Know exactly how much to trust your AI's answer.
Turn a confidence score into a guarantee — right at least 95% of the time — so you know exactly where to draw the line.
Works with any model — OpenAI, Anthropic, Gemini, or self-hosted. No retraining required.
- α
- The error level you choose (α, δ, or a risk, FDR, or cost target), stated on the guarantee card next to the calibration size behind it.
- 9
- Backends behind one interface: six closed APIs, two self-hosted engines, and your own model.
- 1
- Assumption: calibration and production data are exchangeable. Nothing about the model itself.
The problem
Confidence scores don’t carry a warranty
A number derived from a softmax can look reasonable in aggregate and still be wrong on the traffic you care about, with nothing to tell you so.
It does not travel.
A score learned inside one model says nothing about another vendor, a new model version, or the self-hosted fallback you switch to during an outage.
It has no error bars.
No sample size, no interval on the calibration itself, and no statement about the error rate of the decisions you actually automate.
It goes stale in silence.
When a model alias moves or the serving stack changes, a hand-picked threshold keeps being applied as if nothing happened.
Explore
One statistics layer, six ways to use it
Everything below lives on its own page, so you can go straight to what you need.
Three statistical engines
Conformal prediction, Venn-Abers calibration, and e-values — pick the shape of guarantee your decision needs.
See the engines →Seven typed primitives
Ask for a Set, a Gate, a Judge, or any of seven guarantee-bearing answers from a single SDK call.
See the primitives →Where it's used today
Support routing, RAG claim filtering, high-stakes approvals, cost routing, and drift monitoring.
See the use cases →Guard every agent action
Plug into LangChain, LangGraph, Google ADK, or Microsoft Agent Framework in a few lines.
See the agent guide →Not a confidence score
See exactly what changes when a heuristic number becomes a statement you can defend in review.
See the comparison →Bring your own model
Pay for calibration and monitoring, not for tokens you already buy from your model provider.
See pricing →Calibrate the model you already have.
Start with the Python SDK against the hosted API, or run the statistics engine entirely offline.