Triage confirmed incidents
AI reviews the failed check and deterministic diagnosis to explain whether the problem looks real, what is most likely happening, and what to do next.
AI observability
Turn monitoring data into a next move.
Use AI to investigate anomalies, triage confirmed incidents, and build a sensible monitoring setup—without giving up control of your signals or data.
- AI cost tracks incidents, not every check
- Once per incident
- AI cost tracks incidents, not every check
- the diagnosis engine is deterministic by design
- LLM-free core
- the diagnosis engine is deterministic by design
- AI never resolves, hides, or suppresses an incident
- Advisory only
- AI never resolves, hides, or suppresses an incident
01Deterministic diagnosis
Priority-ordered rules read sibling monitors and third-party impact data — reproducible, fast, never empty.
02AI triage (advisory)
A model reviews that diagnosis and returns a plain-language verdict, once per incident, not per check.
03De-prioritize, never resolve
A suspected false positive drops to P4 — it is flagged, never silently closed.
04Delivered with the alert
Your Slack DM and email carry the summary, likely cause, and suggested action.
How it works
Useful AI for the work between alert and resolution.
Investigate with live context
Ask about monitors, incidents, recent failures, response anomalies, locations, and affected services using the workspace data already in Monitero.
Set up monitoring faster
Smart Setup scans a website and proposes a tailored set of checks, while the assistant can create a focused batch of monitors from a single request.
Keep humans in control
AI is advisory: deterministic monitoring remains authoritative, false positives are never silently resolved, and every assistant tool is workspace-scoped.
What makes it powerful
Turns down the noise without ever risking a missed outage
The deterministic engine tells you what the network-level failure looks like; the AI tells you whether it matters and what to do — quietly reducing the false positives that make teams stop trusting their monitoring, without the AI ever getting a vote on whether an incident is real.
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