RAG that ships: Building a production knowledge copilot your team actually uses

The business problem

Your knowledge lives in a dozen places: Google Drive, SharePoint, Confluence, Notion, email, ticketing systems, and old PDFs buried in archival storage. New hires waste hours hunting for the “right” version. Busy experts answer the same questions repeatedly. Decisions get made on stale or incomplete information. Meanwhile, compliance and access control needs are non‑negotiable.

Most RAG demos don’t survive production. They ignore permissions, don’t refresh content, hallucinate confidently, and offer no way to measure quality. We build RAG systems that operate inside your reality: grounded answers with citations, strict access controls, continuous ingestion, and observable performance.

What “good” looks like

• Grounded responses with citations to source documents—every time.
• Permission‑aware retrieval: users only see what they’re allowed to see.
• Fast first‑token latency with robust fallbacks.
• Continuous ingestion and re‑indexing when content changes.
• Offline evaluation suite that tracks quality over time.
• Operational visibility: traces, metrics, and red‑teaming.

System blueprint

• Connectors for Google Drive, SharePoint, Confluence, Notion, Git, email exports, and line‑of‑business systems.
• Ingestion pipeline: text extraction (PDF, DOCX, HTML), cleaning, de‑duplication, auto‑chunking with semantic boundaries, metadata enrichment.
• Indexing: vector store + keyword (BM25) for hybrid search; per‑document ACLs embedded into the index.
• Retrieval: query rewriting, hybrid search, reranking, and diversification to avoid “single‑source” bias.
• Answering: grounded synthesis with explicit citations; structured output for UI and downstream tools.
• Controls: rate limits, content filters, and “no answer” when confidence is low.

Where LLMs add real value

• Query understanding: expand acronyms, infer synonyms, and capture intent without losing precision.
• Chunk‑aware synthesis: stitch together multiple sources while preserving nuance and uncertainty.
• Structured tool calling: fetch additional context (tickets, metrics) when relevant.
• Style and tone controls for internal vs. customer‑facing answers.

Access control and governance

Every retrieved document is filtered through user permissions before it reaches the model. We propagate ACLs from the source systems, tokenise them into the index, and enforce them again at response‑build time. We log every retrieval set and citation for audits.

• SSO integration (SAML/OIDC) and per‑group policies.
• PII redaction at ingestion and response time where required.
• Encryption at rest and in transit; tamper‑evident logs.

Evaluation and quality

• Golden‑set questions maintained with your SMEs. We track exact match, faithfulness, groundedness, and citation accuracy.
• Contrastive tests for tricky edge cases and sensitive topics.
• Drift monitoring: alerts when quality dips after content or model updates.
• Human feedback loops in the UI to catch bad answers quickly.

Performance and scale

• Hybrid search with cached results for hot queries.
• Streaming responses with budget‑aware planning.
• Back‑pressure and circuit breakers for source system outages.

Example scenario (anonymised)

A professional services firm with thousands of SOPs, contracts, and client briefs needed faster onboarding and fewer escalations. The RAG copilot allows consultants to ask natural‑language questions and get grounded answers with citations and links to the relevant clauses. Access controls mirror their DMS permissions. Result: faster ramp for new hires, fewer “where is that doc?” messages, and better client responses.

Rollout plan

1. Week 1: Connect 2–3 highest‑value sources, define golden‑set questions, set up SSO.
2. Week 2–3: Build ingestion and index with ACLs; ship a usable UI; add citation‑first prompts and guardrails.
3. Week 4: Pilot with a small team; measure; fix edge cases; expand sources.
4. Week 5–6: Production rollout; add monitoring, dashboards, and content freshness alerts.

Anti‑patterns to avoid

• Putting everything in one index without metadata or ACLs.
• Over‑chunking or fixed‑size chunks that break context and citations.
• Ignoring “no answer” paths; forcing hallucinated responses to look confident.
• No offline evals; chasing anecdotal feedback instead of measured quality.

UI patterns that drive adoption

• Show citations prominently with anchors into the source document.
• Offer quick filters (space, time range, repository) to refine retrieval.
• Make feedback trivial: “useful / not useful” with a short reason.
• Enable copy‑safe formatting and export to documents or tickets.

How we work

We start with your most painful knowledge flows and wire up only the sources that matter. We build the ingestion and retrieval layers with evaluation from day one, then iterate quickly. The first version ships in weeks, not quarters, and we improve it with real usage—safely and visibly.