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1.
Guide

1.1.
Getting Started

1.1.1.
Introduction

PredictLeads provides company intelligence data on 75M+ companies globally.

All PredictLeads data is proprietary and sourced directly from company websites. All datasets are structured and point-in-time.

Please check the Guide section first for more detailed info on relevant datasets.

1.1.2.
Data Delivery

The data can be delivered via Flat Files, APIs or Webhooks.
When using:

  • Flat Files
    Please review the Data Model section of relevant dataset. PredictLeads team can provide you sample files in JSONL (JSON Lines) format for each dataset.

  • APIs
    Please review the API Endpoints section.

  • Webhooks
    Please review the API Webhooks section.

1.1.3.
Quick overview of the datasets

  • Job Openings Dataset
    Information about Job Openings sourced directly from company websites for highest accuracy and freshness.


  • Technology Detections Dataset
    Information about approximately 800 million detections of companies that use various technologies.


  • Technologies Dataset
    Information about approximately 30,000 technologies we track.


  • News Events Dataset
    Structured and categorized News Events from some 18 million different blogs, PR sites and News sites.


  • Financing Events Dataset
    Financing data on companies, most often this would include startup funding rounds. Extracted from News Events data.


  • Connections Dataset
    Categorized Connections between companies and where available also summarized context of how two companies work together.


  • Website Evolution Dataset
    Information on how websites are structured, what kind of subpages they have, categories of subpages (e.g. blog, careers, about, terms...) and cleaned text content from these subpages.


  • Github Repositories Dataset
    Information about public GitHub repositories of companies.


  • Startup Platform Posts Dataset
    Information about posts on popular startup platforms.

1.1.4.
Use cases

  • Sales Enablement platforms use PredictLeads data for account enrichment, scoring and personalization.
  • Market Intelligence platforms use PredictLeads data for segmentation, competitive analysis and to understand market trends.
  • Investment companies use PredictLeads data to monitor portfolio companies, spot net-new investment opportunities and to help with investment decision making.

1.2.
Companies Dataset

1.2.1.
Introduction

Companies Dataset includes data such as Company Name, Meta Title, Meta Description, Structured Location Data, Ticker, Parent Company, Language and other basic company information.

1.2.2.
Data Model

CompaniesDataset
object
data
array
Company
object

The Company object.
id
string
Format:
uuid

ID of the CompanyLite object.
type
string
Constant:
"company"

Type of the CompanyLite object.
attributes
object

Attributes of the CompanyLite object.
domain
string

Company domain name.
company_name
string nullable

Company name if available.
ticker
string nullable

Company stock symbol (ticker) if available.
friendly_company_name
string

Company friendly name.
meta_title
string

Company website's meta title.
meta_description
string

Company website's meta title.
language
string

Company website's language.
location
string

Company location.
location_data
array
LocationData
object

The 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
relationships
object

Contains references to the Company-related objects listed in the included property.
parent_company
[optional]
object

Contains a reference to the Company-related CompanyLite object listed in the included property.
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

Contains a reference to the Company-related CompanyLite object listed in the included property.
data
object
id
string
Format:
uuid

ID of the included CompanyLite object.
type
string
Constant:
"company"

Type of the included CompanyLite object.
meta
object

Meta information about the redirect.
reason
string nullable
One of (3 total):
"acquisition_merger", "locality", "rebranding"

Reason of the redirect.
included
array

Contains objects referenced in property relationships.
CompanyLite
object

The CompanyLite object.
id
string
Format:
uuid

ID of the CompanyLite object.
type
string
Constant:
"company"

Type of the CompanyLite object.
attributes
object

Attributes of the CompanyLite object.
domain
string

Company domain name.
company_name
string nullable

Company name if available.
ticker
string nullable

Company stock symbol (ticker) if available.
meta
[optional]
object

Meta information about CompaniesDataset object.
schema_version
string

Represents schema version of objects.
record_state
string
Constant:
"active"

Represents record state of objects.
count
[optional]
integer
Example:
344

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.
CompaniesDataset object

1.2.3.
API Endpoints

1.3.
Job Openings Dataset

1.3.1.
Introduction

PredictLeads has historical jobs data since 2018 that includes over 190 million records, and is available for 1.6 million companies, which includes an average of 7 million active jobs at any given time.

Job Openings are sourced directly from company websites which includes their career subpages, and ATS integrations. All Jobs are categorized using industry-standard O*NET codes.

The Job Openings Dataset includes fields such as Job Opening Title, Job Opening URL, First Seen At, Last Seen At, Location, Category, Seniority, Description, Salary, Contract Type, O*NET Job Category Codes and other Job Opening information.

1.3.2.
Use cases

  • Sales Enablement platforms leverage Job Openings data to extend their lead search criteria.
  • Market Intelligence platforms use Job Openings data to track hiring trends.
  • Investment Companies keep track of headcount changes and leverage job descriptions to identify similar companies.

1.3.3.
Data Model

JobOpeningsDataset
object
data
array
JobOpening
object

The 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 the JobOpening object.
title
string

Job title.
description
string nullable

JobOpening description.
url
string
Format:
uri

URL of the web page where the data was found.
first_seen_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) of when the JobOpening was first seen.
last_seen_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) of when the JobOpening was last seen.
last_processed_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) of when the JobOpening was last sent to processing.
contract_types
array
Example:
[ "full time", "remote" ]

Zero or more types describing the JobOpening.
categories
array
Example:
[ "finance", "data_analysis" ]

Zero or more organizational categories of the JobOpening.
Items
string
One of (27 total):
"administration", "consulting", "data_analysis", "design", "directors", "education", "engineering", "finance", "healthcare_services", "human_resources", "information_technology", "internship", "legal", "management", "marketing", "military_and_protective_services", "operations", "purchasing", "product_management", "quality_assurance", "real_estate", "research", "sales", "software_development", "support", "manual_work", "food"
onet_data
object

Inferred O*NET data for this JobOpening. O*NET is a source and standardization of occupational information. More info on the O*NET data: onetonline.org.
code
string nullable
Example:
"29-1021.00"

O*NET category code representing this JobOpening.
family
string nullable
Example:
"Healthcare Practitioners and Technical"

O*NET family name matching the code for this JobOpening.
occupation_name
string nullable
Example:
"Dentists, General"

O*NET occupation name matching the code for this JobOpening.
posted_at
string nullable
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) of when the JobOpening was posted.
salary
string nullable
Example:
"£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 (6 total):
"hour", "day", "week", "month", "year", null

Salary time unit.
seniority
string nullable
One of (10 total):
"owner", "founder", "c_level", "partner", "president", "vice_president", "head", "director", "manager", "non_manager"

Seniority inferred from the JobOpening title.
status
string nullable
One of (2 total):
"closed", null

null or "closed" if keyword "job no longer available", "vacancy listing expired", "position filled", etc. is found on JobOpening site.
language
string nullable

Language in which a JobOpening description is written.
location
string nullable

Raw location string.
location_data
array
LocationData
object

The 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 JobOpening description.
relationships
object

Contains references to the JobOpening-related objects listed in the included property.
company
object

Contains a reference to the JobOpening-related CompanyLite object listed in the included property.
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

The CompanyLite object.
id
string
Format:
uuid

ID of the CompanyLite object.
type
string
Constant:
"company"

Type of the CompanyLite object.
attributes
object

Attributes of the CompanyLite object.
domain
string

Company domain name.
company_name
string nullable

Company name if available.
ticker
string nullable

Company stock symbol (ticker) if available.
meta
[optional]
object

Meta information about JobOpeningsDataset object.
schema_version
string

Represents schema version of objects.
record_state
string
Constant:
"active"

Represents record state of objects.
count
[optional]
integer
Example:
344

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.
JobOpeningsDataset object

1.3.4.
API Endpoints

1.3.5.
API Webhooks

1.4.
Job Openings Deleted Dataset

1.4.1.
Introduction

Deleted Job Openings.

