Production retrieval that cites its sources
or doesn’t ship.
A RAG demo is two days of work. A RAG system that survives audit, version control, and quarterly evals is a different category. We build the second one — with hybrid retrieval, cross-encoder reranking, citation enforcement, and an eval harness your team runs after we leave.
RAG (retrieval-augmented generation) is how enterprises ship LLM features against private data without fine-tuning. The hard problems are not “wire up a vector database” — they are chunking, retrieval quality, hallucination control, citation accuracy, eval drift, and governance. We’ve shipped RAG systems for Tier-1 banks, government, and global insurers. Every one cites sources. Every one passes eval gates before deploy.
Outcomes, not artefacts.
A production RAG system
Ingestion pipeline, vector store, hybrid retrieval, reranker, generation, citation layer, observability — all running in your environment.
An eval harness
Golden-set evaluation, retrieval metrics (hit rate, MRR, NDCG), generation metrics (faithfulness, answer relevancy), CI integration that blocks regressions.
A runbook
How to retrain rerankers, refresh the corpus, debug a bad answer, audit a citation, rotate models. Owned by your team after handover.
A clean handover
Your team owns the system after week 12. We don't sit on top of it. Steady-state engagement is optional, not built-in.
Compliance posture
Region-locked deployments, audit logging, PII redaction, model attestations. SOC 2 Type II, ISO 27001, HIPAA where the engagement requires it.
Specifics, because ‘the latest tools’ means nothing.
- Chunking
- Semantic chunking with structural awareness — headings, tables, lists. Not naive 512-token splits.
- Embeddings
- OpenAI text-embedding-3-large by default; open-weights (BGE, Nomic) where residency or cost requires it.
- Vector store
- Qdrant or Postgres + pgvector. Pinecone where the team already runs it. Hybrid retrieval = vector + BM25 + filters.
- Reranking
- Cross-encoder reranking (Cohere Rerank, BGE-reranker, or fine-tuned in-domain). Reranking is not optional in production.
- Generation
- Claude / GPT / open-weights with provider routing and fallback. Structured output with citation tagging at the prompt layer.
- Eval
- Golden-set retrieval evals + LLM-as-judge for faithfulness. Run on every PR. Block deploy on regression.
- Observability
- Langfuse or custom OTEL pipeline. Per-query traces, retrieval scores, eval results, cost telemetry.
How it runs
A Tier-1 bank's corporate banking team reviewed contracts manually — six hours per contract. We shipped a RAG system with citation gating and human review. Median review time fell to ~95 minutes (a 73% reduction) with zero unsupported claims in the audit window.
Tier-1 bank · Corporate banking RAG · Citation-gated
Ask the system about itself.
The demo runs against AIEngineersLabs’s own service documentation. Every answer shows the chunks it retrieved, the rerank score, and a citation back to the source. In production this same interface runs against a Qdrant collection with a real LLM.
Pick a question above. The retrieval result and citations will appear here.
What buyers actually ask
Do you use LangChain or LlamaIndex?
Which vector database do you recommend?
How do you measure retrieval quality?
What's the eval harness specifically?
Talk to an engineer, not a salesperson.
30 minutes. No slides. Bring an architecture, a stalled roadmap, or a vendor proposal you want a second opinion on. We'll tell you what we'd do.