Know it will fail 24 hours before it does.
Most AI failures announce themselves in advance: retries tick up, latency drifts, an upstream API gets flaky. Predictive Failure reads those signals continuously and scores every workflow’s probability of failing in the next 24 hours — so you fix things on your schedule, not at 2am.
The problems that pile up quietly.
Failures arrive as pager alerts at the worst possible time, when all the warning signs were visible for days.
Rising retry counts and creeping latency are invisible until they cross the cliff edge.
Your team is permanently reactive — firefighting instead of preventing.
What changes for your team.
A 24-hour head start
FAILS <24H alerts arrive while the workflow is still passing, with the leading indicators that triggered the prediction — so the fix ships before the failure does.
Fewer 2am pages
Teams using predictive alerts convert the majority of would-be outages into planned daytime fixes.
Risk you can rank
The 0–100 risk index across your whole estate tells you where to spend hardening effort — the 83 gets attention, the 11 doesn’t.
Live in minutes, not sprints.
Learn each workflow’s normal
Error rate, latency distribution, retry patterns, and upstream dependency behavior are baselined per workflow.
Score risk continuously
Every execution updates the failure-risk index. Trend acceleration matters more than absolute values — a fast-moving 40 beats a stable 60.
Warn inside the window
When the model projects failure within 24 hours, you get an alert with the evidence: which signals moved, and what to check first.
See it on your own AI stack.
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