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IBZA

Applied AI

A grounded assistant for complex internal knowledge

A representative AI assistant pattern combining permission-aware retrieval, citations, evaluation, and human escalation.

55% less search time

Representative outcome pattern. Replace with approved client evidence before publication.

Challenge

Specialists spent significant time finding current policy and procedure information across fragmented repositories.

Approach

Real questions formed the evaluation set. The team designed permission-aware ingestion, hybrid retrieval, source citations, feedback, and safe refusal behavior.

Architecture

Content synchronization, access filtering, retrieval, reranking, generation, evaluation, and trace telemetry were independently observable and replaceable.

Outcome

The representative target is a 55% reduction in knowledge search time while maintaining visible source evidence.

Technology

LLM APIs, PostgreSQL, vector retrieval, identity integration, and OpenTelemetry.