RAG Implementation
Retrieval-Augmented Generation connects AI to your documents and knowledge bases. Instead of hallucinating, AI retrieves relevant information and generates accurate answers with source citations.
AI that answers from your knowledge
RAG systems retrieve the right information from your content before generating an answer. The result is accurate, cited, and grounded in your actual documents rather than the model's training data.
Document ingestion
We build pipelines that ingest your documents, PDFs, web pages, and knowledge bases into a searchable index. Automated re-indexing keeps everything current.
Intelligent retrieval
Semantic search with reranking to find the most relevant passages. Retrieval accuracy of 85-95 percent with good embeddings and reranking.
Cited generation
Answers include source citations so users can verify information. Transparent AI that shows its working, not a black box.
Production-grade RAG
A demo RAG system is easy. A production RAG system that handles access control, multi-source retrieval, and edge cases is the hard part. That is what we build.
Access control
Inherit permissions from source systems. SharePoint ACLs, AD groups, and database roles are respected. Users only see documents they are authorised to access.
Multi-source support
Connect to SharePoint, Confluence, databases, file shares, and custom systems. One retrieval layer across all your knowledge sources.
Continuous sync
Automated re-indexing through daily or weekly sync, or webhook-triggered updates when documents change. Your AI always has current information.
Where RAG delivers value
Internal knowledge search
Help employees find answers across policies, procedures, and documentation. Reduce time spent searching and asking colleagues.
Customer support
Power chatbots and agent assistants with accurate product and service information. Cited answers that staff and customers can trust.
Compliance and legal
Search regulatory documents, contracts, and policies with natural language. Find relevant clauses and precedents quickly.
Technical documentation
Help developers and engineers find answers across API docs, runbooks, and architecture documents. Reduce onboarding time for new team members.
How we implement RAG
Typical implementation takes 10 to 15 weeks depending on the number of sources and complexity of access control requirements.
Knowledge assessment
We review your document sources, test embeddings with samples, plan the architecture, and estimate costs for both the initial build and ongoing operation.
Pipeline build
We build the full RAG pipeline: ingestion, indexing, retrieval, reranking, generation, access control, and deployment with monitoring.
Optimise and scale
We measure retrieval quality, tune embeddings and chunking strategies, and expand to additional sources based on real usage patterns.
Frequently Asked Questions
How is RAG different from fine-tuning?
RAG retrieves knowledge from documents at query time, provides citations, and stays current. Fine-tuning bakes knowledge into model weights, has no citations, and requires expensive retraining to update.
How accurate are RAG systems?
With good embeddings and reranking, retrieval accuracy is typically 85-95 percent for correct documents in the top five results. Answer quality is 80-90 percent correctness, significantly better than pure LLM generation.
How do you handle access control?
We inherit permissions from your source systems. Search results are filtered by user permissions before retrieval, so users only see documents they are authorised to access.
Can RAG work with multilingual content?
Yes. We use multilingual embeddings that support 50 to 100 or more languages depending on the provider. Quality varies by language but is strong for major European and Asian languages.
Ready to connect AI to your knowledge?
RAG projects start with a knowledge assessment. Book a call and we will review your document sources and estimate what is possible.