Predictive Quality from Machine Memory

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

The Problem

Late Detection

Defects found at end-of-line, not at source.

Recall Costs

Quality escapes cost millions in recalls and reputation.

Data Overload

Millions of sensor readings with no semantic context.

The HatiData Fix

Semantic Triggers

Fire when sensor patterns match historical defect signatures.

Defect Memory

Build a knowledge base of every quality issue and root cause.

Process Simulation

Test parameter changes in branches before production.

See It in Action

SELECT s.line_id, s.reading, s.timestamp,
       semantic_rank(m.embedding, 'vibration anomaly bearing failure') AS risk
FROM sensor_data s
JOIN_VECTOR quality_memories m
  ON semantic_match(m.embedding, 'vibration anomaly bearing failure', 0.7)
WHERE s.timestamp > CURRENT_TIMESTAMP - INTERVAL '1 hour'
ORDER BY risk DESC LIMIT 10;

70%

fewer quality escapes

<1s

anomaly detection

45%

reduced scrap rate

Related Use Cases

Supply Chain Intelligence

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

View use case

DevOps & Incident Response

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

View use case

Retail & Personalization

Real-time personalization powered by persistent customer memory. Recommendations improve with every interaction.

View use case

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