Reliability for production models

Don't just detect drift.Decide.

Your model still returns a number when the world underneath it changes. Kirelta watches the data going in, and tells you when the answers coming out stop being trustworthy — with the evidence, and a recommendation you can act on.

Start free See a real decision

Free plan · 2 models · no card

✗

UNTRUSTED

fraud-model · from a verified engine run

flagged

100.0%

alarm

YES

points

Recommendation

Block / escalate to a human. The inputs moved well outside the range this model was trained on. Features #0 and #2 carry most of the deviation.

Real engine output — not a mockup Open it →

The problem isn't detection. It's the decision.

Most monitoring tools tell you something changed. Then they hand you a chart and leave. The engineer on call still has to work out whether it matters and what to do — usually at 3am.

Traditional monitoring

A metric crossed a threshold
Here is a dashboard of charts
You decide if it's real
You decide if it matters
You decide what to do
Tests re-run on every batch, so false alarms pile up over time

Kirelta

A verdict: trusted, degraded, or untrusted
The evidence behind it, in plain fields
Which features carry the deviation
A recommended action
Whether it's already recovering
One false-alarm budget for the whole run — not per batch

Three calls

Kirelta doesn't need your model, your code, or your raw records. It needs the same feature vectors you already compute.

01 / FIT

Show it healthy data

Send rows from a window when the model was working. Kirelta learns that shape and picks its own monitor for it — you don't choose an algorithm.

02 / ASSESS

Send recent rows

The batch you want a call on. Kirelta compares it to the baseline and answers with a verdict, an action, and the evidence.

03 / DECIDE

Branch on the verdict

Serve, flag, or hold. One if statement in the code you already have.

What we will and won't claim

The drift alarm is anytime-valid. Its false-alarm probability is bounded across the entire run, not per batch. Tools that re-run a fresh test on every window accumulate false alarms the longer you watch — this is a real mathematical difference, and it's why Kirelta can keep watching without becoming noise. We measured it rather than just asserting it: across 300 clean batches on a verified synthetic baseline, the alarm fired 1.7% of the time against the ≤ 5% bound — and caught a one-sigma shift in 60/60 batches.

On its own, it cannot tell you your model's accuracy dropped. Kirelta watches the inputs by default, not the outcomes — if the world changed in a way that leaves your input distribution untouched, the verdict above will not see it, and it will say so rather than invent a number. Send it your model's predictions too and that changes: a second, independent signal checks whether the model's own behaviour has moved, which is exactly how it catches a model that has quietly stopped behaving like itself while its inputs still look ordinary — the layer is optional and documented here.

The engine's own documentation lists where it wins and where a simpler method beats it. We'd rather you trust the verdicts you do get.