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.
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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.
Kirelta doesn't need your model, your code, or your raw records. It needs the same feature vectors you already compute.
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.
The batch you want a call on. Kirelta compares it to the baseline and answers with a verdict, an action, and the evidence.
Serve, flag, or hold. One if statement in the code you already have.
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.
It cannot tell you your model's accuracy dropped. Kirelta watches the inputs, not the outcomes. If the world changed in a way that leaves your input distribution untouched, Kirelta will not see it — and it will say so rather than invent a number.
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.
The demo console runs the engine on a verified dataset — the numbers you see are the numbers it produced.