RAG Development Services
Production-grade retrieval-augmented generation that answers from your own knowledge — with citations, evaluation and a spec agreed before the first line of code.
A general-purpose LLM does not know your contracts, manuals, tickets or product catalogue — and when it does not know, it guesses. Retrieval-augmented generation fixes that by fetching the relevant passages from your own data at question time and making the model answer from them, with a link back to the source.
We design and build the whole pipeline: ingestion and chunking, embeddings, vector or hybrid search, re-ranking, prompt assembly, guardrails and the chat or API surface your users touch. Every system ships with an evaluation set so retrieval accuracy and answer grounding are measured, not assumed.
What we deliver
- Ingestion connectors for PDFs, docs, wikis, databases and APIs
- Chunking, metadata and embedding strategy tuned to your content
- Vector, keyword or hybrid search with re-ranking
- Answers with inline citations and source links
- Access control so users only retrieve what they may see
- Evaluation suite for retrieval quality, grounding and hallucinations
Where it fits
Internal knowledge assistant
Let staff ask questions across policies, SOPs and wikis instead of searching folders.
Customer support chatbot
Answer customer questions from help-centre articles and product docs, and hand off to a human when unsure.
Document Q&A
Query long contracts, reports or technical manuals and get answers that point to the exact clause or page.
Sales & product search
Natural-language search over catalogues, specs and pricing sheets for sales teams and shoppers.
Spec-Driven Agile Development
AI projects fail when nobody defines what “good” looks like. We write it down first, then deliver against it in short, demo-driven sprints.
Specify
We start with a written spec: goals, user stories, acceptance criteria, data sources and the metrics an AI feature must hit.
Architect
Retrieval pipeline, model selection, API contracts and infrastructure are designed against the spec before code is written.
Build in Sprints
Short agile sprints with a working demo at the end of each one. The spec evolves with your feedback, so scope never drifts silently.
Evaluate
Every release is tested against the spec — including retrieval accuracy, answer grounding and hallucination checks for RAG features.
Launch & Improve
We ship to production, monitor real usage and answer quality, and feed what we learn into the next sprint.
Frequently asked questions
What is retrieval-augmented generation (RAG)?
RAG is a technique where an AI model first retrieves relevant passages from your own data — documents, databases or APIs — and then generates an answer based on them. It keeps answers current and specific to your business and lets every answer cite its source.
Should we use RAG or fine-tune a model?
For most business knowledge, RAG is the better starting point: it works with data that changes, it can cite sources, and you do not need to retrain anything when documents are updated. Fine-tuning is useful for teaching a model a style or format, and the two can be combined.
Which LLMs and vector databases do you work with?
We are model-agnostic and choose per project — hosted models such as OpenAI, Anthropic Claude or Google Gemini, or open-weight models you host yourself — with vector stores such as pgvector, Qdrant, Pinecone or Elasticsearch.
How do you prevent hallucinations?
By grounding answers strictly in retrieved context, requiring citations, letting the system say “I don’t know”, and measuring all of it against an evaluation set built from your real questions before launch and after every change.
Can our data stay private?
Yes. We can deploy in your own cloud account, use providers with no-training data agreements, or run open-weight models fully on your infrastructure, and we enforce document-level permissions at retrieval time.
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