Retrieval-augmented generation lets a language model answer from your documents instead of from memory. The model part is the easy bit.
Tell us what you want to build. A senior engineer sends back a scope and a fixed price within two business days, no obligation.
In short
What we build
Parsing PDFs, tables, scanned files and wikis so headings, tables and page numbers survive, because bad parsing causes most bad answers.
Hybrid keyword and vector search, reranking and metadata filters, tested against real questions from your team.
Every answer links to the passage it came from, so people can check it in seconds.
Open models and self-hosted vector databases in your cloud or on your own servers when documents cannot leave.
Fit
Process
We ask the people who will use it for the questions they actually ask, along with the right answers.
We inspect how documents are parsed before touching prompts.
We measure whether the right passage is retrieved, separately from whether the answer is good.
Feedback buttons, logging and a regular review of failed questions.
Pitfalls
We have seen these enough times to plan around them from the start.
Next step
Want to see similar work? Browse our case studies or tell us what you are working on.
FAQ
Usually because the right passage was never retrieved, often due to poor parsing or chunking, not because the model is weak. We test retrieval and generation separately to find out which one is failing.
RAG when the answer depends on facts in documents that change. Fine-tuning when you need a consistent style, format or narrow skill. Many systems use RAG alone, and some use both.
Yes. We deploy open models and vector databases on your own hardware or private cloud when data cannot leave.
With a test set of real questions and correct answers, scored on every change: was the right passage retrieved, and was the answer correct and supported by it?
Keep reading
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