Odyssey - Private Market Intelligence

Odyssey is a private market intelligence dataset for agents that provides comprehensive data on companies, investors, people, deals, funds, limited partners, service providers, patents, and credit analysis.

It aggregates and enriches data from multiple sources to deliver a unified dataset for AI agents powering private market research, due diligence, and investment analysis.

What Odyssey Covers

Entity	Description	Key Data Points
Companies	Private and public company profiles	Financials, funding history, investors, industries, social analytics, similar companies
People	Professional profiles and contact data	Bio, work history, education, verified contact info
Investors	VC, PE, and angel investor profiles	Portfolio companies, investment preferences, funds, board seats
Deals	Funding rounds and transactions	Round details, participants, valuations, service providers
Funds	Investment fund data	Portfolio, performance, LP commitments
Limited Partners	LP institution profiles	Commitment preferences, allocations
Service Providers	Law firms, auditors, advisors	Company clients, deal participation, investor relationships
Patents	Patent search and details	Patent filings, inventors, assignees
Credit Analysis	Private credit market intelligence	News, agreements, BDC portfolios, borrowers, fund formations, market spreads, N-PORT filings, relationships, SBA lending

How It Works

Search any entity type by name, domain, or natural language query
Get the entity ID from search results
Fetch detailed data using the entity ID across multiple endpoints (bio, financials, investors, deals, etc.)

All endpoints require authentication via Authorization: Bearer {api_key} and X-Organization-ID headers.

People Search

People search accepts any natural language query and returns matching LinkedIn profiles with rich metadata. This is designed for AI agents that need to discover and enrich professional contacts.

Search returns previews. Enrichment endpoints return full data. This two-step design means you only pay for enrichment on the people you actually need, while search itself is nearly free.

Search

$ curl "https://api.runcaptain.com/v2/datasets/odyssey/people/search?q=engineers+at+Anthropic+in+San+Francisco&limit=10" \
>   -H "Authorization: Bearer YOUR_API_KEY" \
>   -H "X-Organization-ID: YOUR_ORG_ID"

Each result includes: name, headline, company, location, bio excerpt, LinkedIn follower count, school, and LinkedIn URL. Use the entity_id from any result to fetch full data.

Enrich

Once you have an entity_id, call /people/{entity_id}/bio to get the complete profile:

Profile: full name, headline, summary, location, current company and title
Contact: verified emails, phone numbers, and social profiles (LinkedIn, Twitter, GitHub, Facebook)
Work history: all positions with companies, titles, start/end dates, and durations
Education: schools, degrees, fields of study

One API call. Everything you need.

Pagination

People search supports limit (1-500) and offset parameters for paging through results:

Page 1: ?q=...&limit=20&offset=0
Page 2: ?q=...&limit=20&offset=20
Page 3: ?q=...&limit=20&offset=40

Check the has_more field in the response to know if additional pages are available. For requests above 100 results, the API automatically expands the search with query variations to discover more profiles and deduplicates by LinkedIn URL.

Example: Agent Workflow

A typical agent workflow for talent research:

Search: ?q=machine learning engineers at fintech companies in New York&limit=50
Filter results client-side by headline, school, or follower count
For each candidate of interest, call /bio to get their complete profile (contact, work history, education all in one call)
Use emails from the response for outreach

Data Dictionary

Every field available across Odyssey entity types, grouped by endpoint.

Company Profile

Returned by /companies/{id}/bio and /companies/{id}/full.

Field	Description	Example
`display_name`	Company name	`"Anthropic"`
`website`	Website URL	`"anthropic.com"`
`description`	Company summary	`"AI safety company building reliable..."`
`founded`	Year founded	`2021`
`employee_count`	Total employees	`1200`
`industry`	Industry classification	`"artificial intelligence"`
`company_type`	Private or public	`"private"`
`headquarters`	HQ location object	`{"city": "San Francisco", "state": "California", "country": "United States"}`
`size`	Size range	`"1001-5000"`
`headline`	Company tagline	`"AI safety company building reliable AI systems."`
`tags`	Sector tags	`["artificial intelligence", "machine learning"]`
`inferred_revenue`	Revenue estimate (private)	`"$500M-$1B"`
`ticker`	Stock ticker (public)	`"GOOG"`
`total_funding_raised`	Lifetime funding (USD)	`7300000000`
`latest_funding_stage`	Last round type	`"series_d"`
`last_funding_date`	Last round date	`"2024-03-01"`
`number_funding_rounds`	Total rounds	`6`
`linkedin_url`	LinkedIn URL	`"linkedin.com/company/anthropic"`
`twitter_url`	Twitter/X URL	`"twitter.com/AnthropicAI"`
`facebook_url`	Facebook URL	`"facebook.com/anthropic"`
`linkedin_follower_count`	LinkedIn followers	`250000`
`alternative_names`	Former names	`["Anthropic AI"]`
`alternative_domains`	Other domains	`["claude.ai"]`

