RAG & Knowledge Base Builds (pgvector, Pinecone)
About This Service
RAG Pipeline and AI Knowledge Base Development for UAE Companies
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.
What's included
- End-to-end RAG pipeline — Ingestion, chunking, embeddings and retrieval wired to an LLM, deployed on your infrastructure or cloud account.
- Vector database setup — pgvector inside your existing Postgres, or managed Pinecone or Qdrant — chosen for your document volume and budget.
- Hybrid search — BM25 keyword matching fused with vector similarity, which consistently beats vector-only retrieval on exact names, codes and clauses.
- Grounded answers with citations — Every response links back to the source passage, and the system says "not in the documents" rather than inventing an answer.
- Eval suite on your real documents — A scored test set built from your Arabic and English files, with accuracy and retrieval metrics reported before launch.
- Re-ingestion tooling — Scripts to add or update documents after handover so the knowledge base stays current without me.
How it works
- 1Corpus audit
I review a sample of your documents — formats, languages, volume — and recommend the vector store and chunking approach.
- 2Pipeline build
Ingestion, embeddings, hybrid retrieval and the answering layer are built and connected to your chosen interface.
- 3Eval and tune
We build a question set from real staff queries, score the system, and iterate on chunking and prompts until the numbers hold.
- 4Handover
You get the code, the eval report, re-ingestion scripts and a walkthrough for whoever maintains it.
Why work with me
| With me | Typical agency | |
|---|---|---|
| Accuracy measured with evals before launch | Demo on cherry-picked questions | |
| Arabic and English retrieval both tested | ||
| Runs on your own Postgres if you prefer | pgvector supported | Locked to their SaaS |
| Answers cite source passages | Sometimes |