 "watches": 8,
 "last_update_at": "2024-01-06T14:38:59Z"
 }
 }
 },
 {
 "id": "0dc772bf-ac85-4903-be41-fd5ac41c3f98",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2024-01-21 08:22:54 +0100",
 "data": {
 "forks": 30,
 "stars": 722,
 "watches": 8,
 "last_update_at": "2024-01-19T10:55:47Z"
 }
 }
 },
 {
 "id": "6340ad72-e92e-4405-bba5-710d91ffce29",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2024-02-05 07:07:35 +0100",
 "data": {
 "forks": 31,
 "stars": 747,
 "watches": 8,
 "last_update_at": "2024-02-04T13:11:28Z"
 }
 }
 },
 {
 "id": "651d12f9-e254-401b-a74a-0d5aa1d771ef",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2024-02-19 10:41:17 +0100",
 "data": {
 "forks": 33,
 "stars": 767,
 "watches": 8,
 "last_update_at": "2024-02-18T09:54:41Z"
 }
 }
 },
 {
 "id": "9f596991-e1dd-4b9b-89f4-dbdfba78e19a",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2024-03-17 09:19:58 +0100",
 "data": {
 "forks": 37,
 "stars": 829,
 "watches": 8,
 "last_update_at": "2024-03-16T21:21:43Z"
 }
 }
 },
 {
 "id": "3d18c7cc-373a-4200-9340-021d22526bbb",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2024-03-22 11:13:22 +0100",
 "data": {
 "forks": 38,
 "stars": 834,
 "watches": 8,
 "last_update_at": "2024-03-21T18:15:37Z"
 }
 }
 },
 {
 "id": "0176990c-3179-49fe-a554-3dba4e8753cf",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2024-04-07 23:20:03 +0200",
 "data": {
 "forks": 41,
 "stars": 855,
 "watches": 8,
 "last_update_at": "2024-04-07T17:37:28Z"
 }
 }
 },
 {
 "id": "51f78008-db13-42ee-a388-bde4d4bb90c0",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2024-04-22 17:39:12 +0200",
 "data": {
 "forks": 41,
 "stars": 872,
 "watches": 8,
 "last_update_at": "2024-04-22T14:39:26Z"
 }
 }
 },
 {
 "id": "6a9f5003-83f3-4f9f-b388-4eb06bdb2d77",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2024-05-09 00:55:56 +0200",
 "data": {
 "forks": 45,
 "stars": 897,
 "watches": 8,
 "last_update_at": "2024-05-08T14:50:01Z"
 }
 }
 },
 {
 "id": "150c58b8-e691-44cd-876f-a773cd8b75a1",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2024-05-23 08:08:12 +0200",
 "data": {
 "forks": 47,
 "stars": 916,
 "watches": 9,
 "last_update_at": "2024-05-21T12:58:03Z"
 }
 }
 },
 {
 "id": "cf8079bc-b8ed-45d7-9987-3bbce3a3b35f",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2024-06-16 08:59:53 +0200",
 "data": {
 "forks": 50,
 "stars": 948,
 "watches": 10,
 "last_update_at": "2024-06-16T06:25:43Z"
 }
 }
 },
 {
 "id": "9a93794c-3772-4396-9e88-e5e40a1f1c4f",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2024-06-30 09:07:57 +0200",
 "data": {
 "forks": 51,
 "stars": 968,
 "watches": 10,
 "last_update_at": "2024-06-30T05:24:25Z"
 }
 }
 },
 {
 "id": "36986e70-c55b-426a-be7d-1d5dcbccbb2b",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2024-07-14 09:14:36 +0200",
 "data": {
 "forks": 55,
 "stars": 1013,
 "watches": 11,
 "last_update_at": "2024-07-13T19:57:41Z"
 }
 }
 },
 {
 "id": "0fdc8de1-d7d0-4020-92b2-f7723da86c3b",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2024-07-28 09:23:22 +0200",
 "data": {
 "forks": 56,
 "stars": 1027,
 "watches": 12,
 "last_update_at": "2024-07-26T12:47:28Z"
 }
 }
 },
 {
 "id": "4f2c0926-e8f8-411c-b440-e23dcfb05c4f",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2024-08-15 00:38:24 +0200",
 "data": {
 "forks": 57,
 "stars": 1041,
 "watches": 11,
 "last_update_at": "2024-08-14T19:02:47Z"
 }
 }
 },
 {
 "id": "406f1fb4-f840-4479-bf92-24024a3b7f2f",
 "type": "timeseries_datapoint",
 "attributes": {
 "timestamp": "2024-09-01 23:34:44 +0200",
 "data": {
 "forks": 59,
 "stars": 1063,
 "watches": 11,
 "last_update_at": "2024-08-31T09:00:01Z"
 }
 }
 },
 {
 "id": "774930e8-6a14-5e6d-bac7-5df186711c52",
 "type": "company",
 "attributes": {
 "domain": "openmeter.io",
 "company_name": "Openmeter",
 "ticker": null
 }
 }
 ]
 }
}

