Documentation | PredictLeads

1.
Guide

1.1.
Introduction

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

We track globally 70+ million companies' information such as:

Companies Dataset
Information about companies.
Job Openings Dataset
Information about job openings from companies.
News Events Dataset
Information about news related to companies.
Financing Events Dataset
Information about investments in companies.
Connections Dataset
Information about company connections with other companies.
Technologies Dataset
Information about technologies.
Technology Detections Dataset
Information about technologies used by companies.
Website Evolution Dataset
Information about how websites of companies have been evolving.
Github Repositories Dataset
Information about public GitHub repositories of companies.

Our data can be delivered to you in Flat Files, through API Endpoints or via API Webhooks and it is structured and organized using Data Models.

1.2.
Use cases

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.

1.3.
Companies Dataset

1.3.1.
Introduction

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

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.

1.5.
News Events Dataset

1.5.1.
Introduction

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.

1.5.1.1.
Use cases

Lead Scoring
- Use Financing data to score companies that received funding recently higher.
- Use number of New Partnerships, Location Expansions, Awards Received, Product Launches etc. to score companies higher.
- To identify prospects matching your target persona you can use the news event categories to target e.g. companies that are expanding offices to a specific location, companies that have recently launched a new product, companies that have hit specific growth metrics using funding data, companies that invested in specific assets etc.
Personalization
The News Events data is very granular so it can be used to create automated personalized messaging to prospects.
E.g. personalized emails on the go that are optionally reviewed by a human being before sending out.

1.5.2.
Categories

Property in schema: category

Category	Group	Description
`acquires`	Acquisition	Company acquired another company.
`merges_with`	Acquisition	Company merges with another company.
`sells_assets_to`	Acquisition	Company sells assets (like properties or warehouses) to other company.
`signs_new_client`	Contract	Company signs new client.
`files_suit_against`	Corporate Challenges	Company files suit against other company.
`has_issues_with`	Corporate Challenges	Company has vulnerability problems.
`closes_offices_in`	Cost Cutting	Company closes existing offices.
`decreases_headcount_by`	Cost Cutting	Company lays off employees.
`expands_facilities`	Expansion	Company opens new or expands existing facilities like warehouses, data centers, manufacturing plants etc.
`expands_offices_in`	Expansion	Company expands existing offices.
`expands_offices_to`	Expansion	Company opens new offices in another town, state, country or continent.
`increases_headcount_by`	Expansion	Company offers new job vacancies.
`opens_new_location`	Expansion	Company opens new service location like hotels, restaurants, bars, hospitals etc.
`goes_public`	Investment	Company issues shares to the public for the first time.
`invests_into`	Investment	Company invests into other company.
`invests_into_assets`	Investment	Company buys assets (like properties or warehouses) from other company.
`receives_financing`	Investment	Company receives financing like venture funding, loan, grant etc.
`hires`	Leadership	Company hired new executive or senior personnel.
`leaves`	Leadership	Executive or senior personnel left the company.
`promotes`	Leadership	Company promoted existing executive or senior personnel.
`retires_from`	Leadership	Executive or senior personnel retire from the company.
`integrates_with`	New Offering	Company integrates with other company.
`is_developing`	New Offering	Company is developing a new offering.
`launches`	New Offering	Company launches new offering.
`partners_with`	Partnership	Company partners with other company.
`receives_award`	Recognition	Company or person at the company receives an award.
`recognized_as`	Recognition	Company or person at the company receives recognition.
`identified_as_competitor_of`	Relational	New or existing competitor was identified.

1.6.
Financing Events Dataset

1.6.1.
Introduction

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.

1.7.
Connections Dataset

1.7.1.
Introduction

Company Connections describe relationships between companies.

It is sourced from Case Study pages, Testimonials pages, Logo images and other resources.

1.7.2.
Use cases

The following use cases highlight some of the most common applications of our dataset in driving business decisions, investment strategies, and competitive analysis. These examples illustrate how key customer data can be leveraged to enhance connections, validate market position, and support strategic growth. For additional, more specialized use cases, please feel free to reach out for further details.

Leveraging Shared Customers
Identifying mutual customers or partners between a company and a potential lead of theirs can facilitate warmer introductions and build trust. Mentioning common partners or customers in outreach can make communications more relevant and increase engagement.
Outreach Personalization
This dataset also helps in identifying companies that are working with your competitors, presenting an opportunity to highlight your advantages.
Assessing Market Validation
Companies with well-known key customers or partnerships have demonstrated market acceptance and validation, making them more attractive prospects for investment due to their proven credibility and success.
Identifying Strategic Relationships
Information about a company's partners, vendors, and customers provides insights into its business ecosystem, helping asset managers evaluate its market position and growth potential.
Supporting Investment Decisions
Key customer data can strengthen investment decisions by providing evidence of a company's strong client base and revenue stability.
Partnerships and Collaborations
Identifying new partnerships or significant clients can signal a company's growth potential and market trust. For instance, a small tech company signing a partnership with a large enterprise can be a strong positive signal.
Company Network Health and Reputation
Analyzing the network health through PageRank algorithms or similar can provide insights into the strength and potential of a company's business relationships. Companies with strong, positive relationships are often more resilient and have better growth prospects. Companies with prestigious key customers or strategic partnerships demonstrate a higher degree of market validation and stability.
Market Position Analysis
Information about a company's clients and partners provides insights into its market position and competitive landscape, which can inform investment strategies.

1.7.3.
Categories

Property in schema: category

We make sure to keep the direction of the category consistent, so the results can be read like a sentence:

Company1 is a {category} to Company2.

The special case is the other category due to its non-specificity.