1.4.2.
Data Model

JobOpeningsDeletedDataset
object
data
array
JobOpeningDeleted
object

The JobOpeningDeleted object.
id
string
Format:
uuid

ID of the JobOpeningDeleted object.
type
string
Constant:
"job_opening"

Type of the JobOpeningDeleted object.
meta
[optional]
object

Meta information about JobOpeningsDeletedDataset object.
schema_version
string

Represents schema version of objects.
record_state
string
Constant:
"deleted"

Represents record state of objects.
count
[optional]
integer
Example:
344

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.
JobOpeningsDeletedDataset object

1.4.3.
API Webhooks

1.5.
Technology Detections Dataset

1.5.1.
Introduction

The Technology Detections Dataset provides insights into the tech stack of a given company.

NOTE: If you're searching for general information about technologies please check the Technologies Dataset section.

Since 2018, PredictLeads has detected approximately 800 million technology adoptions for about 50 million companies.

The Technology Detection Dataset includes fields such as Technology Name, Technology ID, Ticker, First Seen At, Last Seen At, Seen on Subpages Count and other Technology Detection information.

1.5.2.
Use cases

  • Sales Enablement platforms leverage Technology Detections to identify a potential fit, to spot integration opportunities and to extend their lead search criteria.
  • Market Intelligence platforms use Technology Detections data to better understand technology adoption trends.
  • Investment Companies use Technology Detections to keep track of tech market shares and to spot which Technologies are getting adopted by e.g. Inc. 5000 companies.

1.5.3.
Data Model

TechnologyDetectionsDataset
object
data
array
TechnologyDetection
object

The TechnologyDetection object.
id
string
Format:
uuid

ID of the TechnologyDetection object.
type
string
Constant:
"technology_detection"

Type of the TechnologyDetection object.
attributes
object

Attributes of the TechnologyDetection object.
first_seen_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) of when the TechnologyDetection was first seen.
last_seen_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) of when the TechnologyDetection was last seen.
behind_firewall
boolean

When true Technology was found only in JobOpening description attribute. When false it was found via the company website JS or HTML tag.
score
number
relationships
object

Contains references to the TechnologyDetection-related objects listed in the included property.
company
object

Contains a reference to the TechnologyDetection-related CompanyLite object listed in the included property.
data
object
id
string
Format:
uuid

ID of the included CompanyLite object.
type
string
Constant:
"company"

Type of the included CompanyLite object.
seen_on_job_openings
object

Contains a reference to the TechnologyDetection-related JobOpeningLite objects listed in the included property.
data
array
Items
object
id
string
Format:
uuid

ID of the included JobOpeningLite object.
type
string
Constant:
"job_opening"

Type of the included JobOpeningLite object.
seen_on_subpages
object

Contains a reference to the TechnologyDetection-related DetectionOnSubpage objects listed in the included property.
data
array
Items
object
id
string
Format:
uuid

ID of the included DetectionOnSubpage object.
type
string
Constant:
"detection_on_subpage"

Type of the included DetectionOnSubpage object.
technology
object

Contains a reference to the TechnologyDetection-related TechnologyLite object listed in the included property.
data
object
id
string
Format:
uuid

ID of the included TechnologyLite object.
type
string
Constant:
"technology"

Type of the included TechnologyLite object.
included
array

Contains objects referenced in property relationships.
Items
One of (4 total)
Discriminator property name:
"type"
CompanyLite
object

The CompanyLite object.
id
string
Format:
uuid

ID of the CompanyLite object.
type
string
Constant:
"company"

Type of the CompanyLite object.
attributes
object

Attributes of the CompanyLite object.
domain
string

Company domain name.
company_name
string nullable

Company name if available.
ticker
string nullable

Company stock symbol (ticker) if available.
TechnologyLite
object

The TechnologyLite object.
id
string
Format:
uuid

ID of the TechnologyLite object.
type
string
Constant:
"technology"

Type of the TechnologyLite object.
attributes
object

Attributes of the TechnologyLite object.
name
string
DetectionOnSubpage
object

The DetectionOnSubpage object.
id
string
Format:
uuid

ID of the DetectionOnSubpage object.
type
string
Constant:
"detection_on_subpage"

Type of the DetectionOnSubpage object.
attributes
object

Attributes of the DetectionOnSubpage object.
first_seen_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"
last_seen_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"
subpage_url
string
Format:
uri
JobOpeningLite
object

The JobOpeningLite object.
id
string
Format:
uuid

ID of the JobOpeningLite object.
type
string
Constant:
"job_opening"

Type of the JobOpeningLite object.
attributes
object

Attributes of the JobOpeningLite object.
first_seen_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) of when the JobOpeningLite was first seen.
last_seen_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) of when the JobOpeningLite was last seen.
url
string
Format:
uri

URL of the web page where the data was found.
meta
[optional]
object

Meta information about TechnologyDetectionsDataset object.
schema_version
string

Represents schema version of objects.
record_state
string
Constant:
"active"

Represents record state of objects.
count
[optional]
integer
Example:
344

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.
TechnologyDetectionsDataset object

1.5.4.
API Endpoints

1.5.5.
API Webhooks

1.6.
Technologies Dataset

1.6.1.
Introduction

Technologies dataset consists of some 30,000 technologies we track.

NOTE: If you're searching for technologies a given company is using please check the Technology Detections Dataset section.

Technologies are collected from script tags, IP ranges, cookies…, and also from job descriptions where companies mention them as required skill sets. Subpages checked for technology data are of the following categories: main, login, support, trust, retail, blog, news, press and news outlet.

The Technologies dataset includes fields such as Technology Name, Technology ID, Description, Category, Pricing Data and other Technology information.

