Vector Embeddings for Retrieval

[Why embeddings matter]

Semantic vectors let agents retrieve relevant knowledge even when queries and documents use different language.

[How we implement]

Blending semantic and lexical search keeps recall high while respecting compliance boundaries.

• Domain-tuned embeddings with versioned indexes.
• Hybrid search (vector + keyword) with policy filters.
• Freshness SLAs and access controls per source.

[Performance metrics]

Track recall, freshness, and latency to keep retrieval predictable as corpora grow.

• Sources indexed: 60+
• p95 retrieval: < 120ms

[Index governance]

Good hygiene keeps retrieval stable as data and models change.

• Versioned indexes with rollbacks for bad ingestions.
• Access controls per collection and per-role.
• Drift detection on embeddings and reranking quality.

[Rollout sequencing]

Phased rollout ensures reliability before scaling to more sources.

• Start with a single high-value corpus; measure recall and latency.
• Add hybrid search with filters to control precision/recall balance.
• Automate freshness updates with monitoring for staleness and failures.