Category	Description
`partner`	Read as: Company1 is a partner of Company2. A partner relationship signals a collaboration between companies that goes both in two directions. You will find such connections using titles as "We work with", "Our partners", "The company we keep", and other titles similar in meaning. The implications of this category can be quite wide in their meaning, but in general all partnerships are a positive signal. NOTE: Until February 2024 the `partner` category did not always signal a true partnership between two companies, but it also included a tighter knit relationship compared to the `other` category, which are not always as strong as the partner keyword suggests. The meaning is now correctly followed for all new cases or cases that have been last seen since and still have the `partner` category.
`vendor`	Read as: Company1 is a vendor to Company2. The vendor category is much simpler in its meaning. It simply means that company1 is a supplier for company2 in some way. All vendor relationships are positive, although some are more positive than others. On a webpage, such connections can be seen in lists such as "Our customers", "Trusted by", "Enabling businesses to", etc. For example, to be a vendor to Microsoft, the company has to pass certain requirements as to company size, product reliability, safety, etc. This can be seen as a vetting process of some sort and increases the company's trustworthiness. The vendor category can also be used to determine the supply chain of a certain company and evaluate supply chain risks.
`integration`	Read as: Company1 has an integration with Company2. Most of the time an integration happens between a platform and a service. Case examples: Company1 has developed a Machine learning model. To make it more usable, they have developed an Azure Cloud integration. With this, the customers of the model can start using it more quickly, which is good for Company1. It's generally the service's development team that takes on the task of creating this integration. This can also be a positive signal for the platform (Company2). One could calculate the adoption of the Company2 platform and use this as a signal for growth.
`investor`	Read as: Company1 is an investor in Company2. The investor category identifies relationships between companies making investments (Company1) and companies receiving investments (Company2). The more investors the company has, the better. One could also track what companies/sectors competitors are investing in.
`parent`	Read as: Company1 is a parent company of Company2. Parent category defines a parent-subsidiary relationship between websites. In some cases such websites are only localized versions for specific markets. Case examples: "activisionblizzard.com" is a parent to "blizzard.com", and in turn "blizzard.com" is a parent to "blizzard.de". "coca-cola.com" is a parent to a vast array of websites, such as "coca-cola.fr", "coca-cola.de", "coca-cola.es", "coca-cola.tn", "coca-cola.se", etc. NOTE: Often it makes sense to not only check current hierarchy level e.g. "blizzard.de" but also check their parent connections e.g. "blizzard.com" and "activisionblizzard.com". Since sometimes company connections (customers, investors…) are also available at higher company hierarchical levels.
`rebranding`	Read as: Company1 is a rebranding of Company2. Company1 being a rebranding of Company2 suggests that Company2 has undergone a process to change its brand identity. We detect rebranding based on domain change and similarities between the old and the new website. Case examples: Top-level-domain (TLD) change: airmeet.co -> airmeet.com. airmeet.com is a rebranding of airmeet.co. TLD changed to higher value .com. Change to a shorter domain: lamborghini.com is a rebranding of lamborghini-madrid.com. Name change: hausarztpraxis-leinetal.de is a rebranding of praxis-schuenemann.de.
`published_in`	Read as: Company1 is published in Company2. The `published_in` category is usually found on early stage startup websites with fewer accomplishments on the market, but positive press coverage. You can see such connections under titles such as "As seen in", "Featured in", "Talking about us", etc. A `published_in` connection is a positive signal, but after some months or years, we would expect to see them removed and replaced with other content proving the legitimacy of the company.
`other`	Read as: Company1 is connected to Company2. This is the most general category. If we detected a relationship between companies, but we were unsure which category it belonged to, or it didn't belong to any of our categories, then we would categorize it as `other`. Outgoing `other` category connections are a slightly positive signal, while incoming ones are quite more so. Thus a connection Company1 -> other -> Company2 is more positive for Company2, than for Company1. As Company2 was featured on Company1 website. Sometimes the `other` category can have additional information via the source_category attribute depending on where it was found. An example of such a source_category would be `cookie_section`, which is explained further below. Such connections could be evaluated differently.

1.7.4.
Source Categories

Property in schema: source_category

The source_category property provides additional context on where and how the connection was found.

1.8.
Technologies Dataset

1.8.1.
Introduction

Technologies are detected on websites and jobs related posts.

1.9.
Technology Detections Dataset

1.9.1.
Introduction

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.

1.10.
Website Evolution Dataset

1.10.1.
Introduction

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.

1.11.
Github Repositories Dataset

1.11.1.
Introduction

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

2.
Data Models

2.1.
Companies Dataset

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

`CompaniesDataset`	`object`
`data`	`array`
`Company`	`object`
`id`	`string` Format: `uuid` ID of the `Company` object.
`type`	`string` Constant: `"company"` Type of the `Company` object.
`attributes`	`object` Attributes of Company 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` (optional)	`object`
`data`	`object`
`id`	`string` Format: `uuid` ID of the included `CompanyLite` object.
`type`	`string` Constant: `"company"` Type of the included `CompanyLite` object.
`redirects_to` (optional)	`object`
`data`	`object`
`id`	`string` Format: `uuid` ID of the included `CompanyLite` object.
`type`	`string` Constant: `"company"` Type of the included `CompanyLite` object.
`included`	`array` Contains objects referenced in property `relationships`.
`CompanyLite`	`object`
`id`	`string` Format: `uuid` ID of the `CompanyLite` object.
`type`	`string` Constant: `"company"` Type of the `CompanyLite` object.
`attributes`	`object` Attributes of CompanyLite 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 `CompaniesDataset` object.
`record_state`	`string` Constant: `"active"` Represents record state of objects.
`count` (optional)	`integer` Total number of results. NOTE: Only shown in API responses. For performance reasons it will be omitted from response if `page` request parameter is not provided.
`message` (optional)	`string` Additional information about response. NOTE: Only shown in API responses.
`message_type` (optional)	`string` One of the values: `"accepted"` Type of the message. NOTE: Only shown in API responses.

