RAG & knowledge search

RAG, or retrieval-augmented generation, lets a language model answer from your own documents instead of only what it learned in training. It searches your contracts, manuals, emails and scans, passes the relevant passages to the model, and returns an answer that cites its sources. You need it when answers depend on company knowledge that is private or changes often. If the task is general writing or translation, a model on its own is usually enough.

Yes. Arabic, English and mixed documents are supported, and scanned PDFs and images go through Arabic OCR and text cleanup before indexing. Search combines vector and keyword matching with re-ranking, which helps with Arabic spelling variants and technical terms. Quality depends heavily on scan quality, so we measure accuracy on your own questions during a 3 to 4 week pilot before rolling out to more teams.

Accuracy is measured, not assumed. Before rollout we collect real questions from your team and score the answers against them, then tune ingestion, search and prompts until results are acceptable. Every answer cites the page, record or message it used, so users can open the source and check it themselves. When the documents do not contain an answer, we set the assistant to say so rather than guess. Permissions follow the access your people already have.