3.
API Endpoints

3.1.
Introduction

Our API is RESTful with responses delivered in JSON format.

For easier implementation, we provide API description also in OpenAPI schema (version 3.1.0) JSON format and OpenAPI SwaggerUI playground.

In order to use our API, you must first sign up to get an authentication token.

You can see live status of our API services.

If you would like to receive data as Flat Files or via Webhooks, please get in touch with our sales.

Base URL

3.1.1.
Authentication

Authentication is done via the authentication token which you can find on your subscription plans page.

In order to use the API you need to call an endpoint URL with both your API token and API key.

The primary and recommended method for authenticating with PredictLeads is to specify the API key in the HTTP request header using an extended header field.

3.1.2.
Request Limits

Each account using the API has request limits implemented. The amount of requests one can do each month is limited by the chosen plan. You can track your monthly usage on your subscription plans page.

Once the account reaches the limit, all further requests will produce a 403 HTTP error, notifying the user of the reached limit. If you have any further questions regarding the limits, feel free to contact us via support.

3.1.3.
Pagination

Responses with a list of objects are paginated. Use page parameter to get more results. Total number of all possible results is displayed inside meta attributes as count.

3.1.4.
Webhooks

3.1.4.1.
Authentication

To validate whether the webhook came from PredictLeads, we suggest verifying the webhook payloads with the X-Predict-Signature header (which we pass with every webhook).

Header payload is a SHA1 HMAC hexdigest computed with your token and the raw body of the request.

3.1.4.2.
Receiving Objects

Whenever we find a new data for one of the companies you are tracking, we wll send a POST request to your general Webhook URL with the object.

Go to your subscription plans page to enter your Webhook URL.

3.2.
Follow Companies

Follow Companies Endpoints

GET	/followings	Retrieve followed companies
POST	/companies/{domain}/follow	Follow the company
POST	/companies/{domain}/unfollow	Unfollow the company

3.2.1.
Retrieve followed companies

Returns followed companies.

GET

https://predictleads.com/api/v3/followings

Query Parameters
page (optional)	integer Page number of shown items. NOTE: If the parameter is not provided, the meta property "count" will be omitted from response for performance reasons. Default: 1 Minimum: 1
limit (optional)	integer Limit the number of shown items per page. Default: 100 Maximum: 1000

Example of requests

Example of responses

3.2.2.
Follow the company

Follow the company.

POST

https://predictleads.com/api/v3/companies/{domain}/follow

Path Parameters
domain	string Company's domain.

Example of requests

Example of responses

3.2.3.
Unfollow the company

Unfollow the company.

POST

https://predictleads.com/api/v3/companies/{domain}/unfollow

Path Parameters
domain	string Company's domain.

Example of requests

Example of responses

3.3.
Companies Dataset

Companies Dataset Endpoints

GET

/companies/{domain}

Retrieve Company

3.3.1.
Retrieve Company

Returns Company.

GET

https://predictleads.com/api/v3/companies/{domain}

Path Parameters
domain	string Company's domain.
Query Parameters
page (optional)	integer Page number of shown items. NOTE: If the parameter is not provided, the meta property "count" will be omitted from response for performance reasons. Default: 1 Minimum: 1
limit (optional)	integer Limit the number of shown items per page. Default: 100 Maximum: 1000

Example of requests

Example of responses

3.4.
News Events Dataset

News Events Dataset Endpoints

GET	/companies/{domain}/news_events	Retrieve company's News Events
GET	/news_events/{id}	Retrieve a single News Event

3.4.1.
Retrieve company's News Events

Returns a list of company's News Events.

