RAG & AI search
Ask your company's documents a question in plain language and get an answer that cites where it came from. Retrieval-augmented generation (RAG) lets a language model answer from your own sources instead of guessing.
The problem
The answer exists somewhere: in a contract, a manual, a policy or a shared drive. Finding it means keyword search, opening ten files and asking a colleague. General-purpose chatbots cannot see your documents, and answers without sources are hard to trust.
What we deliver overnight
- A working chat over a representative sample of your documents
- Answers that cite the source document and passage
- Document ingestion for the formats in your sample (for example PDF, Word, web pages)
- A small test set of real questions with the prototype's answers, so you can judge quality
- A list of open decisions: model choice, hosting, access rights, which sources to add next
After the prototype
Once you approve the approach, we connect the full document set, add access control so people only see what they are allowed to, set up updates when documents change, and deploy it in your cloud or ours with monitoring.
Good fit if…
- Your team keeps asking the same questions about internal documents
- Answers need a source someone can check
- Your documents are private and must stay under your control
- You want to see results on your own content before committing to a platform