SourceScore

Verified claim · AI-ML · 100% confidence

Runway ML founded in: 2018 — AI creative tools and video generation.

Last verified 2026-05-16 · Methodology veritas-v0.1 · 8b05095324238a8b

SourceScore rates how reliable a source is to cite — for AI answers and research. This is one verified claim from the catalog.

Structured fields

Subject
Runway ML
Predicate
founded_in
Object
2018 — AI creative tools and video generation
Confidence
100%
Tags
runway · runwayml · video-generation · creative-ai · company · founded · 2018

Sources (2)

  1. [1] official blog · Runway · 2018-01-01

    About Runway
    Runway is an applied AI research company building the next era of art, entertainment, and human creativity. Founded in 2018, Runway is based in New York City.
  2. [2] docs · Crunchbase · 2018-01-01

    Runway — Crunchbase company profile

Cite this claim

Ready-to-paste citation (Markdown / plain text):

Runway ML founded in: 2018 — AI creative tools and video generation. — SourceScore Claim 8b05095324238a8b (verified 2026-05-16). https://sourcescore.org/api/v1/claims/8b05095324238a8b.json

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Frequently asked questions

Is the claim "Runway ML founded in: 2018 — AI creative tools and video generation." 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 "Runway ML founded in: 2018 — AI creative tools and video generation."?

Evidence comes from 2 primary sources: Runway, Crunchbase. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/8b05095324238a8b.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/8b05095324238a8b.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.

Use this claim in your code

Fetch this signed envelope from your application. The response includes the verbatim excerpt, primary source URLs, and an HMAC-SHA256 signature you can verify locally for audit trails.

cURL

curl https://sourcescore.org/api/v1/claims/8b05095324238a8b.json

JavaScript / TypeScript

const r = await fetch("https://sourcescore.org/api/v1/claims/8b05095324238a8b.json"); const envelope = await r.json(); console.log(envelope.claim.statement); // "Runway ML founded in: 2018 — AI creative tools and video generation."

Python

import httpx r = httpx.get("https://sourcescore.org/api/v1/claims/8b05095324238a8b.json") envelope = r.json() print(envelope["claim"]["statement"]) # "Runway ML founded in: 2018 — AI creative tools and video generation."

LangChain (retrieve-then-cite)

from langchain_core.tools import tool import httpx @tool def get_runway_ml_fact() -> dict: """Fetch the verified SourceScore claim for Runway ML.""" r = httpx.get("https://sourcescore.org/api/v1/claims/8b05095324238a8b.json") return r.json()
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