1.6.2.
Data Model

TechnologiesDataset
object
data
array
Technology
object

The Technology object.
id
string
Format:
uuid

ID of the TechnologyLite object.
type
string
Constant:
"technology"

Type of the TechnologyLite object.
attributes
object

Attributes of the TechnologyLite object.
name
string
description
string nullable
categories
array
Items
string
parent_categories
array
Items
string
domain
string nullable
url
string nullable
Format:
uri
pricing_data
object
min_usd
number nullable
max_usd
number nullable
average_spend
number nullable

The average technology spend price is derived from estimating company revenue and number of paid users.
interval
string nullable
tags
array
Items
string
created_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"
relationships
object

Contains references to the Technology-related objects listed in the included property.
company
object

Contains a reference to the Technology-related CompanyLite object listed in the included property.
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

The CompanyLite object.
id
string
Format:
uuid

ID of the CompanyLite object.
type
string
Constant:
"company"

Type of the CompanyLite object.
attributes
object

Attributes of the CompanyLite object.
domain
string

Company domain name.
company_name
string nullable

Company name if available.
ticker
string nullable

Company stock symbol (ticker) if available.
meta
[optional]
object

Meta information about TechnologiesDataset object.
schema_version
string

Represents schema version of objects.
record_state
string
Constant:
"active"

Represents record state of objects.
count
[optional]
integer
Example:
344

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.
TechnologiesDataset object

1.6.3.
API Endpoints

1.7.
News Events Dataset

1.7.1.
Introduction

Since 2016, PredictLeads has detected over 7 million relevant news signals, which are available for 2 million companies globally.

The News Events are sourced from some 20 million blogs, news and PR sites and distilled into relevant categories by machine learning algorithms.

The News Events dataset includes fields such as Formatted Signal, Signal Category, Most Relevant Source URL, Article Sentence, Article Body, Article Author, Found At, Effective Date and other News Event information.

1.7.2.
Categories

Property in schema: category

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

1.7.3.
Use cases

  • Sales Enablement platforms leverage News Events categories to identify relevant prospects and personalize their outreach.
  • Market Intelligence platforms use News Events data to score companies based on relevant news signals.
  • Investment Companies keep track of company activity and score potential investment opportunities.

1.7.4.
Data Model

NewsEventsDataset
object
data
array
NewsEvent
object

The 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 the NewsEvent object.
summary
string

A short, human readable excerpt of this NewsEvent data.
category
string
Example:
"invests_into"

Name of the category this NewsEvent represents.
One of (29 total)
string
Constant:
"acquires"

Group name: acquisition
Company acquired another company.
string
Constant:
"merges_with"

Group name: acquisition
Company merges with another company.
string
Constant:
"sells_assets_to"

Group name: acquisition
Company sells assets (like properties or warehouses) to other company.
string
Constant:
"signs_new_client"

Group name: contract
Company signs new client.
string
Constant:
"files_suit_against"

Group name: corporate_challenges
Company files suit against other company.
string
Constant:
"has_issues_with"

Group name: corporate_challenges
Company has vulnerability problems.
string
Constant:
"closes_offices_in"

Group name: cost_cutting
Company closes existing offices.
string
Constant:
"decreases_headcount_by"

Group name: cost_cutting
Company lays off employees.
string
Constant:
"attends_event"

Group name: expansion
Company attends an event.
string
Constant:
"expands_facilities"

Group name: expansion
Company opens new or expands existing facilities like warehouses, data centers, manufacturing plants etc.
string
Constant:
"expands_offices_in"

Group name: expansion
Company expands existing offices.
string
Constant:
"expands_offices_to"

Group name: expansion
Company opens new offices in another town, state, country or continent.
string
Constant:
"increases_headcount_by"

Group name: expansion
Company offers new job vacancies.
string
Constant:
"opens_new_location"

Group name: expansion
Company opens new service location like hotels, restaurants, bars, hospitals etc.
string
Constant:
"goes_public"

Group name: investment
Company issues shares to the public for the first time.
string
Constant:
"invests_into"

Group name: investment
Company invests into other company.
string
Constant:
"invests_into_assets"

Group name: investment
Company buys assets (like properties or warehouses) from other company.
string
Constant:
"receives_financing"

Group name: investment
Company receives financing like venture funding, loan, grant etc.
string
Constant:
"hires"

Group name: leadership
Company hired new executive or senior personnel.
string
Constant:
"leaves"

Group name: leadership
Executive or senior personnel left the company.
string
Constant:
"promotes"

Group name: leadership
Company promoted existing executive or senior personnel.
string
Constant:
"retires_from"

Group name: leadership
Executive or senior personnel retire from the company.
string
Constant:
"integrates_with"

Group name: new_offering
Company integrates with other company.
string
Constant:
"is_developing"

Group name: new_offering
Company is developing a new offering.
string
Constant:
"launches"

Group name: new_offering
Company launches new offering.
string
Constant:
"partners_with"

Group name: partnership
Company partners with other company.
string
Constant:
"receives_award"

Group name: recognition
Company or person at the company receives an award.
string
Constant:
"recognized_as"

Group name: recognition
Company or person at the company receives recognition.
string
Constant:
"identified_as_competitor_of"

Group name: relational
New or existing competitor was identified.
found_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) when the NewsEvent was discovered and created.
confidence
number
Maximum:
1
Minimum:
0

Floating point number of confidence.

Currently PredictLeads takes care of limiting the NewsEvents so as to have a good balance between match rate and accuracy. Thus we do not recommend filtering NewsEvents further based on this confidence.
article_sentence
string

Sentence in the article where NewsEvent information was found.
human_approved
boolean

If true, this NewsEvent has been manually verified by a human.
planning
boolean
Example:
"The company might plan to launch a product in December."

When true, the NewsEvent is planned to happen.
amount
string nullable
Example:
"$1.8 million"

This is a string of a value as extracted from text.
amount_normalized
integer nullable
Example:
18000000

Integer value of "amount" property.
assets
string nullable
Example:
"hydrogen production facility"

Assets found in the NewsEvent.
assets_tags
array
Example:
[ "production" ]

An array of assets tags extracted from "assets" property.
Items
string
award
string nullable
Example:
"Pierre Potier Award"

Award type this NewsEvent is announcing.
contact
string nullable
Example:
"Elon Musk"

Person name that the NewsEvent mentions.
event
string nullable
Example:
"World Business Outlook Awards"

Event name where the NewsEvent was mentioned at.
effective_date
string nullable
Format:
date
Example:
"2024-09-25"

Date (ISO 8601) the NewsEvent is mentioning.
division
string nullable

Whether NewsEvent concerns a specific division.
financing_type
string nullable
Example:
"Series B funding"

A financing type this NewsEvent is mentioning.
financing_type_normalized
string nullable
One of (61 total):
"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:
"series_b"

A normalized value of financing_type property, where possible.
financing_type_tags
array
Example:
[ "equity" ]

An array of financing type categories.
Items
string
headcount
integer nullable
Example:
213

A number of people mentioned organization is involved with.
job_title
string nullable
Example:
"Director of Media and Analytics"

Job title this NewsEvent is mentioning.
job_title_tags
array
Example:
[ "marketing", "directors" ]

An array of job title tags.
Items
string
location
string nullable
Example:
"Menlo Park, California, United States"

Location of where the NewsEvent happened or where the organization has expanded/relocated.
location_data
array
LocationData
object

The 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:
"Credit Pass"

Name of the product as mentioned by this NewsEvent.
product_data
object
full_text
string nullable
Example:
"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:
"VideoPoet"

