RAG System Development & Retrieval Engineering
Answers grounded in your real documents, with citations that check out
Retrieval-augmented generation grounds a language model in your own content. Instead of answering from training data, the system finds the relevant passages in your corpus first, then answers strictly from what it retrieved — and shows you which passages it used.
- Every claim
- GroundedEvery claim
- Search strategy
- HybridSearch strategy
- Retrieval accuracy
- MeasuredRetrieval accuracy
Typical stack
- LlamaIndex
- LangChain
- Pinecone
- pgvector
- Cohere Rerank
Why this keeps costing you
A general model will answer confidently about your pricing, your policies, and your contracts, and be wrong about all three. Once users catch one fabricated answer, they stop trusting the system entirely — and the adoption problem is much harder to fix than the accuracy one.
Inside a RAG system we ship
- Document parsing that preserves tables, headings, and layout structure
- Chunking strategies tuned per document type rather than a fixed token count
- Hybrid dense and sparse retrieval with a cross-encoder reranking pass
- Citation grounding that ties every claim to a specific retrieved passage
- Retrieval evaluation harness with recall and precision tracked per release
- Incremental re-indexing so updated documents are searchable immediately
The part most implementations skip
Most failing RAG deployments fail at retrieval, not generation. We build the evaluation harness first: a set of real questions with known-correct source passages, so retrieval accuracy is a number we can improve rather than a feeling. Chunking strategy, hybrid search, and reranking then get tuned against that number.
What you actually receive
- Indexed corpus with a documented chunking and embedding strategy
- Golden evaluation set of questions with verified source passages
- Retrieval quality report benchmarked before and after tuning
- Query API or chat surface, depending on how you will consume it
- Runbook for adding new document types to the pipeline
RAG Systems in your sector
How rag systems plays out in the industries we work with most.
- RAG Systems for Law FirmsAn uncited legal answer is worse than no answer.
- RAG Systems for HealthcareClinical protocols are only useful if staff can find them mid-shift.
- RAG Systems for EducationCourse knowledge is spread across documents nobody reads end to end.
- RAG Systems for SaaSShip retrieval that is actually accurate, not a demo that impresses once.
- RAG Systems for Financial ServicesRegulatory answers need a source, a date, and a version.
Common Questions
- Why not just fine-tune a model on our documents?
- Fine-tuning teaches style and format well but is a poor way to store facts — it cannot cite sources, and updating a single document means retraining. RAG updates the moment you re-index and can always show its working. Most projects that think they need fine-tuning need better retrieval.
- How accurate can we expect it to be?
- That depends on your corpus, and we will measure it rather than promise a number. The evaluation harness gives you a retrieval accuracy figure on your own documents before anything reaches users — if it is not good enough, we tune until it is.
- Can it handle scanned PDFs and messy documents?
- Yes, with an OCR and layout-parsing stage ahead of chunking. Quality varies with the scans, so we test against your worst documents early rather than discovering the problem after launch.
Often built alongside this
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