CompaniesDataset object

{
 "title": "CompaniesDataset",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "summary": "Information about companies",
 "description": "Companies dataset includes additional information about the company e.g. normalized company location,\nticker, meta title, meta description...\n",
 "type": "object",
 "required": [
 "data",
 "included"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "title": "Company",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes",
 "relationships"
 ],
 "properties": {
 "id": {
 "description": "ID of the `Company` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the `Company` object.",
 "const": "company"
 },
 "attributes": {
 "description": "Attributes of Company object.",
 "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",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "city",
 "state",
 "zip_code",
 "country",
 "region",
 "continent",
 "fuzzy_match"
 ],
 "properties": {
 "city": {
 "type": [
 "string",
 "null"
 ]
 },
 "state": {
 "type": [
 "string",
 "null"
 ]
 },
 "zip_code": {
 "type": [
 "string",
 "null"
 ]
 },
 "country": {
 "type": [
 "string",
 "null"
 ]
 },
 "region": {
 "type": [
 "string",
 "null"
 ]
 },
 "continent": {
 "type": [
 "string",
 "null"
 ]
 },
 "fuzzy_match": {
 "type": [
 "boolean",
 "null"
 ]
 }
 }
 }
 },
 "language": {
 "description": "Company website's language.",
 "type": "string"
 },
 "meta_title": {
 "description": "Company website's meta title.",
 "type": "string"
 },
 "meta_description": {
 "description": "Company website's meta title.",
 "type": "string"
 }
 }
 },
 "relationships": {
 "description": "Contains references to related objects listed in property `included`.",
 "type": "object",
 "properties": {
 "parent_company": {
 "title": "Relationship",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "ID of the included `CompanyLite` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included `CompanyLite` object.",
 "const": "company"
 }
 }
 }
 }
 },
 "redirects_to": {
 "title": "Relationship",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "ID of the included `CompanyLite` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included `CompanyLite` object.",
 "const": "company"
 }
 }
 }
 }
 }
 }
 }
 }
 }
 },
 "included": {
 "description": "Contains objects referenced in property `relationships`.",
 "type": "array",
 "items": {
 "title": "CompanyLite",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "ID of the `CompanyLite` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the `CompanyLite` object.",
 "const": "company"
 },
 "attributes": {
 "description": "Attributes of CompanyLite object.",
 "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 `CompaniesDataset` object.\n",
 "type": "object",
 "required": [
 "record_state"
 ],
 "properties": {
 "record_state": {
 "description": "Represents record state of objects.\n",
 "type": "string",
 "const": "active"
 },
 "count": {
 "description": "Total number of results.\n \n**NOTE**: Only shown in API responses. For performance reasons it will be omitted from response\nif `page` request parameter is not provided.\n",
 "type": "integer",
 "example": 344
 },
 "message": {
 "description": "Additional information about response.\n \n**NOTE**: Only shown in API responses.\n",
 "type": "string"
 },
 "message_type": {
 "description": "Type of the message.\n \n**NOTE**: Only shown in API responses.\n",
 "enum": [
 "accepted"
 ]
 }
 },
 "example": {
 "record_state": "active",
 "count": 344,
 "message": "...",
 "message_type": "..."
 }
 }
 },
 "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 for your Business - Homepage",
 "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": {
 }
 }
 ],
 "included": [

]
  }
}

2.2.
Job Openings Dataset

`JobOpeningsDataset`	`object`
`data`	`array`
`JobOpening`	`object`
`id`	`string` Format: `uuid` ID of the `JobOpening` object.
`type`	`string` Constant: `"job_opening"` Type of the `JobOpening` object.
`attributes`	`object` Attributes of JobOpening object.
`title`	`string` Job title.
`description`	`string nullable` Job opening description.
`url`	`string` Format: `uri` URL of the web page where the data was found.
`first_seen_at`	`string` Format: `date-time` Time & date (ISO 8601) of when the job opening was first seen.
`last_seen_at`	`string` Format: `date-time` Time & date (ISO 8601) of when the job opening was last seen.
`last_processed_at`	`string` Format: `date-time` Time & date (ISO 8601) of when the job opening was last sent to processing.
`contract_types`	`array` Example(s): `[ "full time", "remote" ]` Zero or more types describing the job opening.
`categories`	`array` Example(s): `[ "finance", "data_analysis" ]` Zero or more organizational categories of the job opening.
`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` Example(s): `"29-1021.00"` O*NET category code representing this job opening.
`family`	`string nullable` Example(s): `"Healthcare Practitioners and Technical"` O*NET family name matching the code for this job opening.
`occupation_name`	`string nullable` Example(s): `"Dentists, General"` O*NET occupation name matching the code for this job opening.
`salary`	`string nullable` Example(s): `"£40000 - £50000 GBP"` Raw salary string.
`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` One of the values: `"hour", "day", "week", "month", "year", null` Salary time unit.
`seniority`	`string nullable` Seniority inferred from the job opening title.
`status`	`string nullable` One of the values: `"closed", null` null or "closed" if keyword "job no longer available", "vacancy listing expired", "position filled", etc. is found on job opening site.
`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.
`relationships`	`object` Contains references to related objects listed in property `included`.
`company`	`object`
`data`	`object`
`id`	`string` Format: `uuid` ID of the included `CompanyLite` object.
`type`	`string` Constant: `"company"` Type of the included `CompanyLite` object.
`included`	`array` Contains objects referenced in property `relationships`.
`CompanyLite`	`object`
`id`	`string` Format: `uuid` ID of the `CompanyLite` object.
`type`	`string` Constant: `"company"` Type of the `CompanyLite` object.
`attributes`	`object` Attributes of CompanyLite 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 `JobOpeningsDataset` object.
`record_state`	`string` Constant: `"active"` Represents record state of objects.
`count` (optional)	`integer` Total number of results. NOTE: Only shown in API responses. For performance reasons it will be omitted from response if `page` request parameter is not provided.
`message` (optional)	`string` Additional information about response. NOTE: Only shown in API responses.
`message_type` (optional)	`string` One of the values: `"accepted"` Type of the message. NOTE: Only shown in API responses.

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": "2024-09-28T07:08:48Z",
        "last_seen_at": "2024-10-06T19:15:45Z",
        "last_processed_at": "2024-10-02T07:31:50Z",
        "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",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "summary": "Information about job openings from companies",
 "description": "Job Openings are found on companies' career sites and job boards. Job Openings and other descriptions can\nbe used to identify company investment areas, current priorities and future developments.\n \nProvided are fields like Job Category (marketing, sales, HR, support...), job description, salary, location\nand many more.\n",
 "type": "object",
 "required": [
 "data",
 "included"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "title": "JobOpening",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes",
 "relationships"
 ],
 "properties": {
 "id": {
 "description": "ID of the `JobOpening` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the `JobOpening` object.",
 "const": "job_opening"
 },
 "attributes": {
 "description": "Attributes of JobOpening object.",
 "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",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "city",
 "state",
 "zip_code",
 "country",
 "region",
 "continent",
 "fuzzy_match"
 ],
 "properties": {
 "city": {
 "type": [
 "string",
 "null"
 ]
 },
 "state": {
 "type": [
 "string",
 "null"
 ]
 },
 "zip_code": {
 "type": [
 "string",
 "null"
 ]
 },
 "country": {
 "type": [
 "string",
 "null"
 ]
 },
 "region": {
 "type": [
 "string",
 "null"
 ]
 },
 "continent": {
 "type": [
 "string",
 "null"
 ]
 },
 "fuzzy_match": {
 "type": [
 "boolean",
 "null"
 ]
 }
 }
 }
 },
 "tags": {
 "description": "Includes keywords found under job opening description.",
 "type": "array",
 "items": [
 {
 "type": "string"
 }
 ]
 }
 }
 },
 "relationships": {
 "description": "Contains references to related objects listed in property `included`.",
 "type": "object",
 "required": [
 "company"
 ],
 "properties": {
 "company": {
 "title": "Relationship",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "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",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "ID of the `CompanyLite` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the `CompanyLite` object.",
 "const": "company"
 },
 "attributes": {
 "description": "Attributes of CompanyLite object.",
 "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 `JobOpeningsDataset` object.\n",
 "type": "object",
 "required": [
 "record_state"
 ],
 "properties": {
 "record_state": {
 "description": "Represents record state of objects.\n",
 "type": "string",
 "const": "active"
 },
 "count": {
 "description": "Total number of results.\n \n**NOTE**: Only shown in API responses. For performance reasons it will be omitted from response\nif `page` request parameter is not provided.\n",
 "type": "integer",
 "example": 344
 },
 "message": {
 "description": "Additional information about response.\n \n**NOTE**: Only shown in API responses.\n",
 "type": "string"
 },
 "message_type": {
 "description": "Type of the message.\n \n**NOTE**: Only shown in API responses.\n",
 "enum": [
 "accepted"
 ]
 }
 },
 "example": {
 "record_state": "active",
 "count": 344,
 "message": "...",
 "message_type": "..."
 }
 }
 },
 "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": "2024-09-28T07:08:48Z",
 "last_seen_at": "2024-10-06T19:15:45Z",
 "last_processed_at": "2024-10-02T07:31:50Z",
 "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
 }
 }
 ]
 }
}

