RAG that holds up in production.
Anyone can wire a vector store to a prompt. We build retrieval-augmented generation that stays accurate on a large, messy, permission-controlled corpus — hybrid retrieval, reranking, structure-aware chunking, grounding, and a real evaluation harness so quality is a number you can trust.
The gap between a RAG demo and a RAG system
Retrieval-augmented generation is the dominant pattern for putting an LLM to work on enterprise data — and the most common place enterprise AI quietly fails. A demo over ten clean PDFs looks magical. The same approach over a million documents, with tables, scanned pages, near-duplicate versions, and access controls, starts returning the wrong passage and the model confidently fills the gap.
We treat RAG as the engineering discipline it actually is. The retrieval layer is where accuracy is won or lost, so that is where we invest: getting the right chunk in front of the model every time, proving it with evaluation, and keeping every answer grounded in — and cited to — your real source of truth.
The anatomy of a production RAG pipeline.
Every layer is engineered and measured — not a wrapper around a single embedding call.
- Layout & table extraction
- Semantic / structural chunking
- Metadata & versioning
- Vector + keyword hybrid
- Cross-encoder reranking
- Query rewriting / expansion
- Citation enforcement
- Faithfulness guardrails
- Permission-aware retrieval
- Recall & faithfulness metrics
- Golden question sets
- Regression gating in CI
Where production RAG pays off
RAG earns its keep wherever the right answer already exists in your data but is too slow, too scattered, or too risky to retrieve by hand:
- Internal copilots & search — cited answers across the entire document estate instead of a results page nobody reads.
- Customer-facing assistants — grounded responses safe enough to put in front of customers because every claim is sourced.
- Policy, contract & SOP Q&A — the exact clause, version, and section returned with attribution, not a paraphrase.
- Agent retrieval — the retrieval backbone that feeds tool-using agents the grounded context they need to act correctly.
From scope to production.
Fixed scope, fixed price, twelve weeks from briefing to live deployment.
Common questions.
What is RAG (retrieval-augmented generation)?
RAG is an architecture that retrieves the relevant passages from your own data first, then has the model answer using only that retrieved context — with citations. It grounds answers in your documents instead of the model's training memory, which is what makes the output trustworthy and verifiable.
Why do RAG demos work but production RAG fails?
A demo runs on a handful of clean documents. Production runs on a large, messy, permission-controlled corpus where naive chunking and single-vector search miss the relevant passage. We engineer hybrid retrieval, reranking, structure-aware chunking, and continuous evals so accuracy holds as the corpus grows.
How do you measure whether a RAG system is accurate?
We build a retrieval and answer evaluation harness — measuring retrieval recall, grounding/faithfulness, and citation correctness against a labeled question set drawn from your real corpus — so quality is a tracked number, not a vibe, and regressions are caught before they ship.
Explore related capabilities.
RAG systems by industry.
How production RAG maps to the realities of each regulated vertical we serve.
Ready to make RAG actually accurate?
Bring a slice of your corpus and the questions your teams ask daily. In thirty minutes we will show how engineered retrieval answers them with citations — and how we will measure it. Response inside 24 hours.
Experienced within
Markets served.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: