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Your AI is only as smart as what it knows. Retrieval-Augmented Generation (RAG) systems give your AI agents instant, accurate access to your company knowledge — documents, databases, CRM history, Slack conversations, and more. No hallucinations. No outdated answers. Just precision recall at LLM speed.
Mourad Benhaqi designs and deploys production RAG pipelines that connect your knowledge to leading LLMs — Claude, GPT-4o, Gemini — using vector databases, hybrid search, and continuous evaluation frameworks. Built for enterprise accuracy, not demo demos.
Ingest documents, PDFs, Notion pages, Confluence wikis, Slack channels, Google Drive, emails, and CRM records. Automated chunking strategies — semantic, fixed, hierarchical — tailored to your content type for maximum retrieval precision.
Production-grade vector stores using Pinecone, Weaviate, Qdrant, or Chroma — selected for your scale, latency requirements, and compliance constraints. Metadata filtering, namespace isolation, and multi-tenant support built in from day one.
Choose the right embedding model for your use case: OpenAI text-embedding-3-large for accuracy, Cohere embed-multilingual for multi-language support, or open-source BGE/E5 models for self-hosted GDPR compliance. Embeddings re-generated on knowledge updates.
Combine dense vector search with BM25 sparse retrieval for best-of-both recall. Cohere Rerank or cross-encoder models re-score top candidates to surface the most contextually relevant chunks — not just the most semantically similar.
Complex queries decomposed into sub-queries, each routed to the relevant knowledge namespace. HyDE (Hypothetical Document Embedding) for improved retrieval of abstract questions. Query rewriting via LLM to maximise recall before retrieval.
Intelligent context window management: rank retrieved chunks, remove duplicates, inject structured metadata, and assemble a context prompt within token budget. Prompt templates calibrated for your LLM — Claude, GPT-4o, or Gemini Pro.
Automated pipelines to re-embed and update your vector store when source documents change. Webhook-triggered re-indexing from Notion, Confluence, Google Drive, or any CMS. Your AI always has the freshest knowledge.
Continuous evaluation using RAGAS metrics: faithfulness, answer relevance, context precision, and context recall. Hallucination detection via cross-checking LLM outputs against retrieved sources. Alerting when answer quality degrades.
Answer employee questions using your SOPs, HR policies, product docs, and wikis — no more Slack searches or wrong answers from outdated docs.
Support agents grounded in your product documentation, FAQs, and past ticket history — Claude Haiku responses in under 2 seconds, escalating only complex cases.
Give sales reps instant access to case studies, competitor battlecards, pricing logic, and past proposals — surfaced contextually during live calls.
Query your contract library, compliance guidelines, regulatory filings, and audit trails — with citations back to the source document and page.
Technical documentation, API references, changelog entries, and architecture diagrams — queryable by developers and support teams via natural language.
Ingest competitor content, market reports, analyst notes, and news — giving your strategy team always-fresh competitive context on demand.
Book a free RAG architecture audit. We'll map your knowledge sources, define your retrieval strategy, and scope a production-ready system.