AI integration
Retrieval-augmented assistants grounded in your own documents — not a generic chatbot with your logo on it.
Most "AI features" are a chat box wired straight to a model. It sounds confident and it invents things, which is worse than useless when the subject is a contract, a regulation or a customer record.
What we build instead is retrieval-first: your documents are chunked, embedded and searched, and the model is only allowed to answer from what it retrieved — with citations, and with a refusal when the corpus does not cover the question. We built exactly this for El Maître across 8,000+ legal articles in French and Arabic.
The hard parts are rarely the model. They are the ingestion pipeline, the chunking, the language handling, and knowing when to say nothing.
What you get
- An ingestion pipeline for your documents, re-runnable as they change
- Vector search tuned on your actual content
- Answers with citations, and explicit refusals when coverage is thin
- Multilingual retrieval where you need it
- Cost controls and rate limits so a spike cannot surprise you
Stack
Guides
Common questions
Will it make things up?
It is built not to. The model answers only from retrieved passages and refuses when they do not cover the question. We test the refusal path deliberately, not just the happy path.
Does our data go into training?
No. Your documents stay in your database, and the API providers we use do not train on API traffic.
Does it work in French and Arabic?
Yes, and we have shipped it. Retrieval thresholds have to be tuned per language — a good Arabic match scores measurably lower than an equivalent French one, and ignoring that causes silent false refusals.