We build an assistant powered by retrieval-augmented generation that answers from your documents and internal systems. Every response cites its sources for transparency and verifiability, and the assistant respects each user's access permissions. Both Arabic and English are supported to serve employees and customers alike.
What's included
- Design and build an enterprise RAG assistant that answers from your documents and internal systems.
- Build a retrieval pipeline combining vector indexing with hybrid semantic-and-keyword search and re-ranking.
- Ground answers in sources and attach verifiable citations to every response to reduce hallucination.
- Access-control-aware retrieval so a user only ever sees content they are authorised to access.
- Full Arabic and English support across understanding, retrieval, and generation, including Arabic-specific text handling.
- Integration with enterprise sources such as files, databases, and internal systems through a secure chat interface.
Methodology & standards
Scoping and design: define use cases, knowledge sources, the permissions model, and success criteria.
Knowledge preparation: ingest, clean, chunk, embed, and vector-index the sources, coordinated with the vectorization service where needed.
Build the retrieval and generation pipeline: hybrid search, re-ranking, source grounding, citation controls, and out-of-knowledge refusal.
Evaluation and tuning: measure retrieval accuracy, grounding, and hallucination rate on a test set, then tune parameters.
Deployment and integration: connect the assistant to systems and permissions, and hand it over with a monitoring dashboard and a content-refresh mechanism.
Deliverables
- A production-ready RAG assistant with an Arabic and English chat interface.
- A documented retrieval pipeline covering indexing, hybrid search, re-ranking, and grounding.
- A permissions layer enforcing access-aware retrieval per user.
- An evaluation report showing retrieval accuracy, grounding rate, and hallucination on a test set.
- A usage-and-quality monitoring dashboard and a documented knowledge-refresh mechanism.
- An operations runbook and a knowledge-transfer handover for the internal team.
Regulatory controls it satisfies
Typical timeline
A working prototype is delivered in three to four weeks and a fully integrated production version in six to ten weeks depending on sources and integrations.
Common questions
How do you stop the assistant from making up answers?
We rely on grounding: the assistant answers only from what it actually retrieved, cites its sources on every response, and explicitly declines when the knowledge is insufficient rather than guessing. We measure grounding and hallucination rates on a test set before launch.
Can the assistant see documents the user is not authorised to access?
No. Retrieval is access-aware: permission constraints are applied at the retrieval layer itself, so unauthorised documents never enter the answer context, and your existing permissions model is honoured.
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