Service
RAG System Development & Retrieval Engineering
Need answers from your own documents, not from the internet?
VelvoAI builds retrieval systems that ground a language model in your own content. The system finds the relevant passages in your corpus first, answers strictly from what it retrieved, and shows which passages it used — so a wrong answer is traceable rather than mysterious.
Built by VelvoAI, an independent AI systems studio. One engineer scopes and writes every system.
What the system does
- Every claim
- Grounded
- Search strategy
- Hybrid
- Retrieval accuracy
- Measured
- ComponentLlamaIndex
- ComponentLangChain
- ComponentPinecone
- Componentpgvector
- ComponentCohere Rerank
The problem
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.
What we build
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
How we approach it
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.
Deliverables
What you actually receive
Every VelvoAI engagement ends with working software you own, not a slide deck. Here is what ships with a RAG system.
How a build runs, step by step- D.01Indexed corpus with a documented chunking and embedding strategy
- D.02Golden evaluation set of questions with verified source passages
- D.03Retrieval quality report benchmarked before and after tuning
- D.04Query API or chat surface, depending on how you will consume it
- D.05Runbook for adding new document types to the pipeline
By industry
RAG Systems in your sector
RAG Systems, applied to the workflows and rules of the sectors we work with most.
- 01
RAG Systems for Law Firms
An uncited legal answer is worse than no answer.
- 02
RAG Systems for Healthcare
Clinical protocols are only useful if staff can find them mid-shift.
- 03
RAG Systems for Education
Course knowledge is spread across documents nobody reads end to end.
- 04
RAG Systems for SaaS
Ship retrieval that is actually accurate, not a demo that impresses once.
- 05
RAG Systems for Financial Services
Regulatory answers need a source, a date, and a version.
Common Questions
01Why 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.
02How 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.
03Can 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.
Related services
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- Written by
- Usman Ali, Founder
- Last reviewed
Next step
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