2.3.
News Events Dataset

`NewsEventsDataset`	`object`
`data`	`array`
`NewsEvent`	`object`
`id`	`string` Format: `uuid` ID of the `NewsEvent` object.
`type`	`string` Constant: `"news_event"` Type of the `NewsEvent` object.
`attributes`	`object` Attributes of NewsEvent object.
`summary`	`string` A short, human readable excerpt of this event data.
`category`	`string` 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"` Name of the category this event represents. Read more about categories...
`found_at`	`string` ISO 8601 date when the event was discovered and created.
`confidence`	`number` Maximum: 1 Minimum: 0 Floating point number of confidence. 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` Example(s): `"$1.8 million"` This is a string of a value as extracted from text.
`amount_normalized`	`integer nullable` Example(s): `18000000` Integer value of "amount" property.
`assets`	`string nullable` Example(s): `"hydrogen production facility"` Assets found in the News Event.
`assets_tags`	`array` Example(s): `[ "production" ]` An array of assets tags extracted from "assets" property.
Items	`string`
`award`	`string nullable` Example(s): `"Pierre Potier Award"` Award type this event is announcing.
`contact`	`string nullable` Example(s): `"Elon Musk"` Person name that the News Event mentions.
`conference`	`string nullable` Example(s): `"World Business Outlook Awards"` Conference name where the News Event was mentioned at.
`effective_date`	`string nullable` Example(s): `"2024-04-05"` ISO 8601 date (without time) the News Event is mentioning, in string format.
`division`	`string nullable` Whether event concerns a specific division.
`financing_type`	`string nullable` Example(s): `"Series B funding"` A financing type this event is mentioning.
`financing_type_normalized`	`string nullable` 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"` A normalized value of "financing_type" property, where possible.
`financing_type_tags`	`array` Example(s): `[ "equity" ]` An array of financing type categories.
Items	`string`
`headcount`	`integer nullable` Example(s): `200` A number of people mentioned organization is involved with.
`job_title`	`string nullable` Example(s): `"Director of Media and Analytics"` Job title this event is mentioning.
`job_title_tags`	`array` Example(s): `[ "marketing", "directors" ]` An array of job title tags.
Items	`string`
`location`	`string nullable` Example(s): `"Menlo Park, California, United States"` Location of where the event happened or where the organization has expanded/relocated.
`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` Example(s): `"Credit Pass"` Name of the product as mentioned by this event.
`product_data`	`object`
`full_text`	`string nullable` Example(s): `"Plant Control System", "3D printing service for spare parts"` Full product name as recognized by Named Entity Recognition, without further cleaning of the product name.
`name`	`string nullable` Example(s): `"VideoPoet"` Cleaned name of the product referenced in the News Event.
`release_type`	`string nullable` Example(s): `"major"` Product release type.
`release_version`	`string nullable` Example(s): `"v3.0"` Product release version.
`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` Example(s): `[ "mobile", "online_technology" ]` List of tags regarding the product.
Items	`string`
`recognition`	`string nullable` Example(s): `"50 Smartest Companies"` Name of the recognition the company received.
`vulnerability`	`string nullable` Example(s): `"account suspension errors"` Company issue.
`relationships`	`object` Contains references to related objects listed in property `included`.
`company1` (optional)	`object`
`data`	`object`
`id`	`string` Format: `uuid` ID of the included `CompanyLite` object.
`type`	`string` Constant: `"company"` Type of the included `CompanyLite` object.
`company2` (optional)	`object`
`data`	`object`
`id`	`string` Format: `uuid` ID of the included `CompanyLite` object.
`type`	`string` Constant: `"company"` Type of the included `CompanyLite` object.
`most_relevant_source`	`object`
`data`	`object`
`id`	`string` Format: `uuid` ID of the included `NewsArticleLite` object.
`type`	`string` Constant: `"news_article"` Type of the included `NewsArticleLite` object.
`included`	`array` Contains objects referenced in property `relationships`.
Items
One of	Discriminator property name: `"type"`
`CompanyLite`	`object`
`id`	`string` Format: `uuid` ID of the `CompanyLite` object.
`type`	`string` Constant: `"company"` Type of the `CompanyLite` object.
`attributes`	`object` Attributes of CompanyLite object.
`domain`	`string` Company domain name.
`ticker`	`string nullable` Company stock symbol (ticker) if available.
`company_name`	`string nullable` Company name if available.
`NewsArticleLite`	`object`
`id`	`string` Format: `uuid` ID of the `NewsArticleLite` object.
`type`	`string` Constant: `"news_article"` Type of the `NewsArticleLite` object.
`attributes`	`object` Attributes of NewsArticleLite object.
`url`	`string` Format: `uri` News Article source URL.
`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` Format: `date-time` ISO 8601 date when News Article was published.
`body`	`string` News Article text (article content).
`meta` (optional)	`object` Meta information about `NewsEventsDataset` object.
`record_state`	`string` Constant: `"active"` Represents record state of objects.
`count` (optional)	`integer` Total number of results. NOTE: Only shown in API responses. For performance reasons it will be omitted from response if `page` request parameter is not provided.
`message` (optional)	`string` Additional information about response. NOTE: Only shown in API responses.
`message_type` (optional)	`string` One of the values: `"accepted"` Type of the message. NOTE: Only shown in API responses.