GET

https://predictleads.com/api/v3/companies/{domain}/news_events

Path Parameters
domain	string Company's domain.
Query Parameters
page (optional)	integer Page number of shown items. NOTE: If the parameter is not provided, the meta property "count" will be omitted from response for performance reasons. Default: 1 Minimum: 1
limit (optional)	integer Limit the number of shown items per page. Default: 100 Maximum: 1000

Example of requests

Example of responses

Request was successfully processed.

],
        "headcount": null,
        "job_title": null,
        "job_title_tags": [

3.4.2.
Retrieve a single News Event

Returns specific News Event.

GET

https://predictleads.com/api/v3/news_events/{id}

Path Parameters
id	uuid News Event's id.

Example of requests

Example of responses

Request was successfully processed.

],
        "headcount": null,
        "job_title": null,
        "job_title_tags": [

3.5.
Financing Events Dataset

Financing Events Dataset Endpoints

GET

/companies/{domain}/financing_events

Retrieve company's Financing Events

3.5.1.
Retrieve company's Financing Events

Returns a list of company's Financing Events.

GET

https://predictleads.com/api/v3/companies/{domain}/financing_events

Path Parameters
domain	string Company's domain.
Query Parameters
page (optional)	integer Page number of shown items. NOTE: If the parameter is not provided, the meta property "count" will be omitted from response for performance reasons. Default: 1 Minimum: 1
limit (optional)	integer Limit the number of shown items per page. Default: 100 Maximum: 1000

Example of requests

Example of responses

3.6.
Job Openings Dataset

Job Openings Dataset Endpoints

GET	/companies/{domain}/job_openings	Retrieve company's Job Openings
GET	/job_openings/{id}	Retrieve a single Job Opening

3.6.1.
Retrieve company's Job Openings

Returns a list of company's Job Openings.

GET

https://predictleads.com/api/v3/companies/{domain}/job_openings

Path Parameters
domain	string Company's domain.
Query Parameters
categories (optional)	array Comma-separated (,) Job Opening categories.
Items	string One of the values: "none", "administration", "chairmen", "health_care", "hospitality", "engineering", "education", "maintenance", "finance", "information_technology", "management", "operations", "partnerships", "human_resources", "publishing", "purchasing", "sales", "marketing", "transportation", "directors", "design", "software_development", "general_technology", "support", "data_analysis"
with_job_descriptions (optional)	boolean Set to true if you'd like returned Job Openings to include their descriptions.
active_only (optional)	boolean Set to true if you'd like to receive Job Openings that are not closed, have last_seen_at more recent than 5 days and were found in the last year.
not_closed (optional)	boolean Similar to `active_only`, but without considering `last_seen_at` timestamp.
first_seen_at_from (optional)	string Only return Job Openings first seen after given date. Format: date
first_seen_at_until (optional)	string Only return Job Openings first seen before given date. Format: date
last_seen_at_from (optional)	string Only return Job Openings last seen after given date. Format: date
last_seen_at_until (optional)	string Only return Job Openings last seen before given date. Format: date
with_description_only (optional)	boolean Only return Job Openings that have description.
with_location_only (optional)	boolean Only return Job Openings that have location.
page (optional)	integer Page number of shown items. NOTE: If the parameter is not provided, the meta property "count" will be omitted from response for performance reasons. Default: 1 Minimum: 1
limit (optional)	integer Limit the number of shown items per page. Default: 100 Maximum: 1000

Example of requests

Example of responses

Request was successfully processed.

3.6.2.
Retrieve a single Job Opening

Returns a single Job Opening.

GET

https://predictleads.com/api/v3/job_openings/{id}

Path Parameters
id	uuid Job Opening's id.

Example of requests

Example of responses

Request was successfully processed.

3.7.
Technologies Dataset

Technologies Dataset Endpoints

GET	/technologies	Retrieve Technologies
GET	/technologies/{id}	Retrieve a single Technology
GET	/discover/technologies	Discover Technologies

3.7.1.
Retrieve Technologies

Returns a list of Technologies ordered by creation date ascending.

GET

https://predictleads.com/api/v3/technologies

Query Parameters
page (optional)	integer Page number of shown items. NOTE: If the parameter is not provided, the meta property "count" will be omitted from response for performance reasons. Default: 1 Minimum: 1
limit (optional)	integer Limit the number of shown items per page. Default: 100 Maximum: 1000

Example of requests

Example of responses

3.7.2.
Retrieve a single Technology

Returns a single Technology.

