Document Q&A assistants
Assistants that answer customer and staff questions directly from policies, manuals and guides.
RAG development & document intelligence.
We build retrieval-augmented generation (RAG) systems that let customers and staff ask questions in plain English and get answers drawn straight from your documents, with the source to prove it.
What we build
Answers from your documents, not guesswork.
RAG connects a large language model to your own content, so every answer is based on what your documents actually say.
Semantic search
Natural-language search that finds the right passage even when the question uses different words.
Embeddings & vector databases
Your documents indexed as embeddings in a vector database for fast, relevant retrieval.
Citations & grounding
Answers that quote the original wording and show where it came from.
Summarisation
Long documents turned into clear, plain-English summaries.
APIs & integration
Served through FastAPI so you can plug document intelligence into existing systems.
How RAG works
From document to cited answer.
A RAG pipeline in five steps.
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01
Ingest
Load your documents and split them into well-sized passages.
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02
Embed
Turn each passage into an embedding stored in a vector database.
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03
Retrieve
Find the passages most relevant to each question.
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04
Generate
A large language model writes the answer from that context.
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05
Cite
Return the answer with the source passages, so it can be checked.
Client work
See it in production.
Use cases & technology
Where RAG fits.
Use cases
Insurance policy documentsHR & internal policiesProduct manualsContracts & termsKnowledge basesSupport articles
RAG stack
LangChainLarge Language ModelsEmbeddingsVector DatabaseSemantic search
Delivery
PythonFastAPIDjangoReact
FAQ
Frequently asked questions.
What is RAG (retrieval-augmented generation)?
RAG is a way of building AI assistants where the system first retrieves the most relevant passages from your own documents, then asks a large language model to write an answer using only that context. The result is an answer grounded in your content rather than the model’s general knowledge.
Why not just use a general AI chatbot?
A general chatbot doesn’t know your policies or documents and can produce confident but wrong answers. RAG grounds the model in your content, lets it cite sources, and stays up to date as your documents change.
What kinds of documents can be used?
Most text-based content works well, such as policy documents, PDFs, Word files, web pages, manuals and knowledge-base articles.
Can the assistant show where an answer came from?
Yes. We design RAG assistants to return the source passages alongside each answer, so customers and staff can check the original wording.
Let your documents answer back.
Tell us which documents your customers or team keep searching through, and we’ll show you what RAG can do.