Retrieval-Augmented Generation (RAG)
Answers from your own documents, with the sources shown.
Retrieval-augmented generation that grounds AI answers in your documents and respects who may see what.
Why teams call us
Problems this solves
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01
Answers buried in documents
Policies, manuals, contracts and tickets hold the answers, but searching takes too long.
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02
AI that makes things up
A general-purpose model answers confidently even when it does not know.
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03
Not everyone may see everything
HR, finance and client files need permissions respected by any assistant.
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04
Knowledge changes every week
Retraining a model whenever a document changes is impractical.
Our approach
Retrieval-augmented generation (RAG) finds the passages in your documents most relevant to a question and gives them to the language model to answer from, with citations. Updating knowledge is as simple as updating the documents, and the assistant can say when the documents do not contain an answer.
Quality depends on the details: how documents are cleaned and split, how search is tuned, and how answers are evaluated. We measure retrieval and answer accuracy on real questions, and we enforce document permissions so users only receive answers from content they are allowed to see.
What is included
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Document ingestion
PDFs, Office files, web pages, wikis and tickets, kept in sync.
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Chunking and indexing
Content split and indexed for accurate retrieval.
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Hybrid search
Vector and keyword search combined, with re-ranking.
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Permission-aware answers
Results filtered by the user's access rights.
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Citations
Every answer links to the passages it used.
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Evaluation
Retrieval and answer accuracy measured on real questions.
What you receive
- Ingestion pipeline
- Search index
- Assistant interface or API
- Evaluation report
- Access-control design
- Admin guide for keeping content current
Technology we work with
- pgvector
- Qdrant
- Elasticsearch
- OpenSearch
- OpenAI embeddings
- Cohere
- LlamaIndex
- Python
Industries
How an engagement runs
Every engagement starts with a signed agreement and an advance payment as set out in your proposal. Work begins once both are complete.
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01
Collect
Sources, permissions and test questions gathered.
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02
Build
Ingestion, index and answer pipeline.
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03
Evaluate
Accuracy measured and tuned on real questions.
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04
Deploy
Assistant launched with monitoring and feedback.
Ready to start? Talk to us directly.
Tell us what you need in a few lines. A specialist replies with next steps and a written proposal before any work begins.
Frequently asked questions
Can't find your answer? Ask us
RAG or fine-tuning?
Can it handle scanned PDFs?
Where is our data stored?
Keep exploring
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Start with a conversation.
Share a few details. A specialist replies with next steps, usually starting with a short call.