AI agents hallucinate B2B data because they have no ground truth. I built one. Nopp's Entity Graph pulls from NPPES practitioner data, 40+ state licensing boards, ODO corporate registries, and regulatory filings. Nothing scraped. Nothing inferred. Every entity is deterministically verified. The API surface I want you to stress-test: Entity Graph

POST /api/v1/identify — name, NPI, or LinkedIn URL → verified canonical entity (10 IC) GET /api/v1/people/:id — fetch a resolved person record (3 IC) POST /api/v1/people/search — filter 12M+ licensed professionals by state, city, credential (10 IC) GET /api/v1/organizations/:id — fetch an org record (3 IC)

Intelligence

POST /api/v1/company/enrich — live enrichment, falls back gracefully, never returns empty (10 IC) POST /api/v1/contact/enrich — contact-level deep enrichment (3 IC)

Signals

POST /api/v1/signals/hiring — detect hiring surges (15 IC) POST /api/v1/signals/funding — funding round detection (15 IC) POST /api/v1/signals/intent — competitor frustration + intent signals (20 IC)

Agents

POST /api/v1/agent/launch/companies — autonomous research + outreach in one call (50 IC)

Also ships as an MCP Server — connect Claude, ChatGPT, or any MCP-compatible agent directly. No API calls, no plumbing. What I want from you:

Feed /identify ambiguous or conflicting inputs — same person, two LinkedIn URLs, different name spellings Try to get /company/enrich to return empty or wrong data Test /signals/intent against a company you know well — does it catch real signals? Tell me where the schema is wrong, underdocumented, or just annoying to work with

Find a reproducible bug or a real schema issue → I'll give you free Intelligence Credits. No cap. nopp.us/api-docs | nopp.us/developers I'm the founder. I'll reply to every technical comment personally.

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