Company Industries

Returned by /companies/{id}/industries.

Field	Description	Example
`naics_code`	NAICS code	`"541715"`
`naics_description`	NAICS description	`"R&D in Physical, Engineering, and Life Sciences"`
`sic_code`	SIC code	`"7372"`
`sic_description`	SIC description	`"Prepackaged Software"`

Company Financials

Returned by /companies/{id}/financials and /companies/{id}/financials/recent. Public companies use SEC EDGAR data; private companies get inferred estimates.

Field	Description	Example
`revenue`	Annual revenue (public, USD)	`14400000000`
`inferred_revenue`	Revenue range (private)	`"$500M-$1B"`
`net_income`	Net income (public, USD)	`640000000`
`assets`	Total assets (USD)	`4300000000`
`liabilities`	Total liabilities (USD)	`1900000000`
`fiscal_year`	Fiscal year	`2024`
`source`	Data source	`"sec_edgar"` or `"inferred"`

Company Funding

Returned by /companies/{id}/financing/recent. Each round in the rounds array contains:

Field	Description	Example
`total_raised`	Lifetime funding (USD)	`7300000000`
`num_rounds`	Number of rounds	`6`
`latest_stage`	Most recent stage	`"Series D"`
`last_funding_date`	Date of last round	`"2024-03-01"`
`rounds[].round_type`	Round type	`"Series D"`
`rounds[].amount`	Amount raised (USD)	`2750000000`
`rounds[].date`	Round date	`"2024-03-01"`
`rounds[].investors`	Investor names	`["Menlo Ventures", "Google"]`
`rounds[].valuation`	Valuation at round	`18400000000`

Company Debt

Returned by /companies/{id}/debt-financing/recent.

Field	Description	Example
`round_type`	Debt type	`"term_loan"`
`amount`	Debt amount (USD)	`500000000`
`date`	Date of debt round	`"2024-06-15"`
`lenders`	Lender names	`["JPMorgan Chase", "Goldman Sachs"]`
`currency`	Currency code	`"USD"`

Company Investors

Returned by /companies/{id}/active-investors and /companies/{id}/all-investors.

Field	Description	Example
`name`	Investor name	`"Menlo Ventures"`
`investor_type`	Type	`"venture_capital"`, `"corporate"`, `"individual"`
`website`	Investor website	`"https://menlovc.com"`
`description`	Brief description	`"Early and growth-stage VC firm"`

Company Deals

Returned by /companies/{id}/deals. Each deal in the deals array:

Field	Description	Example
`deal_type`	Deal type	`"funding_round"` or `"acquisition"`
`round_name`	Round name	`"Series D - Anthropic"`
`amount`	Deal amount (USD)	`2750000000`
`date`	Deal date	`"2024-03-01"`
`investors`	Investor names	`["Menlo Ventures", "Google"]`
`status`	Status	`"completed"`
`target_company`	Acquisition target	`null` (funding) or `"Acme Corp"`

Company Similar

Returned by /companies/{id}/similar.

Field	Description	Example
`name`	Competitor name	`"OpenAI"`
`website`	Website	`"https://openai.com"`
`similarity_score`	Similarity (0-1)	`0.92`

Company Service Providers

Returned by /companies/{id}/service-providers and /companies/{id}/service-providers/deal.

Field	Description	Example
`name`	Provider name	`"Wilson Sonsini Goodrich & Rosati"`
`service_type`	Service type	`"law"`, `"accounting"`, `"investment_bank"`
`role`	Role	`"legal counsel"`, `"placement agent"`

Returned by /companies/{id}/social-analytics.

