Reviews every answer
An independent layer reads each model output and forms its own judgment — separate from the model that produced it.
Independent governance layer
LLMs answer with confidence. Not all answers are right.
QSI is an independent layer that reviews every answer — and flags the ones likely to be wrong.
Know which outputs to trust — and which to hold back.
For agentic coding, subagents do the work. QSI judges the results — and surfaces what needs a human.
QSI — the quality gate for AI you can put in production.
A model will hand you a wrong answer with the same fluency as a right one. There is no stack trace, no exception — just confident prose. QSI is the layer that tells you which outputs to trust.
An independent layer reads each model output and forms its own judgment — separate from the model that produced it.
Outputs that are probably incorrect are surfaced with a calibrated confidence, before they reach a user.
QSI never blocks inference. If it is unsure or unavailable, the answer flows through — governance, not a gate that breaks.
A plausible, confidently-stated answer — and an independent review that flags it before it reaches a user. Toggle the examples; one is correct, two contain a planted error.
A photon and an electron have the same wavelength. Which has greater momentum?
The electron has greater momentum, because it has mass and the photon is massless, so the electron must carry more momentum at equal wavelength.
From 7B open models to frontier systems — Kimi-K2, DeepSeek-V4, GLM-5.1, Qwen3, Gemma, Llama. QSI separates correct from incorrect answers with AUC 0.86–0.96.
QSI runs alongside inference, off the critical path. It observes every answer and flags the risky ones — but if it is ever unsure or unavailable, the answer flows through untouched. Governance you can put in production without adding a new way to fail.
How it works →Catch the mistakes weaker and specialized models make — error rates of 40–80% on hard domains — surfaced before they reach your users.