Hire RAG Developers to Ground Your AI in Your Own Data

A RAG developer specializes in retrieval-augmented generation — connecting large language models to your own data sources so answers are grounded in fact rather than only what the model learned during training. It’s a specific, technical subset of AI engineering: chunking, embeddings, vector search, and retrieval tuning.

That’s a classic sign of a language model answering from its general training instead of your actual data — the fix is a RAG pipeline that retrieves your real documents and forces the model to answer from that retrieved context, with citations back to source.

Practically anything text-based: PDFs, Word docs, Confluence or Notion wikis, support tickets, product catalogs, CRM notes, and structured database records. Each source type needs slightly different chunking and preprocessing.

A focused proof-of-concept against a defined document set typically takes two to four weeks; a production-grade system with freshness handling, evaluation, and monitoring is usually a longer, phased engagement.

It depends on your scale, existing infrastructure, and budget — Pinecone and Weaviate suit larger, managed deployments, while pgvector is a strong option if you already run PostgreSQL and want to avoid a new piece of infrastructure. We’ll recommend based on your actual constraints.

It significantly reduces it by grounding answers in retrieved facts, but no system eliminates hallucination entirely — we pair RAG with evaluation pipelines and confidence thresholds so uncertain answers are flagged rather than presented as fact.

Yes — we offer support retainers that include monitoring retrieval quality, re-indexing as your documents change, and tuning as usage patterns evolve.