RAG Development Services

RAG retrieves relevant content from your knowledge base at query time and feeds it to the LLM as context, rather than baking knowledge into model weights. It's faster to update, easier to audit via citations, and usually cheaper than fine-tuning for knowledge-grounding use cases.

Selection depends on scale and requirements — Pinecone or Weaviate for managed simplicity, Milvus for very large self-hosted deployments, and pgvector when the client wants to keep vectors inside an existing Postgres environment.

Development typically ranges from $70,000 to $250,000 depending on corpus size and integration complexity. Ongoing inference and vector database hosting costs scale with query volume and are modelled during the discovery phase.

Most systems reach production in 8–14 weeks, covering chunking design, embedding selection, retrieval architecture, and evaluation against your defined precision targets.

We benchmark groundedness against a held-out evaluation set before launch, then track it in production. Reduction comes primarily from better chunking, hybrid retrieval, reranking, and abstention logic rather than the LLM itself.

Yes. Self-hosted vector databases such as Milvus or pgvector run entirely within your infrastructure for organisations with data residency or air-gapped requirements.

We build incremental sync pipelines triggered on document updates, typically achieving sub-15-minute index freshness rather than relying on periodic full reindexing.

RAG is the retrieval-and-grounding architecture underneath a system — it's often a component inside a chatbot or agent, not a customer-facing product itself. We build RAG standalone for internal knowledge tools, or as the grounding layer inside a chatbot or agent engagement.

If you already know RAG is the right fit (grounding answers in your own documents/data), we scope and build directly. If you're unsure whether RAG, fine-tuning, or a simpler approach fits best, a short consulting engagement resolves that first.

RAG systems need index freshness monitoring as your source documents change, periodic retrieval-quality evaluation, and cost tracking as usage scales. We offer this as a scoped monitoring retainer rather than assuming a 'build and forget' handoff.

Yes — startup engagements are typically a single knowledge base grounding one product surface; enterprise engagements add access-tiered retrieval across multiple document stores and departments.