Cleaned name of the product referenced in the NewsEvent.
release_type
string nullable
Example:
"major"

Product release type.
release_version
string nullable
Example:
"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:
[ "mobile", "online_technology" ]

List of tags regarding the product.
Items
string
recognition
string nullable
Example:
"50 Smartest Companies"

Name of the recognition the company received.
vulnerability
string nullable
Example:
"account suspension errors"

Company issue.
relationships
object

Contains references to the NewsEvent-related objects listed in the included property.
company1
[optional]
object

Contains a reference to the NewsEvent-related CompanyLite object listed in the included property.
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

Contains a reference to the NewsEvent-related CompanyLite object listed in the included property.
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

Contains a reference to the NewsEvent-related NewsArticleLite object listed in the included property.
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 (2 total)
Discriminator property name:
"type"
CompanyLite
object

The CompanyLite object.
id
string
Format:
uuid

ID of the CompanyLite object.
type
string
Constant:
"company"

Type of the CompanyLite object.
attributes
object

Attributes of the CompanyLite object.
domain
string

Company domain name.
company_name
string nullable

Company name if available.
ticker
string nullable

Company stock symbol (ticker) if available.
NewsArticleLite
object

The 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 the 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
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) when NewsArticle was published.
body
string

News Article text (article content).
meta
[optional]
object

Meta information about NewsEventsDataset object.
schema_version
string

Represents schema version of objects.
record_state
string
Constant:
"active"

Represents record state of objects.
count
[optional]
integer
Example:
344

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.
NewsEventsDataset object

1.7.5.
API Endpoints

1.7.6.
API Webhooks

1.8.
News Events Deleted Dataset

1.8.1.
Introduction

Deleted News Events.

1.8.2.
Data Model

NewsEventsDeletedDataset
object
data
array
NewsEventDeleted
object

The 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.
schema_version
string

Represents schema version of objects.
record_state
string
Constant:
"deleted"

Represents record state of objects.
count
[optional]
integer
Example:
344

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.
NewsEventsDeletedDataset object

1.8.3.
API Webhooks

1.9.
Financing Events Dataset

1.9.1.
Introduction

Financing Events Dataset is the funding category from News Events extended into its own standalone dataset that includes information about Venture Capital rounds and Private Equity investments, grants, loans and other types of investments.

The Financing Events Dataset includes fields such as Financing Type, Investors, Investment Amount, Investment Date, Source URL, and other Financing Event information.

1.9.2.
Data Model

FinancingEventsDataset
object
data
array
FinancingEvent
object

The 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 the FinancingEvent object.
effective_date
string nullable
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) when the FinancingEvent has happened.
found_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) when the FinancingEvent was discovered and created.
categories
array
Example:
[ "private_equity", "series_e", "venture" ]

A FinancingEvent category.
Items
string
One of (75 total):
"private_equity", "equity_crowdfunding", "product_crowdfunding", "convertible_note", "debt_financing", "donation", "grant", "post_ipo_debt", "post_ipo_equity", "post_ipo_secondary", "post_ipo", "initial_public_offering", "initial_coin_offering", "initial_dex_offering", "venture", "pre_angel", "angel", "angel1", "angel2", "angel3", "pre_seed", "seed", "seed1", "seed2", "seed3", "pre_series_a", "series_a", "series_a1", "series_a2", "series_a3", "pre_series_b", "series_b", "series_b1", "series_b2", "series_b3", "pre_series_c", "series_c", "series_c1", "series_c2", "series_c3", "pre_series_d", "series_d", "series_d1", "series_d2", "series_d3", "pre_series_e", "series_e", "series_e1", "series_e2", "series_e3", "pre_series_f", "series_f", "series_f1", "series_f2", "series_f3", "pre_series_g", "series_g", "series_g1", "series_g2", "series_g3", "pre_series_h", "series_h", "series_h1", "series_h2", "series_h3", "pre_series_i", "series_i", "series_i1", "series_i2", "series_i3", "pre_series_j", "series_j", "series_j1", "series_j2", "series_j3"
financing_type
string nullable
Example:
"Series E funding"

A financing type this FinancingEvent is mentioning.
financing_type_normalized
string nullable
One of (61 total):
"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:
"series_e"

A normalized value of financing_type property, where possible.
amount
string nullable
Example:
"$555.5 million"

This is a string of a value as extracted from text.
amount_normalized
integer nullable
Example:
555500000

Integer value of "amount" property.
source_urls
array

Urls to news articles.
Items
string
Format:
uri
Example:
"https://www.chinamoneynetwork.com/2018/10/10/yunfeng-advantech-lead-300m-round-in-chinas-airbnb-copycat-xiaozhu-com"
relationships
object

Contains references to the FinancingEvent-related objects listed in the included property.
company
object

Contains a reference to the FinancingEvent-related CompanyLite object listed in the included property.
data
object
id
string
Format:
uuid

ID of the included CompanyLite object.
type
string
Constant:
"company"

Type of the included CompanyLite object.
investors
object

Contains a reference to the FinancingEvent-related CompanyLite objects listed in the included property.
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

The CompanyLite object.
id
string
Format:
uuid

ID of the CompanyLite object.
type
string
Constant:
"company"

Type of the CompanyLite object.
attributes
object

Attributes of the CompanyLite object.
domain
string

Company domain name.
company_name
string nullable

Company name if available.
ticker
string nullable

Company stock symbol (ticker) if available.
meta
[optional]
object

Meta information about FinancingEventsDataset object.
schema_version
string

Represents schema version of objects.
record_state
string
Constant:
"active"

Represents record state of objects.
count
[optional]
integer
Example:
344

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.
FinancingEventsDataset object

1.9.3.
API Endpoints

1.10.
Connections Dataset

1.10.1.
Introduction

Since 2019, PredictLeads has detected over 180 million business connections between companies globally. The Company Connections Dataset describes relationships between some 40 million companies.

Connections are sourced from Case Study pages, Testimonials, Our Customers sections, and is done through image recognition of company logos.

The Connections Dataset includes fields such as Connection Category, Source Category, Source URL, Context, First Seen At, Last Seen At, and other Connection information.

1.10.2.
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.

1.10.3.
Source Categories

Property in schema: source_category

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

1.10.4.
Use cases

  • Sales Enablement platforms leverage Connections to spot shared customers and enhance their personalization efforts.
  • Market Intelligence platforms use Connections data to identify strategic relationships and analyze a company's network health.
  • Investment Companies keep track of company Connections to validate market position and assess portfolio fit.

