RAG & Knowledge Base Builds (pgvector, Pinecone)

I build retrieval-augmented generation (RAG) systems that let an AI answer questions from your company documents - contracts, SOPs, product manuals, support history - instead of from its imagination. The pipeline covers the full chain: document ingestion and chunking strategies tuned to your content, embeddings with OpenAI or Voyage AI models, and a vector store on pgvector, Pinecone or Qdrant depending on your scale and budget.

Retrieval quality is where most RAG projects quietly fail, so I combine BM25 keyword search with vector similarity in a hybrid setup, ground every answer in retrieved passages, and attach citations so users can click through to the source paragraph. Hallucination reduction is measured, not promised: before handover I run an eval suite on your real documents - in Arabic and English - scoring answer accuracy, retrieval hit rate and refusal behaviour on questions the corpus cannot answer.

Typical clients are Dubai and Abu Dhabi firms with thousands of pages nobody can search - legal teams, property managers, free-zone consultancies, mainland trading companies with bilingual documentation. A production knowledge base with evals starts at AED 3,500 and ships in around 10 working days.

How it works

1. 1
   Corpus audit

   I review a sample of your documents - formats, languages, volume - and recommend the vector store and chunking approach.

2. 2
   Pipeline build

   Ingestion, embeddings, hybrid retrieval and the answering layer are built and connected to your chosen interface.

3. 3
   Eval and tune

   We build a question set from real staff queries, score the system, and iterate on chunking and prompts until the numbers hold.

4. 4
   Handover

   You get the code, the eval report, re-ingestion scripts and a walkthrough for whoever maintains it.