API Reference
Search People
Search professional profiles with advanced filters
POST
Documentation Index
Fetch the complete documentation index at: https://docs.dataforb2b.ai/llms.txt
Use this file to discover all available pages before exploring further.
Credit Cost
| Mode | Credits per Result |
|---|---|
enrich_live: true | 1.5 credits |
enrich_live: false | 0.75 credits |
Need access to additional filters? Contact us at [email protected] with your use case.
Request Body
LinkedIn profile URL,
public_id, or member_identity of a person to find similar profiles.Examples:"https://www.linkedin.com/in/thomas-kurian-469b6219/""sebonomics"
Use case for the lookalike search. Only used when
lookalike_profile is provided."sales"— Find profiles similar from a sales/prospecting perspective"recruiter"— Find profiles similar from a recruiting perspective
Logical operator to combine conditions.
and- All conditions must matchor- At least one condition must match
Array of filter conditions or nested filter groups.
Column name to filter on. See Available Columns section below for complete list with data types and recommended operators.Profile:
first_name, last_name, profile_location, profile_country, profile_industry, follower_count, keywordCurrent Job: current_company, current_title, current_job_location, current_company_industry, current_company_category, current_company_size (values: 2-10, 11-50, 51-200, 201-500, 501-1000, 1001-5000, 5001-10000, 10001+), current_company_id, current_employment_type, years_in_current_position, years_at_current_company, current_company_has_funding, current_company_funding_stage, current_company_investorPast Jobs: past_company, past_title, past_job_location, past_company_industry, past_company_size (same values as current_company_size), past_company_id, past_employment_type, years_at_past_companySkills: skill (e.g., “Python”, “JavaScript”, “Machine Learning”)Education: school, degree, degree_level (e.g., “Bachelor”, “Master”, “PhD”), field_of_studyLanguages: language (full name), language_iso (ISO code like “en”, “fr”), language_proficiency (e.g., “Native”, “Professional”, “Elementary”)Certifications: certification, certification_authorityExperience: years_of_experience (total career), num_total_jobs (number of positions), is_currently_employed (boolean)Comparison operator to use. Available operators:
-
=- Exact match -
>- Greater than -
>=- Greater than or equal -
<- Less than -
<=- Less than or equal -
between- Between two values (requiresvalue2) -
in- Value is in a list -
like- Text search (pattern matching)
Filter value to compare against.
Second value (only used with
between operator).Number of results to skip for pagination.
Number of results to return. Maximum: 1000.
Whether to enrich profiles with live data.
true- Returns fresh, up-to-date data (1.5 credits per result)false- Returns cached data (0.75 credits per result)
Filters System
FilterGroup Structure
A FilterGroup combines multiple conditions with a logical operator:| Field | Type | Description |
|---|---|---|
op | string | Logical operator: "and" or "or" |
conditions | array | List of FilterCondition or nested FilterGroup (recursive) |
FilterCondition Structure
| Field | Type | Description |
|---|---|---|
column | string | Column name to filter on |
type | string | Operator type (see below) |
value | any | Filter value |
value2 | any | Second value (only for between operator) |
Available Operators
| Operator | Description | Example |
|---|---|---|
= | Exact match | {"column": "first_name", "type": "=", "value": "John"} |
> | Greater than | {"column": "follower_count", "type": ">", "value": 1000} |
>= | Greater than or equal | {"column": "years_of_experience", "type": ">=", "value": 5} |
< | Less than | {"column": "years_at_current_company", "type": "<", "value": 2} |
<= | Less than or equal | {"column": "follower_count", "type": "<=", "value": 500} |
between | Between two values | {"column": "years_of_experience", "type": "between", "value": 3, "value2": 7} |
in | In a list | {"column": "country", "type": "in", "value": ["US", "UK", "CA"]} |
like | Text search | {"column": "current_title", "type": "like", "value": "Engineer"} |
Available Columns
Profile
| Column | Type | Recommended Operators | Description |
|---|---|---|---|
first_name | text | =, like, in | Person’s first name |
last_name | text | =, like, in | Person’s last name |
profile_location | text | =, like | Current location (city, state) |
profile_country | text | =, in | Country code ISO 2 letters, uppercase (e.g., “US”, “GB”, “FR”, “DE”, “CA”). Note: use GB (not UK) for United Kingdom |
profile_industry | text | =, like, in | Profile industry |
follower_count | int | =, >, >=, <, <=, between | Number of profile followers |
keyword | text | like | Full-text search in headline (trigram matching) |
Current Job
| Column | Type | Operators | Description |
