Proof of Quality
A consensus and attestation system for subjective data. Every AI builder asks the same questions about the data they train on, ship, or act on: who reviewed this, and can the review be trusted? PoQ produces a permanent, auditable answer for every item.
What is Proof of Quality?
Give it any expert output:
- a radiologist's diagnosis
- a security auditor's finding
- a data labeler's annotation
- a model's chain-of-thought
PoQ then sends that output to a panel of independent experts.
- Score: each expert grades it against your rubric.
- Consensus: their scores collapse into one score per item.
- Attest: that score is sealed as a permanent cryptographic record.
How it works
Where PoQ fits
PoQ is the consensus and provenance layer over expert outputs - so it fits anywhere in your pipeline where you need confidence in an expert's work. The shape is the same regardless of context: a credentialed network reviews each item against your rubric, economically aligned consensus produces a single per-item score, and the result is a portable cryptographic proof.
It sits alongside internal QC on labels, datasets, or agent outputs - upgrading ad-hoc review with structured consensus and a per-item attestation. It layers over eval tooling (Braintrust, Langfuse, Arize) for the cases where confidence matters. It addsprovenance to labeling work (Scale, Surge, Sama, in-house) so a dataset ships with a citable proof. And it stamps consequential AI decisions - an agent's tool call, a model's chain-of-thought, an audit firm's AI-generated finding - with a per-item attestation a downstream party can verify.
The tools you already use stay where they are. PoQ is the consensus and provenance layer over whatever they produce - and some of those vendors will be PoQ customers themselves.