Enterprise AI
Intelligence that talks to your data.
The knowledge already exists in your company — buried in files, emails, archives. Enterprise AI turns it into a secure assistant that answers in seconds and shows its sources.
RAG · Vector DB · Claude · LlamaIndex
How it works.
Data map
Which sources get connected, who can access what — we draw the scope and access rules together.
RAG setup
We index your documents securely and connect them to the model: answers come from your data, with source references.
Testing & rollout
We test with real questions and open it to your team. The system keeps improving with usage data.
Example scenarios.
Internal knowledge assistant
"What is the leave procedure?", "What was decided on project X?" — hours of searching become seconds.
Contract & regulation Q&A
Finds the relevant clause in hundreds of pages and shows it with its source — no making things up.
Support knowledge base
Answers customer questions grounded in your own documentation; hands over to a human when it does not know.
Frequently asked.
Does our data leave the company?
No — your data stays on infrastructure you own, access-controlled. Where data flows is fixed in the contract up front; you own everything at delivery.
What about hallucinations?
That is exactly what RAG architecture is for: answers are grounded in your documents and cite sources. For questions outside the documents, the system says "I don't know" — it does not invent.
Our data is messy — is that a problem?
No — PDFs, emails, Word files, spreadsheets; today's AI is good at messy input. A perfect archive is not required, accessible data is enough.
Ready to start?
A technical response within 48 hours.