Search, question and exploit thousands of documents, with sourced answers.
Retrieval-augmented generation (RAG) lets an AI model answer questions from your own documents instead of its general knowledge. The system first retrieves the relevant passages, then writes an answer that cites them, so every statement can be checked.
We build RAG systems that search contracts, procedures, regulations, technical manuals or scientific literature, from a few thousand pages to millions. The same engine drafts answers to tenders, compares long documents and extracts structured data.
Typical timeline: 4–10 weeks
01
We map your sources, formats, update frequency and access rights, and define the questions the system must answer.
02
Documents are parsed (PDF, Word, scans, tables), split and indexed with hybrid search that combines keywords and meaning.
03
We build a test set with your experts and measure answer accuracy and citation quality before anyone uses the system.
04
The assistant is delivered where your teams work: web app, Teams, Slack or inside your existing software, with access control.
Contracts, procedures, product documentation or regulations, answered in seconds with the exact passage.
Millions of pages indexed: legal codes, case law, technical manuals, scientific databases.
First drafts of answers, specifications generated from business requirements, reusable answer libraries.
Differences between 300-page versions, data pulled from invoices and contracts, analyses and summaries.
Casus · HPS · Bot4Lab
Legal & tax · Payments · Pharma
RAG over ~3 million legal documents; 19 AI solutions for a payments leader including RFP automation and 300-page bulletin comparison; expert Q&A for pharma and veterinary research.
We deploy on your own cloud account or on your servers when data must not leave your infrastructure.
For sensitive data we run open-weight models in your environment instead of calling an external API.
Each user only reaches the documents and tools they are allowed to see, and every action is logged.
Your data is never used to train public models. GDPR and UAE data-protection requirements are designed in from the start.
Fine-tuning changes how a model writes; RAG gives it access to your documents at the moment it answers. For questions about your own content, RAG is usually the right choice: it stays up to date when documents change and every answer cites its sources.
There is no practical upper limit. We have built a system that searches about 3 million legal documents. Volume affects the indexing architecture and cost, not the quality of answers.
Answers are grounded in retrieved passages and must cite them. We evaluate the system on questions validated by your experts, and the assistant says when the documents do not contain the answer instead of guessing.
Yes. The index, the documents and the model can all run in your cloud or on-premise, with open-weight models when data must not leave your environment.