Hire RAG Developer — AI that answers from your data, not its imagination
Retrieval-augmented generation is where most AI projects quietly fail: documents chunked badly so answers lose context, embeddings that cannot tell your products apart, vector search returning the wrong passages, and no evaluation to prove any of it works. A RAG developer treats retrieval as an engineering discipline — chunking strategy, hybrid search, reranking, and faithfulness scoring — so the model answers from your sources and cites them.
I'm Omer Muneer Qazi, a Dubai-based Fractional CTO & Solutions Architect with 15+ years of experience and 100+ projects delivered across 6 countries. I build retrieval systems with measured recall and precision, not hope. When your corpus is small or your data needs shaping first, hire an AI fine-tuning expert through me to discuss the alternative path.
Retrieval systems with measured quality
Corpus & chunking strategy
Your documents analyzed for structure, then chunked with overlap, metadata, and hierarchy awareness — because retrieval quality is decided at ingestion, long before the first query runs.
Embedding & index design
Embedding model selection benchmarked on your domain, vector index configuration (pgvector, Pinecone, Qdrant, or Weaviate), and metadata filtering so search respects your access rules and document types.
Hybrid retrieval pipeline
Dense vectors combined with keyword search and reranking models, tuned per query type — the combination that consistently beats pure vector search on real enterprise corpora.
Grounded generation layer
Prompts and citations wired so answers reference the retrieved passages, with refusal behavior when the corpus has no answer — honest ‘I don’t know’ beats confident fiction.
Faithfulness & recall evals
Automated evaluation measuring whether retrieved passages answer the question and whether generated answers stay faithful to them — tracked on every pipeline change.
Incremental ingestion
New and updated documents indexed without full rebuilds, with versioning and access-control propagation so permissions in your source system are respected in search results.
From corpus to cited answers
A structured engagement with no surprises — you’ll always know what’s happening and what’s next.
Corpus analysis
We profile your documents: formats, structure, freshness, access rules — and define the question types the system must answer well.
Pipeline prototype
Chunking, embeddings, and hybrid retrieval built against a sample of your corpus, scored on recall with a hand-labeled test set.
Generation & eval loop
The answering layer tuned with faithfulness evals until citation accuracy clears your bar on real questions from your team.
Production & refresh
Deployed with incremental ingestion, monitoring for retrieval drift, and a feedback loop that captures bad answers for pipeline fixes.
Why hire a RAG developer through a Fractional CTO
RAG looks easy in tutorials and breaks on real corpora: PDFs with tables, scanned documents, conflicting versions, permissioned content. I scope retrieval work around your actual documents and measure it with evals — so you get cited, trustworthy answers instead of a demo that impresses once.
If your team tried RAG and the answers are unreliable, send me a sample of your documents and I will tell you what the retrieval layer is missing.
Frequently asked questions
Why not just fine-tune the model on our documents?
Fine-tuning teaches style and behavior; it does not reliably store facts, and updating it means retraining. RAG keeps facts in your documents — fresh, citable, and permission-aware. We use fine-tuning only when the task needs it.
Which vector database should we use?
pgvector if you already run Postgres and your scale is moderate; Pinecone or Qdrant for managed scale; Weaviate for hybrid search built in. The choice follows your corpus size, team skills, and latency budget.
How do you handle PDFs, tables, and scanned documents?
Layout-aware parsing for structured documents, OCR pipelines for scans, and table-aware chunking so tabular data stays interpretable. Messy source documents are the norm, not the exception.
Can search respect our document permissions?
Yes — metadata filtering at retrieval time enforces the same access rules as your source system, so users only get answers from documents they are allowed to see.
How do we know the answers are accurate?
Two eval layers: retrieval recall (did we find the right passages?) and faithfulness (does the answer match them?). Both run automatically on every change, plus sampled human review in production.
Make your documents answerable
Describe your corpus and the questions it should answer — I will scope a retrieval system with measured quality, not guesswork.