Every answer
cited
Users see where each answer came from and can check it in one click.
RAG development services answers from your own data.
Knowledge assistantsSemantic searchCitationsVector databases
Retrieval-augmented generation (RAG) lets an AI assistant answer from your documents, databases and help centre, with a source for every answer.
Large language models know a lot about the world and nothing about your business. Retrieval-augmented generation fixes that: before the model answers, the system finds the most relevant passages in your own content and asks the model to answer from them, citing its sources. Our RAG development services turn scattered documents into an assistant your team and customers can trust.
Most of the quality in a RAG system comes from the unglamorous parts: cleaning and splitting documents well, combining keyword and vector search, re-ranking results and respecting who is allowed to see what. We build those parts carefully, then measure answer accuracy against a set of real questions before anyone relies on it.
RAG is usually faster and cheaper to keep current than fine-tuning a model, because updating the answers is as simple as updating the documents. If you are unsure which approach suits you, our guide to RAG versus fine-tuning walks through the trade-offs.
What we build
RAG development, end to end.
- 01
Internal knowledge assistants
Let staff ask questions across policies, wikis, tickets and shared drives, and get answers with links to the source.
- Confluence
- Google Drive
- SharePoint
- 02
Customer support assistants
Answer customers from your help centre and product docs, and hand over to a person when confidence is low.
- Help centre
- Hand-off
- Analytics
- 03
Semantic search
Search that understands meaning, not just keywords, for product catalogues, documentation or legal archives.
- Hybrid search
- Re-ranking
- Filters
- 04
Document Q&A
Upload contracts, reports or manuals and ask questions across them, with the exact passages highlighted.
- PDF parsing
- Tables
- Highlights
The difference
What you can count on.
Every answer
Permissions
respected
People only get answers from documents they are already allowed to open.
Always
current
Content syncs automatically, so answers change when your documents do.
How it works
From first call to launch.
A written plan and a fixed price per milestone before any work starts, then a working demo every Friday.
- 01
Content audit
Map the sources, formats and permissions, and collect 50–100 real questions with known answers.
- 02
Pipeline
Ingestion, cleaning, chunking and hybrid search, tuned against those questions.
- 03
Assistant
The chat or search interface, with citations, feedback buttons and access control.
- 04
Measure & tune
Accuracy reports on real usage and ongoing tuning as your content grows.
Tech stack
Tools we use for RAG development.
Vector search
pgvectorPineconeOpenSearch
Models
OpenAIClaudeEmbedding models
Ingestion
PythonPDF & OCR parsingConnectors
Quality
Eval setsRe-rankingTracing
FAQ
RAG development questions.
Want a number? Try the cost estimator for an instant ballpark.
What is RAG in simple terms?
It is a way for an AI model to look up relevant passages in your own content before answering, so answers are specific to your business and come with sources. Our RAG explainer covers it in more detail.
RAG or fine-tuning: which do we need?
RAG suits knowledge that changes and needs sources. Fine-tuning suits teaching a model a style or format. Many products use RAG alone; our comparison guide explains when to combine them.
Which data sources can you connect?
Common ones include Google Drive, SharePoint, Confluence, Notion, help-centre platforms, databases and PDFs. Custom sources can be added through their APIs.
How accurate will the answers be?
We measure accuracy against real questions before launch and share the results. The system is designed to say it doesn't know rather than guess.
Where is our data stored?
In your cloud account wherever possible, with encryption and access controls. Model providers are used on terms that exclude training on your data.
Further reading
Guides from our team.
Tell us what you're building. A senior engineer, not a salesperson, replies within one business day.
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