LabelSets is a small operation focused on one thing: making AI training-data quality cryptographically verifiable so it stops being a trust exercise. The LQS standard, the marketplace, the SDK, the open methodology — all of it serves that one goal.
Every other category has a third-party rating you can cite in regulated paperwork. Bonds have Moody's. Cyber has SOC 2. Cloud has ISO 27001. AI training data had a README and a vibe check. We built the rating that fills the gap.
Closed scoring is a black box no auditor accepts. The LQS specification, the 19 dimensions, the oracle math, and the conformal-prediction layer are all published. The cert is signed; you can verify it offline against our public key without ever calling our API.
If we can't justify a number, we don't make one up. Conformal intervals widen when calibration data is thin. The license suggester refuses to suggest when blockers are present. We'd rather say "we don't know yet" than burn trust the first time a claim is wrong.
Algorithms get copied in 6 weeks. The Outcome Registry — every buyer's downstream eval result tied back to the dataset and signed cert — compounds for years. We're building Moody's: not the smartest, the most-data-in-one-place.
If you're a procurement team or auditor, every artifact you'd need is on this site, public, no signup. The SDK runs offline cert verification. The methodology paper is downloadable. Reach us by email; we reply within a business day, in writing your audit team can quote.
Anything we ship is dated and citable. No "coming soon" boxes that never resolve.