Field	Description	Example
`linkedin_followers`	LinkedIn followers	`250000`
`employee_count`	Current headcount	`1200`
`employee_count_by_month`	Historical headcount	`{"2024-01": 800, "2025-01": 1100}`
`employee_growth_rate`	Growth rates	`{"6_month": 0.09, "12_month": 0.26}`
`employee_count_by_country`	By country	`{"United States": 1000, "United Kingdom": 100}`
`employee_count_by_role`	By role	`{"engineering": 600, "research": 250}`
`average_employee_tenure`	Avg tenure (years)	`1.8`
`employee_churn_rate`	12-month churn	`0.15`

Company Affiliates

Returned by /general/{entity_id}/affiliates.

Field	Description	Example
`name`	Affiliated entity	`"Anthropic UK"`
`relationship`	Relationship type	`"direct_subsidiary"`, `"parent"`

Person Search

Returned by /people/search. These are preview results - call /people/{id}/bio for full data.

Field	Description	Example
`entity_id`	Entity ID (use for enrichment)	`"ody_pe_019cb8ac-f123-..."`
`full_name`	Full name	`"Felix Rieseberg"`
`headline`	Professional headline	`"Member of Technical Staff at Anthropic"`
`company`	Current company	`"Anthropic"`
`location`	Location	`"San Francisco, California, United States"`
`linkedin_url`	LinkedIn URL	`"https://linkedin.com/in/felixrieseberg"`
`description`	Bio excerpt	`"Previously at Notion, Stripe, Slack..."`
`followers`	LinkedIn followers	`6000`
`school`	Notable school	`"University of Oxford"`

Person Bio (Full Profile)

Returned by /people/{id}/bio. Single call returns everything: profile, work history, education, and contacts.

Field	Description	Example
`full_name`	Full name	`"Felix Rieseberg"`
`headline`	Professional headline	`"Member of Technical Staff at Anthropic"`
`summary`	Bio/summary	`"Software engineer with deep experience..."`
`location`	Location object	`{"city": "San Francisco", "state": "California", "country": "United States"}`
`current_company`	Current employer	`{"name": "Anthropic", "title": "MTS", "start_date": "2024-01-01"}`
`linkedin_url`	LinkedIn URL	`"https://linkedin.com/in/felixrieseberg"`
`profile_image_url`	Photo URL	`"https://media.licdn.com/dms/image/..."`
`follower_count`	LinkedIn followers	`6000`
`connection_count`	LinkedIn connections	`2500`
`experience[]`	Work history	See below
`education[]`	Education history	See below
`emails`	Email addresses	`["felix@anthropic.com"]`
`phone_numbers`	Phone numbers	`["+1-415-555-0100"]`
`twitter_url`	Twitter/X URL	`"https://twitter.com/nicknisi"`
`github_url`	GitHub URL	`"https://github.com/nicknisi"`
`facebook_url`	Facebook URL	`null`

Experience (each entry in experience[]):

Field	Description	Example
`company`	Company name	`"Anthropic"`
`company_id`	Company entity ID	`"019cb8ac-adee-..."`
`title`	Job title	`"Member of Technical Staff"`
`location`	Job location	`"San Francisco, CA"`
`start_date`	Start date	`"2024-01-01"`
`end_date`	End date (null if current)	`null`
`is_current`	Current position	`true`
`duration_months`	Duration in months	`28`

Education (each entry in education[]):

Field	Description	Example
`school`	School name	`"University of Oxford"`
`degree`	Degree type	`"MA"`
`field_of_study`	Major/field	`"Computer Science"`
`start_date`	Start date	`"2012-09-01"`
`end_date`	End date	`"2016-06-01"`

Investor Profile

Returned by /investors/{id}/bio.

Field	Description	Example
`name`	Investor firm name	`"Sequoia Capital"`
`display_name`	Display name	`"Sequoia Capital"`
`investor_type`	Type and focus	`{"type": "vc", "stage_focus": ["seed", "series_a"], "sector_focus": ["enterprise"]}`
`founded`	Year founded	`1972`
`headquarters`	HQ location	`{"city": "Menlo Park", "state": "CA", "country": "USA"}`
`website`	Website	`"https://sequoiacap.com"`
`description`	Description	`"Premier venture capital firm"`
`total_investments`	Portfolio count	`1500`
`notable_portfolio`	Top companies	`["Stripe", "OpenAI", "Airbnb"]`

Investor Portfolio

Returned by /investors/{id}/active-investments and /investors/{id}/all-investments.