1.10.5.
Data Model

ConnectionsDataset
object
data
array
Connection
object

The Connection object.
id
string
Format:
uuid

ID of the Connection object.
type
string
Constant:
"connection"

Type of the Connection object.
attributes
object

Attributes of the Connection object.
category
string

Connection category.
One of (8 total)
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 (30 total):
"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 Connection category was found.
source_url
string nullable
Format:
uri

URL where Connection information was found.
individual_source_url
string nullable
Format:
uri

URL where Connection` additional information was found.
context
string nullable

Text found near the connection. Often contains additional relationship information between the two companies.
first_seen_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) of when the Connection was first seen.
last_seen_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) of when the Connection was last seen.
relationships
object

Contains references to the Connection-related objects listed in the included property. Order of companies is guaranteed and necessary to preserve the meaning of the category. For example: Company1 is a vendor to Company2.
company1
object

Contains a reference to the Connection-related CompanyLite object listed in the included property.
data
object
id
string
Format:
uuid

ID of the included CompanyLite object.
type
string
Constant:
"company"

Type of the included CompanyLite object.
company2
object

Contains a reference to the Connection-related CompanyLite object listed in the included property.
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

The CompanyLite object.
id
string
Format:
uuid

ID of the CompanyLite object.
type
string
Constant:
"company"

Type of the CompanyLite object.
attributes
object

Attributes of the CompanyLite object.
domain
string

Company domain name.
company_name
string nullable

Company name if available.
ticker
string nullable

Company stock symbol (ticker) if available.
meta
[optional]
object

Meta information about ConnectionsDataset object.
schema_version
string

Represents schema version of objects.
record_state
string
Constant:
"active"

Represents record state of objects.
count
[optional]
integer
Example:
344

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.
ConnectionsDataset object

1.10.6.
API Endpoints

1.11.
Website Evolution Dataset

1.11.1.
Introduction

Since 2021, PredictLeads has detected some 470 million subpages on about 60 million websites, with the goal to track how their websites are evolving through time.

The Website Evolution dataset tracks when subpages such as "about us", "blog", "careers", "api docs", "customer support" etc. are added over time. The more subpages that are added in a shorter time-frame, the faster the company grows.

1.11.2.
Data Model

WebsiteEvolutionDataset
object
data
array
Subpage
object

The Subpage object.
id
string
Format:
uuid

ID of the Subpage object.
type
string
Constant:
"subpage"

Type of the Subpage object.
attributes
object

Attributes of the Subpage object.
url
string
Format:
uri

Subpage URL.
first_seen_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) of when the Subpage was first seen.
last_seen_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) of when the Subpage was last seen.
category
string
One of (52 total):
"blog", "news", "press", "about", "leadership", "jobs", "facebook", "twitter", "linkedin", "instagram", "pinterest", "contact", "playstore", "appstore", "news_outlet", "customers", "partners", "integrations", "case_studies", "portfolio", "suppliers", "testimonials", "login", "support", "pricing", "job_board", "main", "github", "investors", "products", "solutions", "status", "demo", "resources", "events", "developers", "api", "terms", "privacy", "trust", "feedback", "marketplace", "store", "community", "retail", "sitemap", "webinars", "search", "social_media", "updates", "registration", "locations"

Subpage category.
text_content
string nullable

Text content from the subpage. Sentences are contextually separated with new lines (\n).
relationships
object

Contains references to the Subpage-related objects listed in the included property.
company
object

Contains a reference to the Subpage-related CompanyLite object listed in the included property.
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

The CompanyLite object.
id
string
Format:
uuid

ID of the CompanyLite object.
type
string
Constant:
"company"

Type of the CompanyLite object.
attributes
object

Attributes of the CompanyLite object.
domain
string

Company domain name.
company_name
string nullable

Company name if available.
ticker
string nullable

Company stock symbol (ticker) if available.
meta
[optional]
object

Meta information about WebsiteEvolutionDataset object.
schema_version
string

Represents schema version of objects.
record_state
string
Constant:
"active"

Represents record state of objects.
count
[optional]
integer
Example:
344

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.
WebsiteEvolutionDataset object

1.11.3.
API Endpoints

1.12.
Github Repositories Dataset

1.12.1.
Introduction

Since 2021, PredictLeads has detected some 330,000 public GitHub repositories. GitHub Repositories Dataset covers about 65,000 companies, and provides insight into the frequency of their contribution into their public GitHub repository.

1.12.2.
Data Model

GithubRepositoriesDataset
object
data
array
GithubRepository
object

The GithubRepository object.
id
string
Format:
uuid

ID of the GithubRepository object.
type
string
Constant:
"github_repository"

Type of the GithubRepository object.
attributes
object

Attributes of the GithubRepository object.
url
string
Format:
uri

Url to GitHub repository.
description
string nullable

Description of GitHub repository.
first_seen_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) when the GitHub repository was discovered.
relationships
object

Contains references to the GithubRepository-related objects listed in the included property.
company
object

Contains a reference to the GithubRepository-related CompanyLite object listed in the included property.
data
object
id
string
Format:
uuid

ID of the included CompanyLite object.
type
string
Constant:
"company"

Type of the included CompanyLite object.
timeseries_datapoints
object

Contains a reference to the GithubRepository-related TimeseriesDatapointGithubRepository objects listed in the included property.
data
array
Items
object
id
string
Format:
uuid

ID of the included TimeseriesDatapointGithubRepository object.
type
string
Constant:
"timeseries_datapoint"

Type of the included TimeseriesDatapointGithubRepository object.
included
array

Contains objects referenced in property relationships.
Items
One of (2 total)
Discriminator property name:
"type"
CompanyLite
object

The CompanyLite object.
id
string
Format:
uuid

ID of the CompanyLite object.
type
string
Constant:
"company"

Type of the CompanyLite object.
attributes
object

Attributes of the CompanyLite object.
domain
string

Company domain name.
company_name
string nullable

Company name if available.
ticker
string nullable

Company stock symbol (ticker) if available.
TimeseriesDatapointGithubRepository
object

The TimeseriesDatapointGithubRepository object.
attributes
[optional]
data
[optional]
object
forks
integer

Number of times this repository was forked at the creation of this datapoint.
stars
integer

Number of times this repository was starred at the creation of this datapoint.
watches
integer

Number of GitHub profiles that were watching this repository at the creation of this datapoint.
last_update_at
string
Format:
date-time
Example:
"2024-09-25T16:09:53+00:00"

Date & time (ISO 8601) of the last update to the repository at the creation of this datapoint.
meta
[optional]
object

Meta information about GithubRepositoriesDataset object.
schema_version
string

Represents schema version of objects.
record_state
string
Constant:
"active"

Represents record state of objects.
count
[optional]
integer
Example:
344

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.
GithubRepositoriesDataset object

1.12.3.
API Endpoints

1.13.
Startup Platform Posts Dataset

1.13.1.
Introduction

Provides information about posts on popular startup platforms by companies that are hiring or launching new products.

