RAG development can range from a focused internal knowledge tool to a production platform operating across thousands of documents, multiple data sources and strict permission boundaries. The cost depends less on the chatbot interface and more on the quality and complexity of the retrieval system behind it.

Typical RAG development costs in the UK

For planning purposes, focused RAG pilots can start in the lower tens of thousands of pounds. WeUno typically sees production RAG work as a £20,000 to £60,000+ engineering problem, with complex enterprise and regulated deployments scoped individually. These are indicative ranges, not fixed quotes.

What determines RAG development cost?

Document volume and complexity

A few hundred clean documents are different from thousands of pages containing tables, exceptions, duplicated guidance and changing versions. Ingestion and document preparation can become a significant engineering workstream.

Retrieval architecture

Embeddings and vector search are only the starting point. Metadata filtering, hybrid retrieval, ranking, query transformation and context selection may all be required to reach reliable retrieval quality.

Permissions and security

Internal and regulated knowledge systems often need document-level or user-level permissions so the retrieval layer cannot expose information a user should not see.

Evaluation

A production system needs representative questions, expected evidence and repeatable evaluation. Without this, teams can improve prompts without knowing whether retrieval quality is actually getting better. See our guide to RAG evaluation, groundedness and hallucination for a practical framework.

Application and integrations

Authentication, user interfaces, APIs, queues, document management and integrations with existing systems can represent as much work as the AI layer itself.

Pilot vs production RAG

A pilot should answer whether your data can support useful retrieval and whether the proposed workflow creates value. Production adds resilience, permissions, observability, evaluation, deployment and ongoing operations. Budgeting for those requirements from the beginning avoids rebuilding a promising prototype later.

A real production example

Our work on Vellum involved retrieval and document intelligence across more than 3,000 pages of mortgage guidance and documentation. Read our production RAG engineering deep dive for the lessons behind ingestion, retrieval, evaluation and production architecture.

You can also explore our RAG development services and the broader AI Development Cost UK guide.