Remedy AI root cause analysis across the whole incident trail.
The visible symptom may be frontend. The cause may sit in an API, worker, database query, container or recent deployment.
Software failures cross boundaries
A user may report a frozen screen while the real failure is an expired backend dependency. An API may return 500 because of a database lock rather than controller logic. Remedy is designed to correlate only the relevant authorised evidence across layers.
From correlation to a repairable hypothesis
Root cause analysis should produce a specific explanation tied to source or operational state and a way to test it. Remedy can use runtime, repository and deployment context to narrow the failure path before repair begins.
Deterministic evidence challenges the AI
AI reasoning is a hypothesis generator, not proof. Reproduction steps, unit and integration tests, health assertions and production probes determine whether the explanation and repair survive contact with the system.
Frequently asked questions
Is this traditional AIOps root cause analysis?
Remedy is narrower: it focuses on the evidence required to understand, repair and verify a software defect rather than acting as a general telemetry warehouse.
Can it correlate deployment regressions?
The platform is designed to use deployed source identity and configured evidence so recent software changes can be evaluated as part of an incident.
From bug report to verified fix.
See how Remedy connects evidence, diagnosis, repair, deterministic checks and production verification.
Explore the complete workflow →