1.13.2.
Data Model

StartupPlatformPostsDataset
object
data
array
StartupPlatformPost
object

The StartupPlatformPost object.
id
string
Format:
uuid

ID of the StartupPlatformPost object.
type
string
Constant:
"startup_platform_post"

Type of the StartupPlatformPost object.
attributes
object

Attributes of the StartupPlatformPost object.
published_at
string
Format:
date-time

Date & time (ISO 8601) when the StartupPlatformPost was published.
post_type
string

Indicates on what startup platform post was found.
One of (2 total)
string
Constant:
"show_hn"

Post on startup platform Hacker News - Show page.
string
Constant:
"job_hn"

Post on startup platform Hacker News - Job page.
post_url
string
Format:
uri

Url to post.
company_domain
string

Company domain name.
company_name
string nullable

Company name if available.
fuzzy_match
boolean

When true we might not have extracted the product name cleanly. When false exact product name should be extracted well.
meta
[optional]
object

Meta information about StartupPlatformPostsDataset object.
schema_version
string

Represents schema version of objects.
record_state
string
Constant:
"active"

Represents record state of objects.
count
[optional]
integer
Example:
344

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.
StartupPlatformPostsDataset object

1.13.3.
API Endpoints

1.14.
Api Subscription

1.14.1.
Introduction

Provides the current subscription's status and API usage limits.

This endpoint returns the current subscription's status, the maximum number of API requests allowed per month, and the number of API requests made so far during the current month.

1.14.2.
Data Model

ApiSubscription
object
data
array
Items
object
id
string
Format:
uuid

ID of the ApiSubscription object.
type
string
Constant:
"api_subscription"

Type of the ApiSubscription object.
attributes
object
status
string
One of (3 total):
"disabled", "active", "disabled_due_to_error"

Current subscription status.
max_monthly_api_requests
integer

Amount of available monthly requests with the current subscription.
monthly_api_request_count
integer

Current amount of used API requests.
meta
[optional]
object

Meta information about ApiSubscription object.
schema_version
string

Represents schema version of objects.
ApiSubscription object

1.14.3.
API Endpoints

2.
API Endpoints

2.1.
Introduction

Our API is RESTful and follows the JSON API specification.

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

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

You can see live status of our API services.

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

Base URL

2.1.1.
Authentication

Authentication is done via the authentication token which you can find on Your Subscription Plans page.

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

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

2.1.2.
Request Limits

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

Once the account reaches the limit, all further requests will produce a 402 HTTP error, notifying the user of the reached limit. If you have any further questions regarding the limits, feel free to contact us via support.

2.1.3.
Pagination

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

2.2.
Follow Companies

2.2.1.
Retrieve followed companies

Returns followed companies.

GET https://predictleads.com/api/v3/followings
Query Parameters
page
[optional]
integer
Default:
1
Minimum:
1

Page number of shown items.
NOTE: If the parameter is not provided, the meta property count will be omitted from response for performance reasons.
limit
[optional]
integer
Default:
100
Maximum:
1000

Limit the number of shown items per page.
Request Examples
Response Examples
Response Schemas

2.2.2.
Follow the company

Follow the company.

POST https://predictleads.com/api/v3/companies/{domain}/follow
Path Parameters
domain
string

Company's domain.
Query Parameters
custom_company_identifier
[optional]
string
Example:
"435366"

Provide, if you want to use your custom company identifier.
Request Examples
Response Examples
Response Schemas

2.2.3.
Unfollow the company

Unfollow the company.

POST https://predictleads.com/api/v3/companies/{domain}/unfollow
Path Parameters
domain
string

Company's domain.
Request Examples
Response Examples
Response Schemas

2.3.
Companies Dataset

Companies Dataset API Endpoints
Retrieve Company
GET /companies/{domain}

2.3.1.
Retrieve Company

Returns Company.

GET https://predictleads.com/api/v3/companies/{domain}
Path Parameters
domain
string

Company's domain.
Request Examples
Response Examples
Response Schemas

2.4.
Job Openings Dataset

2.4.1.
Retrieve company's Job Openings

Returns a list of company's Job Openings.

GET https://predictleads.com/api/v3/companies/{domain}/job_openings
Path Parameters
domain
string

Company's domain.
Query Parameters
active_only
[optional]
boolean

Set to true if you'd like to receive JobOpenings that are not closed, have last_seen_at more recent than 5 days and were found in the last year.
not_closed
[optional]
boolean

Similar to active_only, but without considering last_seen_at timestamp.
first_seen_at_from
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return JobOpenings first seen after given date (ISO 8601).
first_seen_at_until
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return JobOpenings first seen before given date (ISO 8601).
last_seen_at_from
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return JobOpenings last seen after given date (ISO 8601).
last_seen_at_until
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return JobOpenings last seen before given date (ISO 8601).
with_description_only
[optional]
boolean

Only return JobOpenings that have description.
with_location_only
[optional]
boolean

Only return JobOpenings that have location.
categories
[optional]
array

Comma-separated (,) JobOpening categories.
Items
string
One of (27 total):
"administration", "consulting", "data_analysis", "design", "directors", "education", "engineering", "finance", "healthcare_services", "human_resources", "information_technology", "internship", "legal", "management", "marketing", "military_and_protective_services", "operations", "purchasing", "product_management", "quality_assurance", "real_estate", "research", "sales", "software_development", "support", "manual_work", "food"
page
[optional]
integer
Default:
1
Minimum:
1

Page number of shown items.
NOTE: If the parameter is not provided, the meta property count will be omitted from response for performance reasons.
limit
[optional]
integer
Default:
100
Maximum:
1000

Limit the number of shown items per page.
Request Examples
Response Examples
Response Schemas

2.4.2.
Retrieve a single Job Opening by ID

Returns a single Job Opening.

GET https://predictleads.com/api/v3/job_openings/{id}
Path Parameters
id
string
Format:
uuid

JobOpening's ID.
Request Examples
Response Examples
Response Schemas

2.5.
Technology Detections Dataset

2.5.1.
Retrieve Technologies used by specific Company

Returns Technologies used by a specific Company as a list of Technology Detections.

GET https://predictleads.com/api/v3/companies/{domain}/technology_detections
Path Parameters
domain
string

Company's domain.
Query Parameters
first_seen_at_from
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return TechnologyDetections first seen after given date (ISO 8601).
first_seen_at_until
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return TechnologyDetections first seen before given date (ISO 8601).
last_seen_at_from
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return TechnologyDetections last seen after given date (ISO 8601).
last_seen_at_until
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return TechnologyDetections last seen before given date (ISO 8601).
page
[optional]
integer
Default:
1
Minimum:
1

Page number of shown items.
NOTE: If the parameter is not provided, the meta property count will be omitted from response for performance reasons.
limit
[optional]
integer
Default:
100
Maximum:
1000

Limit the number of shown items per page.
Request Examples
Response Examples
Response Schemas

2.5.2.
Retrieve Companies using specific Technology

Returns Companies using a specific Technology as a list of Technology Detections, ordered by the first_seen_at, descending.

E.g. using this endpoint one can get a list of all companies using HubSpot or any other of the 30,000 technologies PredictLeads tracks.

The specific Technology ID can be obtained by querying the Retrieve all tracked Technologies endpoint.

