Agents That Learn From Every Incident

Build institutional knowledge from past incidents. Semantic search surfaces relevant runbooks and resolutions instantly.

The Problem

Repeat Incidents

Same issues recur because knowledge lives in people's heads.

Slow MTTR

Engineers waste time rediscovering solutions to known problems.

Scattered Runbooks

Runbooks spread across wikis, Slack, and ticket systems.

The HatiData Fix

Incident Memory

Every incident and resolution stored with semantic embeddings.

Instant Recall

semantic_match() finds relevant past incidents in <5ms.

Safe Testing

Test remediation in branches before applying to production.

See It in Action

SELECT i.incident_id, i.title, i.resolution,
       semantic_rank(m.embedding, 'memory leak OOM kubernetes') AS relevance
FROM incidents i
JOIN_VECTOR incident_memories m
  ON semantic_match(m.embedding, 'memory leak OOM kubernetes', 0.7)
WHERE i.status = 'resolved'
ORDER BY relevance DESC LIMIT 5;

65%

faster MTTR

50%

fewer repeat incidents

5ms

knowledge search

Related Use Cases

Cybersecurity Threat Hunting

Security agents correlate alerts, IOCs, and threat intel using semantic triggers. Novel threats surface before signatures exist.

View use case

Supply Chain Intelligence

Multi-agent crews monitor suppliers, routes, and inventory. Semantic triggers fire when anomalies match historical disruption patterns.

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Manufacturing & Quality Control

Agents correlate sensor data with historical defect patterns. Semantic triggers catch quality drift before defects ship.

View use case

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