Custom RAG Development Services.

Custom RAG development services build retrieval-augmented generation systems that answer from your own documents rather than a model's training data. At query time the system retrieves relevant passages, generates an answer grounded in them and cites the source — the standard approach when answers must be verifiable or when different users see different documents.

A basic proof-of-concept with one document collection takes 2–4 weeks. Production-grade RAG with hybrid search, reranking and monitoring takes 6–12 weeks. Enterprise deployments with multiple data sources and RBAC take 12–20 weeks. Working demos ship at each milestone.

RAG retrieves external knowledge at query time; fine-tuning permanently modifies model weights. RAG is better for changing data, factual accuracy, source citations and lower cost. Fine-tuning suits changing tone or format. Most enterprise use cases benefit more from RAG.

Pinecone for managed simplicity. Weaviate for hybrid vector + keyword search. pgvector if you already run PostgreSQL. Qdrant for filtering and multi-tenancy. We select based on scale, latency and budget during scoping.

Smart chunking, hybrid search, cross-encoder reranking, citation tracking, confidence scoring and answer validation against retrieved documents. Production systems typically reach 95%+ factual accuracy on domain-specific questions.

Yes. RAG systems can run entirely on your infrastructure with self-hosted LLMs and local vector databases so no data leaves your environment. RBAC ensures users only see authorized documents.

Cost depends on document volume, data source count, retrieval accuracy targets and security requirements (RBAC, on-premise). Ongoing infrastructure scales with index size and query volume. We scope a discovery phase first and issue a fixed-price quote.