Field	Description	Example
`company_name`	Company	`"Stripe"`
`company_id`	Company entity ID	`"019cb8ac-adee-..."`
`deal_type`	Round type	`"Series A"`
`amount_invested`	Amount (USD)	`2000000`
`status`	Status	`"active"` or `"exited"`

Investor Preferences

Returned by /investors/{id}/preferences.

Field	Description	Example
`stages`	Preferred stages	`["seed", "series_a", "growth"]`
`sectors`	Preferred sectors	`["enterprise", "fintech"]`
`geographies`	Preferred regions	`["USA", "China", "India"]`
`check_size_min`	Min check size (USD)	`1000000`
`check_size_max`	Max check size (USD)	`100000000`

Investor Board Seats

Returned by /investors/{id}/board-seats.

Field	Description	Example
`company_name`	Company	`"Stripe"`
`board_member`	Board member name	`"Michael Moritz"`
`role`	Board role	`"Board Member"`

Deal

Returned by /deals/{id}/bio and /deals/{id}/detailed.

Field	Description	Example
`deal_id`	Unique deal ID	`"deal_anthropic_d"`
`company_name`	Company name	`"Anthropic"`
`company_id`	Company entity ID	`"019cb8ac-adee-..."`
`deal_date`	Deal date	`"2024-03-01"`
`deal_type`	Deal type	`"Series D"`
`amount`	Amount (USD)	`2750000000`
`currency`	Currency	`"USD"`
`num_investors`	Investor count	`4`
`lead_investors`	Lead investors	`["Menlo Ventures"]`
`status`	Status	`"completed"`

Deal Investors

Returned by /deals/{id}/investors.

Field	Description	Example
`name`	Investor name	`"Google"`
`entity_id`	Investor entity ID	`"019cb8ac-adee-..."`
`role`	Role in deal	`"lead"` or `"participant"`
`amount_invested`	Amount (USD)	`500000000`

Deal Valuation

Returned by /deals/{id}/valuation.

Field	Description	Example
`valuation_pre_money`	Pre-money valuation	`15650000000`
`valuation_post_money`	Post-money valuation	`18400000000`
`equity_percentage`	Equity sold	`14.9`

Fund

Returned by /funds/{id}/bio.

Field	Description	Example
`display_name`	Fund name	`"Sequoia Capital Global Growth Fund III"`
`website`	Website	`"https://sequoiacap.com"`
`industry`	Classification	`"Venture Capital"`
`employee_count`	Employees	`300`
`location`	HQ location	`{"locality": "Menlo Park", "region": "CA"}`

Fund Investments

Returned by /funds/{id}/active-investments and /funds/{id}/all-investments.

Field	Description	Example
`company_name`	Company	`"Stripe"`
`company_id`	Company entity ID	`"019cb8ac-adee-..."`
`investment_date`	Date	`"2021-03-15"`
`funding_round`	Round type	`"Series H"`
`status`	Status	`"active"`

Limited Partner

Returned by /limited-partners/{id}/bio.

Field	Description	Example
`display_name`	LP name	`"California Public Employees' Retirement System"`
`website`	Website	`"https://calpers.ca.gov"`
`industry`	Classification	`"Pension Fund"`
`employee_count`	Employees	`3000`
`headquarters`	HQ location	`"Sacramento, California"`

Service Provider

Returned by /service-providers/{id}/bio.

Field	Description	Example
`display_name`	Firm name	`"Wilson Sonsini Goodrich & Rosati"`
`website`	Website	`"https://wsgr.com"`
`industry`	Practice area	`"law practice"`
`employee_count`	Employees	`1800`
`location`	HQ location	`"Palo Alto, California"`
`description`	Description	`"Premier Silicon Valley technology law firm"`
`founded`	Year founded	`1961`

Patent

Returned by /patents/search and /patents/{id}.