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",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "summary": "Information about news related to companies",
 "description": "News Events are categorized events found in News Articles from various sources such as press releases, industry news, blogs and PR sites.\n \nAll events are provided in a structured format for all categories and always include company domain name to which the News Event belongs.\n \nBecause of the structured nature the News Events are better deduplicated and there is high signal to noise ratio.\n \nNews Event signals also include News Article title, body, author and more.\n",
 "type": "object",
 "required": [
 "data",
 "included"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "title": "NewsEvent",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes",
 "relationships"
 ],
 "properties": {
 "id": {
 "description": "ID of the `NewsEvent` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the `NewsEvent` object.",
 "const": "news_event"
 },
 "attributes": {
 "description": "Attributes of NewsEvent object.",
 "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.",
 "externalDocs": {
 "description": "Read more about categories...",
 "url": "/v3/guide/news_events_dataset/categories"
 },
 "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. 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"
 ],
 "minimum": 0,
 "maximum": 1
 },
 "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",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "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": "Relationship",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "ID of the included `CompanyLite` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included `CompanyLite` object.",
 "const": "company"
 }
 }
 }
 }
 },
 "company2": {
 "title": "Relationship",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "ID of the included `CompanyLite` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included `CompanyLite` object.",
 "const": "company"
 }
 }
 }
 }
 },
 "most_relevant_source": {
 "title": "Relationship",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "ID of the included `NewsArticleLite` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included `NewsArticleLite` object.",
 "const": "news_article"
 }
 }
 }
 }
 }
 }
 }
 }
 }
 },
 "included": {
 "description": "Contains objects referenced in property `relationships`.",
 "type": "array",
 "items": {
 "oneOf": [
 {
 "title": "CompanyLite",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "ID of the `CompanyLite` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the `CompanyLite` object.",
 "const": "company"
 },
 "attributes": {
 "description": "Attributes of CompanyLite object.",
 "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",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "ID of the `NewsArticleLite` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the `NewsArticleLite` object.",
 "const": "news_article"
 },
 "attributes": {
 "description": "Attributes of NewsArticleLite object.",
 "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 `NewsEventsDataset` object.\n",
 "type": "object",
 "required": [
 "record_state"
 ],
 "properties": {
 "record_state": {
 "description": "Represents record state of objects.\n",
 "type": "string",
 "const": "active"
 },
 "count": {
 "description": "Total number of results.\n \n**NOTE**: Only shown in API responses. For performance reasons it will be omitted from response\nif `page` request parameter is not provided.\n",
 "type": "integer",
 "example": 344
 },
 "message": {
 "description": "Additional information about response.\n \n**NOTE**: Only shown in API responses.\n",
 "type": "string"
 },
 "message_type": {
 "description": "Type of the message.\n \n**NOTE**: Only shown in API responses.\n",
 "enum": [
 "accepted"
 ]
 }
 },
 "example": {
 "record_state": "active",
 "count": 344,
 "message": "...",
 "message_type": "..."
 }
 }
 },
 "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"
        }
      }
    ]
  }
}

2.4.
News Events Deleted Dataset

Deleted News Events.

`NewsEventsDeletedDataset`	`object`
`data`	`array`
`NewsEventDeleted`	`object`
`id`	`string` Format: `uuid` ID of the `NewsEventDeleted` object.
`type`	`string` Constant: `"news_event"` Type of the `NewsEventDeleted` object.
`meta` (optional)	`object` Meta information about `NewsEventsDeletedDataset` object.
`record_state`	`string` Constant: `"deleted"` Represents record state of objects.
`count` (optional)	`integer` Total number of results. NOTE: Only shown in API responses. For performance reasons it will be omitted from response if `page` request parameter is not provided.
`message` (optional)	`string` Additional information about response. NOTE: Only shown in API responses.
`message_type` (optional)	`string` One of the values: `"accepted"` Type of the message. NOTE: Only shown in API responses.

NewsEventsDeletedDataset object

{
  "data": [
    {
      "id": "ffffb10a-bdd7-4bc7-bb9e-445b8b97df9c",
      "type": "news_event",
      "attributes": {
        "summary": "Archipelago acquired New York Stock Exchange Group on Jan 1st '17.",
        "category": "acquires",
        "found_at": "2018-12-02T22:00:00Z",
        "confidence": 0.7135,
        "article_sentence": "Fifteen years after Archipelago acquired the New York Stock Exchange, in 2017, two thirds of early stage venture funding rounds went to fintech startups.",
        "human_approved": false,
        "planning": false,
        "amount": null,
        "amount_normalized": null,
        "assets": null,
        "assets_tags": [

],
        "award": null,
        "contact": null,
        "conference": null,
        "division": null,
        "effective_date": "2017-01-01",
        "financing_type": null,
        "financing_type_normalized": null,
        "financing_type_tags": [

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

],
        "location": null,
        "location_data": [

],
        "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": "64e2abd2-ed6e-56a5-ad2f-f9dc9ce5e9cd",
            "type": "company"
          }
        },
        "company2": {
          "data": {
            "id": "558ed1f5-8e32-5a27-b77d-19e96a220314",
            "type": "company"
          }
        },
        "most_relevant_source": {
          "data": {
            "id": "15fc3310-b25b-430e-a972-86708440ccc8",
            "type": "news_article"
          }
        }
      }
    }
  ],
  "included": [
    {
      "id": "64e2abd2-ed6e-56a5-ad2f-f9dc9ce5e9cd",
      "type": "company",
      "attributes": {
        "domain": "archipelago.com",
        "company_name": "Archipelago",
        "ticker": null
      }
    },
    {
      "id": "558ed1f5-8e32-5a27-b77d-19e96a220314",
      "type": "company",
      "attributes": {
        "domain": "nyse.com",
        "company_name": "New York Stock Exchange Group",
        "ticker": null
      }
    },
    {
      "id": "15fc3310-b25b-430e-a972-86708440ccc8",
      "type": "news_article",
      "attributes": {
        "author": null,
        "body": "For many years, as the technology advisor to Mayor Daley of the City of Chicago, and the co-founder and CEO of what is now 1871, Freshwater Advisors learned how to interpret and use proactive data points to gain new insight into the relationships that exist between the Davids and the Goliaths of the finance sector. Freshwater Advisors were beginning to see startup fintech companies that were disrupting centuries-old financial institutions, and Freshwater Advisors joined a chorus of voices, encouraging and literally building common ground for collaboration. One of my all time favorite examples of this phenomenon was Archipelago’s merger and acquisition of the New York Stock Exchange in 2002. Archipelago, a Chicago-born trading platform for the efficient flows of equities, was created by former floor trader Gerald Putnam. 12 short years years after launching, it merged, acquired and then built the operating technology platform for the 150 year old New York Stock Exchange. Fifteen years after Archipelago acquired the New York Stock Exchange, in 2017, two thirds of early stage venture funding rounds went to fintech startups. Today, names like PayPal, Braintree, Prosper and Lending Tree are household names.",
        "image_url": null,
        "url": "https://freshwateradvisors.com/2018/12/03/we-were-watching-change-wash-across-the-financial-sector/",
        "published_at": "2018-12-02T22:00:00Z",
        "title": "We were watching change wash across the financial sector… – Freshwater Advisors"
      }
    }
  ]
}

