RAG Implementation Services
Retrieval-Augmented Generation for enterprise knowledge. Ground AI answers in your documents with source citations and up-to-date information.
Why RAG instead of fine-tuning?
RAG delivers accurate, cited answers from your own documents without the cost and complexity of model fine-tuning.
Up-to-date knowledge
RAG uses current documents. Add a new doc today and AI knows about it tomorrow. Fine-tuned models are frozen in time.
Source citations
RAG answers cite their sources so users can verify accuracy. Fine-tuned models cannot cite sources.
Reduced hallucination
AI is grounded in retrieved documents, dramatically reducing hallucination risk compared to fine-tuned models with no grounding.
RAG system architecture
Six core components that form a production RAG pipeline, from document ingestion through to cited answers with access control.
Document ingestion and chunking
Connect sources, extract text, chunk documents into 500-1000 token segments, and preserve metadata for retrieval.
Embedding and indexing
Generate embeddings with Cohere, OpenAI, or custom models and store in a vector database for fast similarity search.
Semantic search
Generate query embeddings, retrieve top candidates from the vector database in sub-100ms response times.
Reranking
Pass query and candidates to a reranker for 20-40% accuracy improvement over vector search alone.
Generation with citations
Send query and reranked chunks to the LLM to generate grounded answers with source citations users can verify.
Access control
Inherit permissions from source systems so users only see content they are authorised to access.
RAG implementation process
Knowledge audit and integration
Identify sources, review permissions, and set up connectors. Typically 2-3 weeks.
Embedding, indexing, and pipeline build
Choose embedding model, set up vector database, chunk and index documents, implement retrieval with reranking, and build generation with citations. Typically 5-7 weeks.
Testing, deployment, and monitoring
Test with real questions, measure accuracy, optimise parameters, deploy to production, monitor usage, and train users. Typically 3-5 weeks.
Deploy a production RAG system
Book a consultation to discuss RAG implementation for your enterprise knowledge.