GET https://predictleads.com/api/v3/discover/technologies/{id}/technology_detections
Path Parameters
id
string
Format:
uuid

Technology's ID.
Query Parameters
first_seen_at_from
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return TechnologyDetections first seen after given date (ISO 8601).
first_seen_at_until
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return TechnologyDetections first seen before given date (ISO 8601).
last_seen_at_from
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return TechnologyDetections last seen after given date (ISO 8601).
last_seen_at_until
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return TechnologyDetections last seen before given date (ISO 8601).
page
[optional]
integer
Default:
1
Minimum:
1

Page number of shown items.
NOTE: If the parameter is not provided, the meta property count will be omitted from response for performance reasons.
limit
[optional]
integer
Default:
100
Maximum:
1000

Limit the number of shown items per page.
Request Examples
Response Examples
Response Schemas

2.6.
Technologies Dataset

2.6.1.
Retrieve all tracked Technologies

Returns a list of all tracked Technologies.

GET https://predictleads.com/api/v3/technologies
Query Parameters
order_by
[optional]
string
Default:
created_at_asc

Order of TechnologiesDatasets.
One of (2 total)
string
Constant:
"created_at_asc"

Order by created_at, ascending.
string
Constant:
"created_at_desc"

Order by created_at, descending.
page
[optional]
integer
Default:
1
Minimum:
1

Page number of shown items.
NOTE: If the parameter is not provided, the meta property count will be omitted from response for performance reasons.
limit
[optional]
integer
Default:
100
Maximum:
1000

Limit the number of shown items per page.
Request Examples
Response Examples
Response Schemas

2.6.2.
Retrieve a single Technology by ID

Returns a single Technology.

GET https://predictleads.com/api/v3/technologies/{id}
Path Parameters
id
string
Format:
uuid

Technology's ID.
Request Examples
Response Examples
Response Schemas

2.7.
News Events Dataset

2.7.1.
Retrieve company's News Events

Returns a list of company's News Events.

GET https://predictleads.com/api/v3/companies/{domain}/news_events
Path Parameters
domain
string

Company's domain.
Query Parameters
found_at_from
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return NewsEvents found after given date (ISO 8601).
found_at_until
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return NewsEvents found before given date (ISO 8601).
categories
[optional]
array

Comma-separated (,) NewsEvent categories.
Items
One of (29 total)
string
Constant:
"acquires"

Group name: acquisition
Company acquired another company.
string
Constant:
"merges_with"

Group name: acquisition
Company merges with another company.
string
Constant:
"sells_assets_to"

Group name: acquisition
Company sells assets (like properties or warehouses) to other company.
string
Constant:
"signs_new_client"

Group name: contract
Company signs new client.
string
Constant:
"files_suit_against"

Group name: corporate_challenges
Company files suit against other company.
string
Constant:
"has_issues_with"

Group name: corporate_challenges
Company has vulnerability problems.
string
Constant:
"closes_offices_in"

Group name: cost_cutting
Company closes existing offices.
string
Constant:
"decreases_headcount_by"

Group name: cost_cutting
Company lays off employees.
string
Constant:
"attends_event"

Group name: expansion
Company attends an event.
string
Constant:
"expands_facilities"

Group name: expansion
Company opens new or expands existing facilities like warehouses, data centers, manufacturing plants etc.
string
Constant:
"expands_offices_in"

Group name: expansion
Company expands existing offices.
string
Constant:
"expands_offices_to"

Group name: expansion
Company opens new offices in another town, state, country or continent.
string
Constant:
"increases_headcount_by"

Group name: expansion
Company offers new job vacancies.
string
Constant:
"opens_new_location"

Group name: expansion
Company opens new service location like hotels, restaurants, bars, hospitals etc.
string
Constant:
"goes_public"

Group name: investment
Company issues shares to the public for the first time.
string
Constant:
"invests_into"

Group name: investment
Company invests into other company.
string
Constant:
"invests_into_assets"

Group name: investment
Company buys assets (like properties or warehouses) from other company.
string
Constant:
"receives_financing"

Group name: investment
Company receives financing like venture funding, loan, grant etc.
string
Constant:
"hires"

Group name: leadership
Company hired new executive or senior personnel.
string
Constant:
"leaves"

Group name: leadership
Executive or senior personnel left the company.
string
Constant:
"promotes"

Group name: leadership
Company promoted existing executive or senior personnel.
string
Constant:
"retires_from"

Group name: leadership
Executive or senior personnel retire from the company.
string
Constant:
"integrates_with"

Group name: new_offering
Company integrates with other company.
string
Constant:
"is_developing"

Group name: new_offering
Company is developing a new offering.
string
Constant:
"launches"

Group name: new_offering
Company launches new offering.
string
Constant:
"partners_with"

Group name: partnership
Company partners with other company.
string
Constant:
"receives_award"

Group name: recognition
Company or person at the company receives an award.
string
Constant:
"recognized_as"

Group name: recognition
Company or person at the company receives recognition.
string
Constant:
"identified_as_competitor_of"

Group name: relational
New or existing competitor was identified.
page
[optional]
integer
Default:
1
Minimum:
1

Page number of shown items.
NOTE: If the parameter is not provided, the meta property count will be omitted from response for performance reasons.
limit
[optional]
integer
Default:
100
Maximum:
1000

Limit the number of shown items per page.
Request Examples
Response Examples
Response Schemas

2.7.2.
Retrieve a single News Event by ID

Returns specific News Event.

GET https://predictleads.com/api/v3/news_events/{id}
Path Parameters
id
string
Format:
uuid

NewsEvent's ID.
Request Examples
Response Examples
Response Schemas

2.8.
Financing Events Dataset

2.8.1.
Retrieve company's Financing Events

Returns a list of company's Financing Events.

GET https://predictleads.com/api/v3/companies/{domain}/financing_events
Path Parameters
domain
string

Company's domain.
Query Parameters
first_seen_at_from
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return FinancingEvents first seen after given date (ISO 8601).
first_seen_at_until
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return FinancingEvents first seen before given date (ISO 8601).
page
[optional]
integer
Default:
1
Minimum:
1

Page number of shown items.
NOTE: If the parameter is not provided, the meta property count will be omitted from response for performance reasons.
limit
[optional]
integer
Default:
100
Maximum:
1000

Limit the number of shown items per page.
Request Examples
Response Examples
Response Schemas

2.9.
Connections Dataset

2.9.1.
Retrieve company's Connections

Returns a list of company's Connections.

GET https://predictleads.com/api/v3/companies/{domain}/connections
Path Parameters
domain
string

Company's domain.
Query Parameters
first_seen_at_from
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return Connections first seen after given date (ISO 8601).
first_seen_at_until
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return Connections first seen before given date (ISO 8601).
categories
[optional]
array

Comma-separated (,) Connection categories.
Items
One of (8 total)
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.
page
[optional]
integer
Default:
1
Minimum:
1

Page number of shown items.
NOTE: If the parameter is not provided, the meta property count will be omitted from response for performance reasons.
limit
[optional]
integer
Default:
100
Maximum:
1000

Limit the number of shown items per page.
Request Examples
Response Examples
Response Schemas

2.9.2.
Retrieve Portfolio Companies

Returns the Portfolio Companies found on the portfolio pages of thousands of VCs, accelerators, and incubators, as a list of Connections categorized as investor, ordered by the first_seen_at, descending.

