Know when your AI is failing before your customers do.
Reliai monitors AI agents, RAG systems, and model behavior to detect regressions, surface incidents, and prove reliability in production.
Pulse dashboard preview
AREI · Incidents · Actions
Pulse capabilities in production
Detect failures early
Reliai continuously tracks AI behavior and surfaces reliability regressions before they become customer-visible incidents.
- Regression detections
- Failed evals
- High-risk outputs
Investigate incidents
When reliability degrades, Reliai groups signals into incidents and links each incident to the traces and failure patterns driving it.
- Incident grouping
- Trace-level visibility
- Root cause context
Understand what changed
Pulse highlights recent changes so teams can connect deployments, model behavior shifts, and regressions in one operational view.
- Reliability timeline
- Deployment correlation
- Drift detection
Take action
Pulse converts reliability signals into clear next steps, with guardrail posture and certification readiness visible in the same workflow.
- Recommended actions
- Guardrails and controls
- Audit readiness
Core signal
AI Reliability Exposure Index (AREI)
AREI is a 0–100 reliability exposure score. Higher scores indicate greater production exposure. It is built from traces, incidents, audits, and deployments.
Example: Your score is 78 because failed evals increased after your last deployment and 3 incidents remain unresolved.
AREI breakdown
Built for teams operating AI in production
Reliai is used for AI copilots, RAG search systems, and agent workflows where reliability, incident response, and production risk posture need to be visible in real time.
Start with audit or live Pulse preview
Run an AI reliability audit to get certification posture, or review Pulse to see how Reliai turns production reliability signals into action.