GET

https://predictleads.com/api/v3/technologies/{id}

Path Parameters
id	uuid Technology's id.

Example of requests

Example of responses

3.7.3.
Discover Technologies

Returns a list of Technologies ordered by creation date descending.

GET

https://predictleads.com/api/v3/discover/technologies

Query Parameters
page (optional)	integer Page number of shown items. NOTE: If the parameter is not provided, the meta property "count" will be omitted from response for performance reasons. Default: 1 Minimum: 1
limit (optional)	integer Limit the number of shown items per page. Default: 100 Maximum: 1000

Example of requests

Example of responses

3.8.
Technology Detections Dataset

Technology Detections Dataset Endpoints

GET	/companies/{domain}/technology_detections	Retrieve company's Technology Detections
GET	/technology_detections/{id}	Retrieve a single Technology Detection
GET	/discover/technologies/{id}/technology_detections	Discover Technology Detections for a given Technology ID

3.8.1.
Retrieve company's Technology Detections

Returns a list of company's Technology Detections.

GET

https://predictleads.com/api/v3/companies/{domain}/technology_detections

Path Parameters
domain	string Company's domain.
Query Parameters
page (optional)	integer Page number of shown items. NOTE: If the parameter is not provided, the meta property "count" will be omitted from response for performance reasons. Default: 1 Minimum: 1
limit (optional)	integer Limit the number of shown items per page. Default: 100 Maximum: 1000

Example of requests

Example of responses

Request was successfully processed.

3.8.2.
Retrieve a single Technology Detection

Returns a single Technology Detection.

GET

https://predictleads.com/api/v3/technology_detections/{id}

Path Parameters
id	uuid Technology Detection's id.

Example of requests

Example of responses

Request was successfully processed.

3.8.3.
Discover Technology Detections for a given Technology ID

Returns a list of Technology Detections for a given Technology ID, ordered by date found, descending.

GET

https://predictleads.com/api/v3/discover/technologies/{id}/technology_detections

Path Parameters
id	uuid Technology's id.
Query Parameters
page (optional)	integer Page number of shown items. NOTE: If the parameter is not provided, the meta property "count" will be omitted from response for performance reasons. Default: 1 Minimum: 1
limit (optional)	integer Limit the number of shown items per page. Default: 100 Maximum: 1000

Example of requests

Example of responses

3.9.
Connections Dataset

Connections Dataset Endpoints

GET

/companies/{domain}/connections

Retrieve company's Connections

3.9.1.
Retrieve company's Connections

Returns a list of company's Connections.

GET

https://predictleads.com/api/v3/companies/{domain}/connections

Path Parameters
domain	string Company's domain.
Query Parameters
page (optional)	integer Page number of shown items. NOTE: If the parameter is not provided, the meta property "count" will be omitted from response for performance reasons. Default: 1 Minimum: 1
limit (optional)	integer Limit the number of shown items per page. Default: 100 Maximum: 1000

Example of requests

Example of responses

3.10.
Website Evolution Dataset

Website Evolution Dataset Endpoints

GET

/companies/{domain}/website_evolution

Retrieve company's Website Evolution

3.10.1.
Retrieve company's Website Evolution

Returns a list of company's Website Evolution.

GET

https://predictleads.com/api/v3/companies/{domain}/website_evolution

Path Parameters
domain	string Company's domain.
Query Parameters
page (optional)	integer Page number of shown items. NOTE: If the parameter is not provided, the meta property "count" will be omitted from response for performance reasons. Default: 1 Minimum: 1
limit (optional)	integer Limit the number of shown items per page. Default: 100 Maximum: 1000

Example of requests

Example of responses

Request was successfully processed.

3.11.
Github Repositories Dataset

Github Repositories Dataset Endpoints

GET

/companies/{domain}/github_repositories

Retrieve company's Github Repositories

3.11.1.
Retrieve company's Github Repositories

Returns a list of company's Github Repositories.

GET

https://predictleads.com/api/v3/companies/{domain}/github_repositories

Path Parameters
domain	string Company's domain.
Query Parameters
page (optional)	integer Page number of shown items. NOTE: If the parameter is not provided, the meta property "count" will be omitted from response for performance reasons. Default: 1 Minimum: 1
limit (optional)	integer Limit the number of shown items per page. Default: 100 Maximum: 1000

Example of requests

Example of responses

Request was successfully processed.