AI services
RAG systems & enterprise knowledge bases
Retrieval-augmented generation (RAG) lets a language model answer from your own documents instead of guessing. The system finds the relevant passages first, then writes an answer that cites them, so people can check where it came from.
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What's included
- Vector database setup
- Hybrid search (BM25 + dense)
- Document processing pipelines
- Real-time knowledge updates
What is retrieval-augmented generation (RAG)?
Retrieval-augmented generation (RAG) connects a large language model to an external knowledge source. When a question comes in, the system searches your documents for relevant passages and passes them to the model, which writes an answer grounded in that content and can cite where each fact came from.
Where it helps
Common uses for RAG systems
A RAG system is only as good as its retrieval: whether it finds the right passages. That's where we put most of the work, in document processing, chunking, hybrid search and testing on real questions.
Internal knowledge assistant
Answers from policies, wikis, SOPs and past projects, so employees stop searching shared drives.
Customer support answers
Accurate replies drawn from your help centre and product documentation, with links to the source.
Contract and document search
Find and compare clauses, terms and figures across thousands of documents.
Technical documentation
Fast answers for engineers and field teams from manuals, specifications and tickets.
How we deliver
From first call to production
- 01
Audit your sources
We review document formats, owners, permissions and how often content changes.
- 02
Build the pipeline
We parse documents including PDFs and tables, split them into chunks, add metadata and carry over access controls.
- 03
Tune retrieval
We combine keyword (BM25) and vector search with re-ranking, and measure it on questions your team actually asks.
- 04
Ship and keep it fresh
We sync new and changed documents automatically and monitor answer quality and gaps.
Technology we use
- Pinecone
- Weaviate
- PostgreSQL
- LlamaIndex
- LangChain
- OpenAI
- Anthropic Claude
- Python
- FastAPI
FAQ
Questions about RAG systems
Will the AI make things up?
RAG reduces this by grounding answers in retrieved documents and showing sources. We also test with questions your data can't answer, so the system learns to say it doesn't know.
Can it respect who is allowed to see what?
Yes. We carry document permissions into the search index, so people only get answers from documents they can already open.
Which file types can it handle?
PDFs, Word and PowerPoint files, web pages, wikis such as Confluence or Notion, spreadsheets and database records. Scanned documents need OCR, which we add where needed.
Which vector database should we use?
It depends on scale, hosting and budget. We work with Pinecone, Weaviate and PostgreSQL-based options and recommend one after discovery.
Free consultation
Talk to us about RAG systems
Book a free 60-minute strategy session. We'll look at your use case, check feasibility and recommend an approach.
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