RAG that stays accurate after the demo

Who it's for

You're an agency building AI assistants for clients — legal Q&A, HR policy bots, customer support, or internal knowledge search. The demo wowed the room: instant answers, natural language, feels like magic. Then you ship it. Two weeks later, the client emails: "It's making things up. We can't trust it with our data."

Production RAG is different. It's not just embeddings + LLM prompts. It's versioned ingest, hybrid search, citation tracking, answer evals, and observability that tells you when retrieval is failing before the client does. StacX24 builds RAG systems that agencies can deliver to enterprise and mid-market clients — systems that hold up under real usage, not just pitch meetings.

Demo vs. production failure modes

Most RAG demos fail in production because:

• Retrieval is a black box — you don't know which chunks the LLM saw or why those chunks were selected
• No citation hygiene — answers cite documents that don't actually contain the claimed fact
• Ingest is manual — new docs pile up, embeddings drift, no one knows what's indexed
• Zero evals — you ship without knowing accuracy, latency, or retrieval quality baselines
• Observability gaps — when answers degrade, you debug by reading Slack complaints

We build RAG that survives contact with real users.

What we ship

1. Versioned ingest pipeline

Your client's docs live in Google Drive, Notion, Sharepoint, or all three. We wire automatic ingest (webhook or scheduled) so new docs are indexed without manual intervention. Chunking strategies are tested per document type — legal contracts split differently than HR FAQs. Every ingest run is versioned so you can roll back embeddings if quality degrades.

2. Hybrid retrieval with reranking

Semantic search (embeddings) alone misses exact matches. Keyword search alone drowns in noise. We ship hybrid: semantic + BM25, reranked with a cross-encoder so the LLM sees the most relevant chunks, not just the first N by cosine similarity. Retrieval logs every query, chunks retrieved, and rerank scores — visible in your observability dashboard.

3. Citation tracking

Every answer cites source chunks with doc IDs, page numbers, and URLs. The LLM prompt enforces citation discipline: if a fact isn't in the retrieved context, the answer says "I don't have that information" instead of hallucinating. Citations are verifiable — your client can click through to the original document and confirm.

4. Evals before prompts

We write evals — golden question sets with known-good answers — before finalizing the RAG pipeline. Evals test retrieval quality (did the right chunks surface?), answer accuracy (does the LLM output match ground truth?), and latency (is it fast enough for production?). You ship with a baseline. When performance drifts, you see it in metrics, not user complaints.

5. Observability

Trace every RAG call: query → retrieval → rerank → LLM → answer. Log chunk IDs, retrieval scores, token counts, latency, and costs. Monitor drift: are new questions hitting the index successfully? Are citation rates dropping? We wire dashboards (Datadog, Grafana, or Langfuse) so you and your client see RAG health in real time.

Timeline: 4–8 weeks

Week 1–2: Ingest design + golden eval set. We profile your client's docs, design chunking strategies, and write the first 20–50 eval questions with ground truth answers.

Week 3–4: Retrieval pipeline. Hybrid search, reranking, citation enforcement. Evals run in CI so you never ship a regression.

Week 5–6: Integration + observability. Wire the RAG backend into your client's app (API or embed widget). Traces flow to your monitoring stack.

Week 7–8: Beta rollout + iteration. Limited user group, eval expansion, prompt refinement based on real queries.

Scope adjusts based on data volume, integration complexity, and whether you're self-hosting or using our managed runtime.

Proof: Real estate RAG for Purposx

We built a multi-modal RAG assistant for Purposx, a real estate platform, indexing property listings, PDFs, and images. The system handles 500+ concurrent users with sub-2-second latency. Retrieval traces show which chunks contributed to each answer. Citation accuracy sits at 94% in production evals. The client's support team uses it daily — which means it actually works.

See the RAG knowledge assistant case study for architecture details.

Ship RAG that your clients can trust

If you're building AI assistants for enterprise or mid-market clients and need a RAG backend that holds up under production load, book a strategy call. We scope your project in 30 minutes, no sales pitch.

Book Strategy Call →

Or email contact@stacx24.com with your use case.