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.
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.
Every layer is engineered and measured — not a wrapper around a single embedding call.
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:
Fixed scope, fixed price, twelve weeks from briefing to live deployment.
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.
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.
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.
How production RAG maps to the realities of each regulated vertical we serve.
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.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: