Checks the work is really done
QSI reads the agent’s plan and the structured signals of what it actually did — not your prompts or content — and catches a “done” that skipped a required step or its evidence.
Completion integrity for AI agents
Your agent said “Done.” It wasn’t. We catch that.
QSI is an independent checkpoint between your AI agent and what it does next. It catches a false “done” and a likely-wrong answer before they turn into an action — and it fails open, so it never becomes a new way for your system to break.
The problem
AI agents fail quietly.
An AI agent will say “done” when the work isn’t finished, and answer with full confidence when it’s wrong. There’s no crash and no error — just a result that looks fine and isn’t. QSI is the independent check that tells you which results to trust before they reach a user or trigger the next step.
Flags answers likely to be wrong
A separate review reads the final answer and surfaces the ones that are probably incorrect, with a confidence score, before they ship.
Fails open by design
If QSI is ever unsure, late, or unavailable, the output flows through untouched — a checkpoint, never a new single point of failure.
The difference
The same answer — with and without a safety net.
A wrong answer ships with the same confidence as a right one. No flag, no stack trace — it just reaches your user, or runs in your pipeline as if it were correct.
Every output is reviewed by an independent layer and the likely-wrong ones are flagged — with a calibrated confidence — before they ship. If QSI is ever unsure or unavailable, the answer still flows through.
0.83 AUC separating right from wrong · 5 domains · n=787
0.86–0.96 per-model on hard banking questions · n=200 each
fail-open fails open — never the single point of failure
See it in action
See QSI catch a confident wrong answer.
Pick an answer below. QSI shows its read — highlighting the parts it trusts least, attaching a verdict and a confidence, and saying plainly why. Some answers are right; some are confidently wrong.
The answer under review
The electron has greater momentum, because it has mass and the photon is massless, so the electron must carry more momentum at equal wavelength.
FLAGGEDWrong. At the same wavelength both carry the same momentum — momentum is set by the wavelength, not by whether the particle has mass. The "electron wins because it has mass" reasoning is the giveaway.
confidence91%
Try it
Run a query.
Type a question. QSI returns the model answer plus a verdict and a confidence indicator — the same reliability read it attaches in production.
Model-agnostic
One detector, every model we tested.
Across five domains and ten models (n=787), QSI separates correct from incorrect answers at AUC 0.83. On the hardest single domain — banking — it reaches 0.86–0.96 per model across 12 models spanning the DeepSeek, Kimi, GLM, Qwen, Gemma, Llama and Mistral families. Real models, real runs.
sciencemathmedicinecodegeneral
frontier open weightAUC · per model on hard banking questions (n=200 each) · 0.5 = chance
Posture
A sidecar, never a gate that breaks.
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 → FAQ
Questions, answered plainly.
What is an LLM quality gate?
A quality gate is a layer that checks AI output for reliability before it reaches a user or a downstream system. QSI reviews every answer your model produces, flags the ones that are likely wrong, and lets the trustworthy ones through — so you ship AI with a safety net instead of hoping every answer is right.
How does QSI catch unreliable AI output?
QSI acts as an independent reviewer that forms its own judgment about each answer, separate from the model that produced it. When an answer looks likely to be wrong, QSI surfaces it with a calibrated confidence so your team and your systems know exactly which results to hold back — all without changing your model.
Is QSI model-agnostic?
Yes. QSI works across open-weight and frontier models alike — from small models to the largest systems — and requires no changes to the model itself. It reads the reliability of any model output, not a model it was tuned for.
What does fail-open mean?
Fail-open means QSI can never take your product down. It runs alongside inference, off the critical path. If QSI is ever unsure or unavailable, the answer flows through untouched. You add a layer of judgment without adding a new way to fail.
Does QSI work with agentic coding agents like Cursor?
Yes. When an orchestrator fans work out to subagents that write code, run tests, and edit files, QSI reviews each result independently — flagging the confident-but-wrong edits that need a human before they merge, so you keep the speed of agents without inheriting their blind spots.
Will QSI slow my app down?
No. QSI is a low-latency sidecar that runs in parallel with inference, not in front of it. Your user-facing path is never gated by QSI being fast, available, or certain, so adopting it adds judgment without adding latency to the answers your users see.
QSI — the quality gate for AI you can put in production.
Catch the mistakes weaker and specialized models make — error rates of 40–73% on hard domains — surfaced before they reach your users.