Enterprise knowledge assistant
Ask questions across policies, wikis, contracts and tickets and get cited answers.
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AI that knows your business
Private assistants and search grounded in your own documents and systems, with citations, permissions and evaluation built in.
What we build
Knowledge assistants people actually trust
Customer-facing answer engine
Accurate, on-brand support answers grounded in your product documentation.
Document intelligence
Extract, classify and summarise from invoices, contracts, forms and reports.
Permission-aware search
Retrieval that respects who is allowed to see what, enforced at query time.
Domain-tuned models
Fine-tuning or prompt systems for specialist tone, format and task behaviour.
Evaluation & monitoring
Test sets and dashboards that measure quality and catch regressions.
Approach
How we make RAG work
- 1
Ingest
Clean extraction, chunking and metadata from your real documents.
- 2
Retrieve
Hybrid semantic and keyword search with re-ranking and access control.
- 3
Generate
Grounded answers with citations and graceful refusal when evidence is missing.
- 4
Evaluate
Real-question test sets that run on every change.
FAQ
LLM & RAG, answered
Will our data train a public model?
We design for your data to stay private: your cloud or VPC, and provider settings or models that do not train on your data. Specifics depend on your requirements and are agreed up front.
Can it respect existing permissions?
Yes. Retrieval can be filtered by user and group so people only get answers from content they are allowed to see.
How do you stop it making things up?
Grounding in retrieved sources, citations, refusal when evidence is thin, and evaluation tests. No system is perfect, so we measure and monitor.
Which models do you use?
We are model-agnostic and choose per use case, including OpenAI, Anthropic, Google and open-source models.
Put your knowledge to work
Tell us where your team loses time searching for answers.