RAG & Knowledge Systems

A language model on its own does not know your contracts, your procedures or last month's reports, and when asked it will produce something plausible anyway. Retrieval augmented generation fixes this by giving the model the right passages at the right time, but the quality of the result depends entirely on how documents are processed, indexed, filtered and ranked. Most disappointing AI assistants are retrieval problems, not model problems.

Policy and procedure libraries, contract repositories, technical documentation, regulatory texts, case histories, product manuals, and any archive that mixes PDFs, scans, spreadsheets and email. Arabic documents, scanned documents and tables are where generic solutions fail first.

We build the pipeline deliberately: extraction that handles Arabic, scans and tables; chunking that follows document structure; embeddings chosen for your languages; hybrid search that combines semantic and keyword retrieval; and filtering that enforces permissions before anything reaches the model. Answers carry citations. We evaluate retrieval quality separately from answer quality so problems can be located and fixed.

We start with a representative sample of your documents and a set of real questions with known good answers. The pipeline is built and tuned against that set until retrieval accuracy meets the agreed target. Then we connect the full sources, set up incremental updates so new documents appear within minutes, and hand over the monitoring that shows when quality drifts.

A retrieval system that can be measured, tuned and trusted.

• Ingestion pipeline for your document types and languages
• Search index with hybrid retrieval and permission filtering
• Evaluation set, accuracy reports and drift monitoring

Answers you can trace to a source, knowledge that stays inside your boundaries, and a foundation every future assistant or agent in the organisation can build on.