Field	Description	Example
`patent_number`	Patent number	`"US11475380B2"`
`title`	Title	`"Methods for training ML models with reduced computation"`
`abstract`	Abstract	`"Systems and methods for training large-scale..."`
`assignee`	Assignee	`"Anthropic"`
`inventors`	Inventor names	`["Chris Olah", "Sam McCandlish"]`
`filing_date`	Filing date	`"2020-03-15"`
`publication_date`	Publication date	`"2022-10-18"`
`patent_url`	Google Patents URL	`"https://patents.google.com/patent/US11475380B2"`

Credit Analysis

Private credit market intelligence across news, agreements, BDC portfolios, fund formations, market data, and more.

News

Returned by /credit-analysis/news/search, /credit-analysis/news/recent, /credit-analysis/news/bulk, and /general/{entity_id}/news.

Field	Description	Example
`title`	Headline	`"Tesla Issues $1.8B in Investment-Grade Bonds"`
`url`	Article URL	`"https://bloomberg.com/news/..."`
`snippet`	Excerpt	`"Tesla sold $1.8 billion in investment-grade bonds..."`
`source`	Publication	`"Bloomberg"`
`published_date`	Date	`"2024-06-15"`
`category`	Category (credit)	`"bond_issuance"`, `"rating_change"`, `"default"`

Agreements

Returned by /credit-analysis/agreements/search and /credit-analysis/agreements/{company}.

Field	Description	Example
`company`	Borrower name	`"Acme Corp"`
`agreement_type`	Type of credit agreement	`"term_loan"`, `"revolving_credit"`
`amount`	Facility size	`500000000`
`date`	Agreement date	`"2024-03-15"`

BDC (Business Development Companies)

Returned by /credit-analysis/bdc/search, /credit-analysis/bdc/{ticker}/portfolio, and /credit-analysis/bdc/{ticker}/stats.

Field	Description	Example
`ticker`	BDC ticker symbol	`"ARCC"`
`name`	BDC name	`"Ares Capital Corporation"`
`portfolio`	Portfolio holdings	Array of investment positions
`nav`	Net asset value	`18.45`

Borrowers

Returned by /credit-analysis/borrowers/{name}.

Field	Description	Example
`name`	Borrower name	`"Acme Corp"`
`facilities`	Active credit facilities	Array of loan/credit agreements
`total_debt`	Total outstanding debt	`750000000`

Funds

Returned by /credit-analysis/funds/search, /credit-analysis/funds/formations, and /credit-analysis/funds/managers/{name}.

Field	Description	Example
`fund_name`	Credit fund name	`"Apollo Credit Fund IX"`
`manager`	Fund manager	`"Apollo Global Management"`
`target_size`	Target fund size	`5000000000`
`strategy`	Investment strategy	`"direct_lending"`, `"distressed"`

Market

Returned by /credit-analysis/market/overview, /credit-analysis/market/spreads, and /credit-analysis/market/lending-standards.

Field	Description	Example
`metric`	Market metric name	`"leveraged_loan_spread"`
`value`	Current value	`425` (bps)
`period`	Reporting period	`"2024-Q4"`
`trend`	Directional trend	`"widening"`, `"tightening"`

N-PORT

Returned by /credit-analysis/nport/search, /credit-analysis/nport/funds, and /credit-analysis/nport/{ticker}/filings.

Field	Description	Example
`ticker`	Fund ticker	`"PIMIX"`
`filing_date`	N-PORT filing date	`"2024-09-30"`
`holdings`	Portfolio holdings from filing	Array of positions with fair values

Relationships

Returned by /credit-analysis/relationships/search and /credit-analysis/relationships/portals.

Field	Description	Example
`entity`	Entity name	`"Goldman Sachs"`
`role`	Role in relationship	`"agent"`, `"lead_arranger"`, `"lender"`
`counterparties`	Related entities	Array of connected parties

SBA (Small Business Administration)

Returned by /credit-analysis/sba/search and /credit-analysis/sba/stats.

Field	Description	Example
`lender`	SBA lender name	`"Live Oak Banking Company"`
`program`	SBA program	`"7(a)"`, `"504"`
`loan_count`	Number of loans	`1250`
`total_amount`	Total lending volume	`450000000`