|---|---|---|---|
current_company | text | =, like, in | Current employer name |
current_title | text | =, like, in | Current job title |
current_job_location | text | =, like, in | Current job location (city, state) |
current_company_industry | text | =, like, in | Industry of current employer, capitalized (e.g., “Information Technology & Services”, “Computer Software”, “Hospital & Health Care”, “Financial Services”, “Marketing & Advertising”) |
current_company_category | text | =, like, in | Category of current employer, lowercase (e.g., software, consulting, financial services, e-commerce, health care) |
current_company_size | text | =, in | Company size range (2-10, 11-50, 51-200, 201-500, 501-1000, 1001-5000, 5001-10000, 10001+) |
current_company_id | text | =, in | Company identifier |
current_employment_type | text | =, in | Employment type. Values: “Full-time”, “Part-time”, “Self-employed”, “Freelance”, “Contract”, “Permanent”, “Permanent Full-time”, “Permanent Part-time”, “Contract Full-time”, “Contract Part-time”, “Internship”, “Apprenticeship”, “Seasonal” |
years_in_current_position | int | =, >, >=, <, <=, between | Years in current role |
years_at_current_company | int | =, >, >=, <, <=, between | Years at current company |
current_company_has_funding | bool | = | Whether the current company has received funding (true/false) |
current_company_funding_stage | text | =, in | Funding stage of the current company. Values: seed_round, series_a, series_b, series_c, series_d, series_e, series_f, series_g, series_h, series_unknown, pre_seed_round, angel_round, grant, private_equity_round, debt_financing, convertible_note, corporate_round, equity_crowdfunding, post_ipo_equity, post_ipo_debt, post_ipo_secondary, secondary_market, non_equity_assistance, product_crowdfunding, initial_coin_offering, undisclosed. Note: data also contains some legacy values without _round suffix (seed, pre_seed, angel, private_equity) — use in with both forms for max coverage |
current_company_investor | text | =, like, in | Investor name of the current company. Supports partial matching with like |
Past Jobs
| Column | Type | Operators | Description |
|---|---|---|---|
past_company | text | =, like, in | Former employer name |
past_title | text | =, like, in | Previous job title |
past_job_location | text | =, like, in | Past job location (city, state) |
past_company_industry | text | =, like, in | Industry of former employer |
past_company_size | text | =, in | Former company size range (same values as current_company_size) |
past_company_id | text | =, in | Company identifier of former employer |
past_employment_type | text | =, in | Past employment type |
years_at_past_company | int | =, >, >=, <, <=, between | Years spent at former company |
Skills
| Column | Type | Operators | Description |
|---|---|---|---|
skill | text | =, like, in | Skills listed on profile (e.g., “Python”, “JavaScript”, “Machine Learning”) |
Education
| Column | Type | Operators | Description |
|---|---|---|---|
school | text | =, like, in | Educational institution name |
degree | text | =, like, in | Degree name |
degree_level | text | =, in | Degree level (e.g., “Bachelor”, “Master”, “PhD”, “Associate”) |
field_of_study | text | =, like, in | Major or field of study |
Languages
| Column | Type | Operators | Description |
|---|---|---|---|
language | text | =, like, in | Language name (e.g., “English”, “French”, “Spanish”) |
language_iso | text | =, in | ISO language code (e.g., “en”, “fr”, “es”) |
language_proficiency | text | =, in | Proficiency level (e.g., “Native”, “Professional”, “Limited”, “Elementary”) |
Certifications
| Column | Type | Operators | Description |
|---|---|---|---|
certification | text | =, like, in | Certification name (e.g., “AWS Certified”, “PMP”, “CPA”) |
certification_authority | text | =, like, in | Issuing organization (e.g., “Amazon Web Services”, “PMI”) |
Experience
| Column | Type | Operators | Description |
|---|---|---|---|
years_of_experience | int | =, >, >=, <, <=, between | Total years of professional experience |
num_total_jobs | int | =, >, >=, <, <=, between | Total number of positions held |
is_currently_employed | bool | = | Whether person is currently employed (true/false) |
Complex Query Examples
Senior developers at FAANG companies
Senior developers at FAANG companies
Ex-consultants from McKinsey/BCG now at startups
Ex-consultants from McKinsey/BCG now at startups
Engineers at Series A/B funded companies backed by Sequoia
Engineers at Series A/B funded companies backed by Sequoia
OR search (profiles with Python OR JavaScript)
OR search (profiles with Python OR JavaScript)
Response
Total number of results matching the filters.
Offset used for pagination.
Number of results returned in this response.
List of profiles matching the filters. Each profile contains the fields specified by the search columns.