> For a complete page index of the Captain API documentation, fetch https://docs.runcaptain.com/llms.txt?excludeSpec=true

# Odyssey - Private Market Intelligence

> Odyssey is Captain's private market intelligence dataset API. Search and retrieve data on companies, investors, people, deals, funds, limited partners, service providers, patents, and credit analysis via v2 endpoints.

## Agent Quick Reference - Odyssey API

* **Base path**: `/v2/datasets/odyssey/`
* **Auth**: Bearer token + X-Organization-ID header
* **Workflow**: Search by name/keyword/NL query → get entity ID → fetch detailed data by ID

### Entity endpoints

* Companies: `/v2/datasets/odyssey/companies/search`, `/{company_id}/full`, `/{company_id}/bio`, `/{company_id}/financials`, `/{company_id}/deals`, `/{company_id}/active-investors`, `/{company_id}/similar`, etc.
* People: `/v2/datasets/odyssey/people/search` (supports NL queries, pagination via `limit` + `offset`, up to 500 results), `/{person_id}/bio` (returns everything: profile, contact, work history, education in one call)
* Investors: `/v2/datasets/odyssey/investors/search`, `/{id}/bio`, `/{id}/active-investments`, `/{id}/preferences`, `/{id}/funds`, `/{id}/board-seats`
* Deals: `/v2/datasets/odyssey/deals/search`, `/{id}/bio`, `/{id}/detailed`, `/{id}/investors`, `/{id}/valuation`, `/{id}/cap-table`
* Funds: `/v2/datasets/odyssey/funds/search`, `/{fund_id}/bio`, `/{fund_id}/performance`, `/{fund_id}/active-investments`, `/{fund_id}/cash-flows`
* Limited Partners: `/v2/datasets/odyssey/limited-partners/search`, `/{lp_id}/bio`, `/{lp_id}/commitments/detailed`, `/{lp_id}/allocations/actual`
* Service Providers: `/v2/datasets/odyssey/service-providers/search`, `/{provider_id}/bio`, `/{provider_id}/companies`, `/{provider_id}/deals`
* Patents: `/v2/datasets/odyssey/patents/search`, `/{id}`, `/{entityId}/file`
* Credit Analysis: `/v2/datasets/odyssey/credit-analysis/news/search`, `/news/recent`, `/news/{newsId}`, `/agreements/search`, `/bdc/search`, `/bdc/{ticker}/portfolio`, `/borrowers/{name}`, `/funds/search`, `/funds/formations`, `/funds/managers`, `/market/overview`, `/market/spreads`, `/market/lending-standards`, `/nport/search`, `/nport/funds`, `/relationships/search`, `/sba/search`
* General Search: `/v2/datasets/odyssey/general/search`, `/general/{entity_id}/people`, `/general/{entity_id}/news`

### Feed endpoints (firehose)

* Event feed: `GET /v2/datasets/odyssey/feed` is a global reverse-chronological stream of company events (`type`: funding, acquisition, ipo, exec\_change, layoff, partnership, shutdown, product\_launch). Optional filters: `types` (comma-separated), `sector`, `min_amount`. Each event carries `company.entity_id` (plus `company.industry`, `company.location`).
* Publication feed: `GET /v2/datasets/odyssey/publications/feed` is a global stream of articles from \~130 publications. Items carry `enriched: bool` and `detail_url`; `companies_mentioned` is NOT on the feed listing. Call `GET /publications/articles/{id}` to get `companies_mentioned[].entity_id`. Optional filters: `publications` (comma-separated, plus singular `publication`), `sector`, `has_entities`, `title_only`/`fields`.
* Both default to the full global stream with no params. Cursor pagination: start with `since` (ISO-8601 UTC), then pass the returned `next_cursor` back as `cursor` until `has_more` is false; while `cursor` is set, `since` is ignored. `page_size` 1 to 500 (default 40). `title_only=true` (alias for `fields=ids`) on the publication feed returns a minimal payload (id, title, published\_at) for high-frequency polling. For watchlist-style "tell me when this company shows up" alerts, use webhook subscriptions (see the Live Feeds & Webhook Alerts page).

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

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

All endpoints require authentication via an `Authorization: Bearer {api_key}` header. `X-Organization-ID` is optional: include it only when your key is not already scoped to an organization.

## 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

```bash
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" \
```

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:

1. Search: `?q=machine learning engineers at fintech companies in New York&limit=50`
2. Filter results client-side by headline, school, or follower count
3. For each candidate of interest, call `/bio` to get their complete profile (contact, work history, education all in one call)
4. 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"`       |

### Company Social Analytics

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`                  |

## Feeds

Most Odyssey endpoints answer "tell me about *this* entity." The two **feed** endpoints invert that: they stream **everything** in reverse-chronological order and let you decide what matters. This is the firehose pattern: scan the stream rather than search for a specific item.

The event feed is directly entity-linked: each event's subject company resolves to an Odyssey `entity_id`. The publication feed works differently: the listing tells you which articles exist and whether they've been enriched. Call `GET /publications/articles/{id}` to get `companies_mentioned` with resolved entity IDs.

| Feed                 | Endpoint                                     | Streams                                                                                                                                           |
| -------------------- | -------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Event feed**       | `GET /v2/datasets/odyssey/feed`              | Structured company events: funding, acquisitions, IPOs, exec changes, layoffs, partnerships, shutdowns, product launches                          |
| **Publication feed** | `GET /v2/datasets/odyssey/publications/feed` | Articles from \~130 tracked publications. `enriched: true` items have companies resolved; call the article endpoint to get `companies_mentioned`. |

Both feeds page the same way: open a window with `since`, then pass `next_cursor` back as `cursor` until `has_more` is `false`. The publication feed is a two-step read: poll the feed for titles, then call the article endpoint for the companies mentioned in an article.

Full request and response schemas, paging code, the article detail and body endpoints, and webhook alerts are on the [Live Feeds & Webhook Alerts](/datasets/odyssey-feeds) page.