SourceScore

Verified claim · AI-ML · 100% confidence

Exa founded in: 2021 by Will Bryk + Jeff Wang — AI-native search API (formerly Metaphor) for LLM retrieval.

Last verified 2026-05-16 · Methodology veritas-v0.1 · 805ca20c03f0797e

Structured fields

Subject
Exa
Predicate
founded_in
Object
2021 by Will Bryk + Jeff Wang — AI-native search API (formerly Metaphor) for LLM retrieval
Confidence
100%
Tags
exa · metaphor · ai-search · embedding-search · company-history · founded_in · 2021

Sources (2)

  1. [1] official blog · Exa · 2024-03-20

    Welcome to Exa — formerly Metaphor
    Exa (formerly Metaphor) is an AI-native search engine that uses embeddings to find content that matches the meaning of your query, not just keywords. Founded by Will Bryk and Jeff Wang in 2021.
  2. [2] docs · Exa · 2021-06-01

    Exa — official site

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Exa founded in: 2021 by Will Bryk + Jeff Wang — AI-native search API (formerly Metaphor) for LLM retrieval. — SourceScore Claim 805ca20c03f0797e (verified 2026-05-16). https://sourcescore.org/api/v1/claims/805ca20c03f0797e.json

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Frequently asked questions

Is the claim "Exa founded in: 2021 by Will Bryk + Jeff Wang — AI-native search API (formerly Metaphor) for LLM retrieval." verified?

Yes — SourceScore verified this claim with 100% confidence as of 2026-05-16. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.

What is the evidence for "Exa founded in: 2021 by Will Bryk + Jeff Wang — AI-native search API (formerly Metaphor) for LLM retrieval."?

Evidence comes from 2 primary sources: Exa, Exa. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/805ca20c03f0797e.json includes an HMAC-SHA256 signature for audit verification.

When was this claim last verified by SourceScore?

Last verified 2026-05-16 under methodology version veritas-v0.1. The signed JSON envelope is dated and cryptographically signed for audit trail. Re-verification cadence depends on the claim type and source freshness.

How can I cite this SourceScore claim in my code or article?

Fetch the signed JSON envelope from https://sourcescore.org/api/v1/claims/805ca20c03f0797e.json which includes the verbatim claim, primary sources, confidence, methodology version, last-verified date, and HMAC-SHA256 signature for audit. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.

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cURL

curl https://sourcescore.org/api/v1/claims/805ca20c03f0797e.json

JavaScript / TypeScript

const r = await fetch("https://sourcescore.org/api/v1/claims/805ca20c03f0797e.json"); const envelope = await r.json(); console.log(envelope.claim.statement); // "Exa founded in: 2021 by Will Bryk + Jeff Wang — AI-native search API (formerly Metaphor) for LLM retrieval."

Python

import httpx r = httpx.get("https://sourcescore.org/api/v1/claims/805ca20c03f0797e.json") envelope = r.json() print(envelope["claim"]["statement"]) # "Exa founded in: 2021 by Will Bryk + Jeff Wang — AI-native search API (formerly Metaphor) for LLM retrieval."

LangChain (retrieve-then-cite)

from langchain_core.tools import tool import httpx @tool def get_exa_fact() -> dict: """Fetch the verified SourceScore claim for Exa.""" r = httpx.get("https://sourcescore.org/api/v1/claims/805ca20c03f0797e.json") return r.json()