Solutions
RAG and LLMs
Private assistants grounded in your own documents, so answers come from your manuals, contracts and policies rather than the open internet.
Answers with sources
Every reply points back to the document it came from.
Kept private
Documents stay inside infrastructure you control.
Current
New documents are indexed as they are added, so answers do not go stale.
A general purpose model knows a great deal about the world and nothing at all about your business. Retrieval augmented generation closes that gap: the assistant searches your own documents and answers from what it finds, quoting the source it used.
Run privately, it becomes a way to make the knowledge your team already has far easier to reach, without sending internal material to a third party service.
What a grounded assistant is good for
- Answering staff questions from policies, procedures and handbooks
- Helping a support desk find the right fix in technical documentation
- Summarising a contract, specification or tender into the points that matter
- Onboarding new starters who do not yet know where things are written down
- Checking a proposal against a set of internal standards before it is sent
Why grounding matters
A model answering from memory will produce something plausible whether or not it is correct. Grounding changes the task from recalling to reading, and reading leaves a citation you can check.
- Documents are indexed with permissions, so people only retrieve what they may see
- Answers carry a reference to the source passage
- Where the documents do not contain an answer, the assistant says so rather than inventing one
- Indexing and retrieval run in infrastructure you control, not on a public endpoint
Getting the documents right
The quality of the answers depends on the quality of the source material. Duplicated versions, superseded policies and scanned documents with no text layer all need dealing with, and we include that clean up in the scope rather than leaving it as an exercise for you.
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