GET https://predictleads.com/api/v3/discover/portfolio_companies/connections
Query Parameters
first_seen_at_from
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return TechnologyDetections first seen after given date (ISO 8601).
first_seen_at_until
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return TechnologyDetections first seen before given date (ISO 8601).
last_seen_at_from
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return TechnologyDetections last seen after given date (ISO 8601).
last_seen_at_until
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return TechnologyDetections last seen before given date (ISO 8601).
page
[optional]
integer
Default:
1
Minimum:
1

Page number of shown items.
NOTE: If the parameter is not provided, the meta property count will be omitted from response for performance reasons.
limit
[optional]
integer
Default:
100
Maximum:
1000

Limit the number of shown items per page.
Request Examples
Response Examples
Response Schemas

2.10.
Website Evolution Dataset

2.10.1.
Retrieve company's Website Evolution

Returns a list of company's Website Evolution.

GET https://predictleads.com/api/v3/companies/{domain}/website_evolution
Path Parameters
domain
string

Company's domain.
Query Parameters
first_seen_at_from
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return Subpages first seen after given date (ISO 8601).
first_seen_at_until
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return Subpages first seen before given date (ISO 8601).
page
[optional]
integer
Default:
1
Minimum:
1

Page number of shown items.
NOTE: If the parameter is not provided, the meta property count will be omitted from response for performance reasons.
limit
[optional]
integer
Default:
100
Maximum:
1000

Limit the number of shown items per page.
Request Examples
Response Examples
Response Schemas

2.11.
Github Repositories Dataset

2.11.1.
Retrieve company's Github Repositories

Returns a list of company's Github Repositories.

GET https://predictleads.com/api/v3/companies/{domain}/github_repositories
Path Parameters
domain
string

Company's domain.
Query Parameters
first_seen_at_from
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return GithubRepositories first seen after given date (ISO 8601).
first_seen_at_until
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return GithubRepositories first seen before given date (ISO 8601).
page
[optional]
integer
Default:
1
Minimum:
1

Page number of shown items.
NOTE: If the parameter is not provided, the meta property count will be omitted from response for performance reasons.
limit
[optional]
integer
Default:
100
Maximum:
1000

Limit the number of shown items per page.
Request Examples
Response Examples
Response Schemas

2.12.
Startup Platform Posts Dataset

Startup Platform Posts Dataset API Endpoints
Retrieve latest posts
GET /discover/startup_platform_posts

2.12.1.
Retrieve latest posts

Returns a list of latest posts on popular startup platforms by companies that are hiring or launching new products.

GET https://predictleads.com/api/v3/discover/startup_platform_posts
Query Parameters
published_at_from
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return StartupPlatformPosts published after given date (ISO 8601).
published_at_until
[optional]
string
Format:
date
Example:
"2024-09-25"

Only return StartupPlatformPosts published before given date (ISO 8601).
post_types
[optional]
array

Comma-separated (,) StartupPlatformPost post types.
Items
One of (2 total)
string
Constant:
"show_hn"

Post on startup platform Hacker News - Show page.
string
Constant:
"job_hn"

Post on startup platform Hacker News - Job page.
page
[optional]
integer
Default:
1
Minimum:
1

Page number of shown items.
NOTE: If the parameter is not provided, the meta property count will be omitted from response for performance reasons.
limit
[optional]
integer
Default:
100
Maximum:
1000

Limit the number of shown items per page.
Request Examples
Response Examples
Response Schemas

2.13.
Api Subscription

2.13.1.
Retrieve API subscription information

Provides the current subscription's status and usage limits

GET https://predictleads.com/api/v3/api_subscription
Request Examples
Response Examples
Response Schemas

3.
API Webhooks

3.1.
Introduction

A webhook is an event-driven mechanism that automatically sends updated dataset records from our server to your Webhook URL over HTTP whenever a specific trigger event occurs.

NOTE: Webhooks are triggered only for companies you follow and are not enabled by default. If you'd like to use them, please get in touch with our sales team to enable this feature.

3.1.1.
Server Requirements

  • Compatibility
    The server must be able to receive a payload in the request body using any of the following supported HTTP methods: POST, PUT, PATCH or DELETE

    NOTE: Some Microsoft and other similar servers do not support receiving a payload in the request body using the DELETE HTTP method.

  • Fast Response Time
    The server should respond with a status code of 200 (OK) as soon as it receives a payload in the request body, ensuring that the connection is closed within 5 seconds. It should then process and store the data asynchronously in the background.

    IMPORTANT: Your server should not process data while request connection is still open, as this may cause a timeout and could result in your subscription being temporarily disabled.

  • Availability
    We recommend implementing your webhook server as a stand-alone application to ensure uptime during deployments of your main application.

  • Connectivity
    A reliable and stable connection.

3.1.2.
How It Works

NOTE: Webhooks are triggered only for companies you follow and are not enabled by default. If you'd like to use them, please get in touch with our sales team to enable this feature.


  1. Setting Up
    On the Webhook Subscriptions page, for any Webhook Subscription, choose the record state (Active for receiving created/updated records or Deleted for receiving deleted records), select the HTTP method that your server supports, provide a Webhook URL endpoint on your server to handle incoming requests, and click Save Webhook Settings.

  2. Testing
    On the Webhook Subscriptions page, for any Webhook Subscription, you can use the Webhook Test form to confirm that your provided settings work.

  3. Trigger Event Occurs
    When a specified trigger event occurs on our server (e.g., a new News Event is created), our server triggers the webhook.

  4. Sending the Payload
    Our server sends an HTTP request to your Webhook URL endpoint, including a payload in the request body.

  5. Processing the Data
    Your server receives the request, responds with a status code of 200 (OK), and processes the received data asynchronously in the background.

3.1.3.
Authentication

To verify that the webhook request came from PredictLeads, we suggest verifying the X-Predict-Signature header value, which is included with every webhook request we send.

The header value is a SHA1 HMAC hexdigest, computed using your authentication token as the key and the raw body of the request as the data.

You can also configure custom headers for your Webhook Subscriptions if needed.

3.2.
Job Openings Dataset

3.2.1.
Receive created or updated company's Job Openings

Sends a list of created or updated company's Job Openings.

POST {webhook_url}
Request Body

3.3.
Job Openings Deleted Dataset

Job Openings Deleted Dataset API Webhooks
Receive deleted company's Job Openings
DELETE {webhook_url}

3.3.1.
Receive deleted company's Job Openings

Sends a list of deleted company's Job Openings.

DELETE {webhook_url}
Request Body

3.4.
Technology Detections Dataset

3.4.1.
Receive created company's Technology Detections

Sends a list of created company's Technology Detections.

POST {webhook_url}
Request Body

3.5.
News Events Dataset

3.5.1.
Receive created or updated company's News Events

Sends a list of created or updated company's News Events.

POST {webhook_url}
Request Body

3.6.
News Events Deleted Dataset

News Events Deleted Dataset API Webhooks
Receive deleted company's News Events
DELETE {webhook_url}

3.6.1.
Receive deleted company's News Events

Sends a list of deleted company's News Events.

DELETE {webhook_url}
Request Body