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.
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.