{
 "title": "NewsEventsDeletedDataset",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "description": "Deleted News Events.\n",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "title": "NewsEventDeleted",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "ID of the `NewsEventDeleted` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the `NewsEventDeleted` object.",
 "const": "news_event"
 }
 }
 }
 },
 "meta": {
 "description": "Meta information about `NewsEventsDeletedDataset` object.\n",
 "type": "object",
 "required": [
 "record_state"
 ],
 "properties": {
 "record_state": {
 "description": "Represents record state of objects.\n",
 "type": "string",
 "const": "deleted"
 },
 "count": {
 "description": "Total number of results.\n \n**NOTE**: Only shown in API responses. For performance reasons it will be omitted from response\nif `page` request parameter is not provided.\n",
 "type": "integer",
 "example": 344
 },
 "message": {
 "description": "Additional information about response.\n \n**NOTE**: Only shown in API responses.\n",
 "type": "string"
 },
 "message_type": {
 "description": "Type of the message.\n \n**NOTE**: Only shown in API responses.\n",
 "enum": [
 "accepted"
 ]
 }
 },
 "example": {
 "record_state": "deleted",
 "count": 344,
 "message": "...",
 "message_type": "..."
 }
 }
 },
 "example": {
 "data": [
 {
 "id": "ffffb10a-bdd7-4bc7-bb9e-445b8b97df9c",
 "type": "news_event",
 "attributes": {
 "summary": "Archipelago acquired New York Stock Exchange Group on Jan 1st '17.",
 "category": "acquires",
 "found_at": "2018-12-02T22:00:00Z",
 "confidence": 0.7135,
 "article_sentence": "Fifteen years after Archipelago acquired the New York Stock Exchange, in 2017, two thirds of early stage venture funding rounds went to fintech startups.",
 "human_approved": false,
 "planning": false,
 "amount": null,
 "amount_normalized": null,
 "assets": null,
 "assets_tags": [

],
          "award": null,
          "contact": null,
          "conference": null,
          "division": null,
          "effective_date": "2017-01-01",
          "financing_type": null,
          "financing_type_normalized": null,
          "financing_type_tags": [

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

],
          "location": null,
          "location_data": [

],
          "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": "64e2abd2-ed6e-56a5-ad2f-f9dc9ce5e9cd",
              "type": "company"
            }
          },
          "company2": {
            "data": {
              "id": "558ed1f5-8e32-5a27-b77d-19e96a220314",
              "type": "company"
            }
          },
          "most_relevant_source": {
            "data": {
              "id": "15fc3310-b25b-430e-a972-86708440ccc8",
              "type": "news_article"
            }
          }
        }
      }
    ],
    "included": [
      {
        "id": "64e2abd2-ed6e-56a5-ad2f-f9dc9ce5e9cd",
        "type": "company",
        "attributes": {
          "domain": "archipelago.com",
          "company_name": "Archipelago",
          "ticker": null
        }
      },
      {
        "id": "558ed1f5-8e32-5a27-b77d-19e96a220314",
        "type": "company",
        "attributes": {
          "domain": "nyse.com",
          "company_name": "New York Stock Exchange Group",
          "ticker": null
        }
      },
      {
        "id": "15fc3310-b25b-430e-a972-86708440ccc8",
        "type": "news_article",
        "attributes": {
          "author": null,
          "body": "For many years, as the technology advisor to Mayor Daley of the City of Chicago, and the co-founder and CEO of what is now 1871, Freshwater Advisors learned how to interpret and use proactive data points to gain new insight into the relationships that exist between the Davids and the Goliaths of the finance sector. Freshwater Advisors were beginning to see startup fintech companies that were disrupting centuries-old financial institutions, and Freshwater Advisors joined a chorus of voices, encouraging and literally building common ground for collaboration. One of my all time favorite examples of this phenomenon was Archipelago’s merger and acquisition of the New York Stock Exchange in 2002. Archipelago, a Chicago-born trading platform for the efficient flows of equities, was created by former floor trader Gerald Putnam. 12 short years years after launching, it merged, acquired and then built the operating technology platform for the 150 year old New York Stock Exchange. Fifteen years after Archipelago acquired the New York Stock Exchange, in 2017, two thirds of early stage venture funding rounds went to fintech startups. Today, names like PayPal, Braintree, Prosper and Lending Tree are household names.",
          "image_url": null,
          "url": "https://freshwateradvisors.com/2018/12/03/we-were-watching-change-wash-across-the-financial-sector/",
          "published_at": "2018-12-02T22:00:00Z",
          "title": "We were watching change wash across the financial sector… – Freshwater Advisors"
        }
      }
    ]
  }
}

