Review less.
Keep errors under your cap.
We find which of your AI's answers can go out without a human check, prove on your own data, with 95% confidence, that errors among them stay under the cap you set, and say so when your data can't carry it yet.
Pilots run in shadow mode. Your current review stays in place.
Checking everything eats the savings
In a global survey published in 2025, about a quarter of respondents whose companies use generative AI said people review everything it produces before use.[F1] That is safe, but slow and expensive. Letting more through unchecked is cheaper, but then nobody can say how many errors get out. That is also what an auditor, a regulator or a customer will ask.
We answer it on your own data, with the confidence and the conditions spelled out.
Listen, prove, then run with a watch
Listen
We connect next to your current review, in shadow mode. Every case still goes to a reviewer, who records a verdict on the AI's original answer; those verdicts become the answer key.
Prove
From your own reviewed cases we work out how much can go out without review while errors among those cases stay under your cap, with 95% confidence.[F2] If your data can't carry the cap yet, we say so, and estimate how many more reviewed cases it would take.
Run with a watch
Cases scored below the calibrated threshold go out; the rest go to people. A watch checks a random audit sample of released cases and the mix of risk scores, and hands decisions back to people when either moves past its alarm line. Every decision gets a signed receipt that your auditor can check offline.
A number, a proven limit and a record
Your number
The share of cases that can go out without review at your cap. Or an honest “not yet”, with what it would take.
A proven limit
Calibrated on your reviewed history: errors among the cases released without review stay under your cap with 95% confidence, as long as traffic stays like that history.
A watch
When audited errors rise or the score mix shifts, decisions go back to people until the cap is proven again. A rise in errors that leaves the scores unchanged shows up only in the audits, so the audit rate sets how fast it is caught.
Receipts
A signed, chained record of every decision. With our public key, your auditor can check offline that the records are authentic and the chain is unbroken. The check confirms who signed what, not that each answer was right.
What it looks like
Customer-support ticket routing on open data, with a simplified stand-in routing model and a 5% error cap.[F3]
Until the alarm, released tickets carried the updated model's higher error rate. After recalibration only 10% could be released, so the rest stayed with people. These are demo numbers, not customer results. Your number is measured in a pilot on your own flow.
An 8-week pilot on your own flow
Weeks 0–2: we connect and agree what counts as an error. Weeks 2–8: shadow mode next to your current review. At the end: your number, what the watch saw, and a plan for going live.
How much of your flow can skip review?
Fifteen minutes of demo, then a pilot on your own flow.