Why grounding matters more than model choice

The fastest way to lose stakeholder trust is to let a copilot hallucinate policy or legal language. We start every engagement by mapping source of truth inventories, access policies, and freshness SLAs. Our principle: every answer must cite a document that leadership already signs off on.

  • Segment corpuses by sensitivity and retention requirements.
  • Normalize titles, owners, and review cadence so governance teams stay in the loop.
  • Prefect or Airflow jobs refresh embeddings on the same cadence as document approvals.

The retrieval blueprint we lean on

Most copilots require both semantic search and precise lookups. Our baseline stack is dual vector stores + metadata filters + factuality checks. We tune chunk sizes per document type, use lightweight rerankers, and run grounding tests before a single user sees the UI.

  1. Ingest via webhooks or scheduled syncs into a sanitized blob store.
  2. Process through a structured chunker (markdown-first, table-aware, named anchors).
  3. Embed into a preferred vector store (Pinecone, Weaviate, pgvector) with version tags.
  4. Attach a relational store for metadata-driven policies.

Human-in-the-loop never goes away

Compliance teams need receipts. We auto-log every conversation, attach source excerpts, and route low-confidence answers to reviewers. Feedback loops feed the evaluation harness so we can quantify accuracy, latency, and user satisfaction.

  • Weekly eval runs with curated question sets and red-team prompts.
  • Precision / recall dashboards inside Metabase or Mode.
  • On-call channel for users to flag questionable answers in under 30 seconds.

Launch checklist

Before GA we run a short, intense checklist with stakeholders:

  • ✅ Data owners sign off on corpuses, access tiers, and redaction rules.
  • ✅ Grounding evals exceed 95% factual support across hundreds of prompts.
  • ✅ Observability hooks monitor latency, cost, and answer confidence.
  • ✅ Enablement session trains the pilot group with clear escalation paths.

Retrieval copilots succeed when trust is earned early. Want to see the architecture diagram or evaluation harness we shared with this client? We’re happy to walk through it live.