2.5.
Financing Events Dataset

`FinancingEventsDataset`	`object`
`data`	`array`
`FinancingEvent`	`object`
`id`	`string` Format: `uuid` ID of the `FinancingEvent` object.
`type`	`string` Constant: `"financing_event"` Type of the `FinancingEvent` object.
`attributes`	`object` Attributes of FinancingEvent object.
`effective_date`	`string nullable` Format: `date-time` ISO 8601 date when the event has happened.
`found_at`	`string` Format: `date-time` ISO 8601 date when the event was discovered and created.
`categories`	`array \| 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" Example(s): `[ "private_equity", "series_e", "venture" ]` A financing event category.
`financing_type`	`string nullable` Example(s): `"Series E funding"` A financing type this event is mentioning.
`financing_type_normalized`	`string nullable` 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"` A normalized value of "financing_type" property, where possible.
`amount`	`string nullable` Example(s): `"$555.5 million"` This is a string of a value as extracted from text.
`amount_normalized`	`integer nullable` Example(s): `555500000` Integer value of "amount" property.
`source_urls`	`array` Urls to news articles.
Items	`string` Format: `uri` Example(s): `[ "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` Format: `uuid` ID of the included `CompanyLite` object.
`type`	`string` Constant: `"company"` Type of the included `CompanyLite` object.
`investors`	`object`
`data`	`array`
Items	`object`
`id`	`string` Format: `uuid` ID of the included `CompanyLite` object.
`type`	`string` Constant: `"company"` Type of the included `CompanyLite` object.
`included`	`array` Contains objects referenced in property `relationships`.
`CompanyLite`	`object`
`id`	`string` Format: `uuid` ID of the `CompanyLite` object.
`type`	`string` Constant: `"company"` Type of the `CompanyLite` object.
`attributes`	`object` Attributes of CompanyLite 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 `FinancingEventsDataset` object.
`record_state`	`string` Constant: `"active"` Represents record state of objects.
`count` (optional)	`integer` Total number of results. NOTE: Only shown in API responses. For performance reasons it will be omitted from response if `page` request parameter is not provided.
`message` (optional)	`string` Additional information about response. NOTE: Only shown in API responses.
`message_type` (optional)	`string` One of the values: `"accepted"` Type of the message. NOTE: Only shown in API responses.

FinancingEventsDataset object

{
 "title": "FinancingEventsDataset",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "summary": "Information about investments in companies",
 "description": "Financing Events are events extracted from News Events data.\n \nThese include e.g. Venture capital rounds, Private Equity investments etc.\n \nFinancing events are grouped by date of investment and include investment amount, names of investors and\nmore.\n",
 "type": "object",
 "required": [
 "data",
 "included"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "title": "FinancingEvent",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes",
 "relationships"
 ],
 "properties": {
 "id": {
 "description": "ID of the `FinancingEvent` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the `FinancingEvent` object.",
 "const": "financing_event"
 },
 "attributes": {
 "description": "Attributes of FinancingEvent object.",
 "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": {
 "description": "A financing event category.",
 "examples": [
 [
 "private_equity",
 "series_e",
 "venture"
 ]
 ],
 "type": "array",
 "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"
 ]
 },
 "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": [
 "$555.5 million"
 ],
 "type": [
 "string",
 "null"
 ]
 },
 "amount_normalized": {
 "description": "Integer value of \"amount\" property.",
 "examples": [
 555500000
 ],
 "type": [
 "integer",
 "null"
 ]
 },
 "source_urls": {
 "description": "Urls to news articles.",
 "type": "array",
 "items": {
 "examples": [
 [
 "https://www.chinamoneynetwork.com/2018/10/10/yunfeng-advantech-lead-300m-round-in-chinas-airbnb-copycat-xiaozhu-com"
 ]
 ],
 "type": "string",
 "format": "uri"
 }
 }
 }
 },
 "relationships": {
 "description": "Contains references to related objects listed in property `included`.",
 "type": "object",
 "required": [
 "company",
 "investors"
 ],
 "properties": {
 "company": {
 "title": "Relationship",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "ID of the included `CompanyLite` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included `CompanyLite` object.",
 "const": "company"
 }
 }
 }
 }
 },
 "investors": {
 "title": "Relationship",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "data"
 ],
 "properties": {
 "data": {
 "type": "array",
 "items": {
 "type": "object",
 "required": [
 "id",
 "type"
 ],
 "properties": {
 "id": {
 "description": "ID of the included `CompanyLite` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the included `CompanyLite` object.",
 "const": "company"
 }
 }
 }
 }
 }
 }
 }
 }
 }
 }
 },
 "included": {
 "description": "Contains objects referenced in property `relationships`.",
 "type": "array",
 "items": {
 "title": "CompanyLite",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "type": "object",
 "required": [
 "id",
 "type",
 "attributes"
 ],
 "properties": {
 "id": {
 "description": "ID of the `CompanyLite` object.",
 "type": "string",
 "format": "uuid"
 },
 "type": {
 "description": "Type of the `CompanyLite` object.",
 "const": "company"
 },
 "attributes": {
 "description": "Attributes of CompanyLite object.",
 "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 `FinancingEventsDataset` object.\n",
 "type": "object",
 "required": [
 "record_state"
 ],
 "properties": {
 "record_state": {
 "description": "Represents record state of objects.\n",
 "type": "string",
 "const": "active"
 },
 "count": {
 "description": "Total number of results.\n \n**NOTE**: Only shown in API responses. For performance reasons it will be omitted from response\nif `page` request parameter is not provided.\n",
 "type": "integer",
 "example": 344
 },
 "message": {
 "description": "Additional information about response.\n \n**NOTE**: Only shown in API responses.\n",
 "type": "string"
 },
 "message_type": {
 "description": "Type of the message.\n \n**NOTE**: Only shown in API responses.\n",
 "enum": [
 "accepted"
 ]
 }
 },
 "example": {
 "record_state": "active",
 "count": 344,
 "message": "...",
 "message_type": "..."
 }
 }
 },
 "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
 }
 }
 ]
 }
}

2.6.
Connections Dataset

Company Connections describe relationships between companies.

It is sourced from Case Study pages, Testimonials pages, Logo images and other resources.

