Agents That Remember Every Customer

End the amnesia loop. Your support agent recalls every past interaction, preference, and resolution — across sessions.

The Problem

Agent Amnesia

Agents restart from zero every session. Customers repeat themselves endlessly.

Slow Resolution

Without context, agents waste time re-discovering known solutions.

Churn Risk

Frustrated repeat-explainers leave for competitors with better support.

The HatiData Fix

Persistent Memory

Every interaction stored and searchable with semantic_match().

Instant Context

JOIN_VECTOR retrieves relevant history in <5ms at query time.

Continuous Learning

Agents improve with every ticket. semantic_rank() surfaces best resolutions.

See It in Action

-- Find relevant past resolutions for this customer
SELECT t.ticket_id, t.resolution, m.content AS context
FROM tickets t
JOIN_VECTOR agent_memories m
  ON semantic_match(m.embedding, 'billing dispute resolution', 0.75)
WHERE t.customer_id = 'cust_12345'
  AND t.status = 'resolved'
ORDER BY semantic_rank(m.embedding, 'billing dispute resolution') DESC
LIMIT 5;

73%

faster resolution

5ms

memory search (p50)

41%

fewer repeat contacts

Related Use Cases

FinTech Compliance

Combine structured financial data with semantic context. JOIN_VECTOR links transaction tables to agent memory for intelligent fraud detection.

View use case

Healthcare Data Intelligence

Agents access patient history with HIPAA-grade audit trails. Column masking and row-level security enforced at query time.

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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