RAG & AI Knowledge Bases

RAG (Retrieval-Augmented Generation) connects an AI model to your own documents: procedures, regulatory databases, customer history. A standard search engine returns a list of files for you to open, whereas RAG understands the meaning of your question and writes a synthesized answer that cites the exact sources it used. Your teams can therefore verify every statement by opening the original document.

The budget depends first on the scope: the volume of documents to ingest, the formats involved (PDF, Word, Excel, emails), the languages to cover and the integrations with your existing tools. Role or department access controls also add complexity, because each confidentiality level has to be defined and then tested. That is why we start with a scoping phase that inventories your sources and use cases and leads to a precise estimate before any commitment.

Yes, indexing and search natively support French and Arabic, so a question asked in either language retrieves the relevant passages whatever the original format. We also designed the offering around Moroccan reference texts: the labor code, Law 09-08, Bank Al-Maghrib circulars or your standard contracts can all be indexed, and every answer points back to the exact articles of the text it cites.

Confidentiality relies on granular access controls defined by role, department or sensitivity level, in compliance with Law 09-08. An employee therefore only gets answers drawn from documents their permissions allow them to see; a confidential HR procedure stays invisible to teams with no reason to consult it. Each answer also cites its sources, which lets your administrators trace exactly which document was used.