`ConnectionsDataset`	`object`
`data`	`array`
`Connection`	`object`
`id`	`string` Format: `uuid` ID of the `Connection` object.
`type`	`string` Constant: `"connection"` Type of the `Connection` object.
`attributes`	`object` Attributes of Connection object.
`category`	`string` Connection category.
One of
	`string` Constant: `"partner"` Read as: Company1 is a partner of Company2. A partner relationship signals a collaboration between companies that goes both in two directions. You will find such connections using titles as "We work with", "Our partners", "The company we keep", and other titles similar in meaning. The implications of this category can be quite wide in their meaning, but in general all partnerships are a positive signal. NOTE: Until February 2024 the `partner` category did not always signal a true partnership between two companies, but it also included a tighter knit relationship compared to the `other` category, which are not always as strong as the partner keyword suggests. The meaning is now correctly followed for all new cases or cases that have been last seen since and still have the `partner` category.
	`string` Constant: `"vendor"` Read as: Company1 is a vendor to Company2. The vendor category is much simpler in its meaning. It simply means that company1 is a supplier for company2 in some way. All vendor relationships are positive, although some are more positive than others. On a webpage, such connections can be seen in lists such as "Our customers", "Trusted by", "Enabling businesses to", etc. For example, to be a vendor to Microsoft, the company has to pass certain requirements as to company size, product reliability, safety, etc. This can be seen as a vetting process of some sort and increases the company's trustworthiness. The vendor category can also be used to determine the supply chain of a certain company and evaluate supply chain risks.
	`string` Constant: `"integration"` Read as: Company1 has an integration with Company2. Most of the time an integration happens between a platform and a service. Case examples: Company1 has developed a Machine learning model. To make it more usable, they have developed an Azure Cloud integration. With this, the customers of the model can start using it more quickly, which is good for Company1. It's generally the service's development team that takes on the task of creating this integration. This can also be a positive signal for the platform (Company2). One could calculate the adoption of the Company2 platform and use this as a signal for growth.
	`string` Constant: `"investor"` Read as: Company1 is an investor in Company2. The investor category identifies relationships between companies making investments (Company1) and companies receiving investments (Company2). The more investors the company has, the better. One could also track what companies/sectors competitors are investing in.
	`string` Constant: `"parent"` Read as: Company1 is a parent company of Company2. Parent category defines a parent-subsidiary relationship between websites. In some cases such websites are only localized versions for specific markets. Case examples: "activisionblizzard.com" is a parent to "blizzard.com", and in turn "blizzard.com" is a parent to "blizzard.de". "coca-cola.com" is a parent to a vast array of websites, such as "coca-cola.fr", "coca-cola.de", "coca-cola.es", "coca-cola.tn", "coca-cola.se", etc. NOTE: Often it makes sense to not only check current hierarchy level e.g. "blizzard.de" but also check their parent connections e.g. "blizzard.com" and "activisionblizzard.com". Since sometimes company connections (customers, investors…) are also available at higher company hierarchical levels.
	`string` Constant: `"rebranding"` Read as: Company1 is a rebranding of Company2. Company1 being a rebranding of Company2 suggests that Company2 has undergone a process to change its brand identity. We detect rebranding based on domain change and similarities between the old and the new website. Case examples: Top-level-domain (TLD) change: airmeet.co -> airmeet.com. airmeet.com is a rebranding of airmeet.co. TLD changed to higher value .com. Change to a shorter domain: lamborghini.com is a rebranding of lamborghini-madrid.com. Name change: hausarztpraxis-leinetal.de is a rebranding of praxis-schuenemann.de.
	`string` Constant: `"published_in"` Read as: Company1 is published in Company2. The `published_in` category is usually found on early stage startup websites with fewer accomplishments on the market, but positive press coverage. You can see such connections under titles such as "As seen in", "Featured in", "Talking about us", etc. A `published_in` connection is a positive signal, but after some months or years, we would expect to see them removed and replaced with other content proving the legitimacy of the company.
	`string` Constant: `"other"` Read as: Company1 is connected to Company2. This is the most general category. If we detected a relationship between companies, but we were unsure which category it belonged to, or it didn't belong to any of our categories, then we would categorize it as `other`. Outgoing `other` category connections are a slightly positive signal, while incoming ones are quite more so. Thus a connection Company1 -> other -> Company2 is more positive for Company2, than for Company1. As Company2 was featured on Company1 website. Sometimes the `other` category can have additional information via the source_category attribute depending on where it was found. An example of such a source_category would be `cookie_section`, which is explained further below. Such connections could be evaluated differently.
`source_category`	`string` 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" Describes source where the category was found. Read more about source categories...
`source_url`	`string nullable` Format: `uri` If the connection was found on a subpage, source_url points to that subpage.
`individual_source_url`	`string nullable` Format: `uri` Some connections contain additional information on their individual subpages. individual_source_url points to that subpage.
`context`	`string nullable` Text found near the connection. Often contains additional relationship information between the two companies.
`first_seen_at`	`string` Format: `date-time` Time & date (ISO 8601) of when the connection was first seen.
`last_seen_at`	`string` Format: `date-time` Time & date (ISO 8601) of when the connection was last seen.
`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: Company1 is a `vendor` to Company2.
`company1`	`object`
`data`	`object`
`id`	`string` Format: `uuid` ID of the included `CompanyLite` object.
`type`	`string` Constant: `"company"` Type of the included `CompanyLite` object.
`company2`	`object`
`data`	`object`
`id`	`string` Format: `uuid` ID of the included `CompanyLite` object.
`type`	`string` Constant: `"company"` Type of the included `CompanyLite` object.
`included`	`array` Contains objects referenced in property `relationships`.
`CompanyLite`	`object`
`id`	`string` Format: `uuid` ID of the `CompanyLite` object.
`type`	`string` Constant: `"company"` Type of the `CompanyLite` object.
`attributes`	`object` Attributes of CompanyLite 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 `ConnectionsDataset` object.
`record_state`	`string` Constant: `"active"` Represents record state of objects.
`count` (optional)	`integer` Total number of results. NOTE: Only shown in API responses. For performance reasons it will be omitted from response if `page` request parameter is not provided.
`message` (optional)	`string` Additional information about response. NOTE: Only shown in API responses.
`message_type` (optional)	`string` One of the values: `"accepted"`

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1.
Guide

1.1.
Introduction

1.2.
Use cases

1.3.
Companies Dataset

1.3.1.
Introduction

1.4.
Job Openings Dataset

1.4.1.
Introduction

1.5.
News Events Dataset

1.5.1.
Introduction

1.5.1.1.
Use cases

1.5.2.
Categories

1.6.
Financing Events Dataset

1.6.1.
Introduction

1.7.
Connections Dataset

1.7.1.
Introduction

1.7.2.
Use cases

1.7.3.
Categories

1.7.4.
Source Categories

1.8.
Technologies Dataset

1.8.1.
Introduction

1.9.
Technology Detections Dataset

1.9.1.
Introduction

1.10.
Website Evolution Dataset

1.10.1.
Introduction

1.11.
Github Repositories Dataset

1.11.1.
Introduction

2.
Data Models

2.1.
Companies Dataset

2.2.
Job Openings Dataset

2.3.
News Events Dataset

2.4.
News Events Deleted Dataset

2.5.
Financing Events Dataset

2.6.
Connections Dataset