Data, RAG & backend
Thousands of pages of policies, contracts and documentation, and an answer in seconds, backed by a source citation. I'll build you a private RAG architecture your data never leaves and never trains anything.
Who it makes sense for
- Company knowledge sits in documents nobody can search quickly
- Compliance forbids sending internal data to public AI tools
- New hires take months to onboard because "only Frank from accounting knows that"
What you get
- A private vector database over your documents, policies, contracts, wikis, e-mails
- Every answer linked to its source document, full auditability
- Access control: whoever can't read a document won't get an answer from it either
- APIs with no data retention, or an open-source model entirely in your cloud
How it works
Documents are split into semantic blocks and stored in a vector database inside your infrastructure. A query finds the relevant passages and the language model composes the answer from them, it never answers "from its own head". If the answer isn't in the documents, the system says so plainly.
Parameters
FAQ
Does the model train on our data?+
No. RAG doesn't train the model, documents are searched only at query time. I use APIs with no data retention, or a model running entirely on your side.
Can it handle our industry terminology?+
Yes. Deployment includes an evaluation set built from your real questions, I measure answer quality on your domain terminology before handover and continuously after.
Bring three questions you currently answer by digging through documents by hand, and I'll show you how RAG answers them.
Write to me, I'll reply within hours.