RAG Systems

Retrieval-augmented generation systems that give AI accurate access to your documents, knowledge base, and proprietary data. Citations included.

What is RAG?

RAG (Retrieval-Augmented Generation) gives AI accurate access to your documents and knowledge base. Instead of relying on pre-training alone, the AI searches your data, retrieves relevant information, and provides answers with citations. You need RAG if you want AI to answer questions about your documentation, policies, or proprietary information accurately.

What You Get

Document Ingestion

Automated pipeline for PDF, DOCX, HTML, and other formats

Vector Database

Setup with Pinecone, Weaviate, or pgvector based on your needs

Semantic Search

Hybrid retrieval combining dense and sparse vectors for accuracy

Citation Tracking

Every answer includes source attribution and links

Chunking Strategy

Optimal document chunking for retrieval quality

Re-ranking

ML-powered re-ranking for most relevant results first

Common Use Cases

1

Support Bot RAG

Index support docs, answer customer questions with citations, deflect 60% of tickets

2

Internal Knowledge AI

Search across Confluence, Notion, Google Docs. Employees get instant answers

3

Legal Document Search

Search case law, contracts, legal precedents with semantic understanding

4

Technical Documentation

API docs, codebase documentation, troubleshooting guides

Our Process

1

Document Audit

Inventory your document sources, formats, and update frequency

2

Vector DB Setup

Choose and configure vector database, set up embedding pipeline

3

Retrieval Implementation

Implement search with re-ranking and Claude integration

4

Citation & Feedback

Add citation tracking, build feedback loop for improvements

Pricing

£40,000 - £80,000

Typical project range

2-3 weeks

Average delivery time

Simple RAG systems with single data source start around £40K. Complex multi-source RAG with multiple document types, hybrid search, and custom frontends range £60K-£80K.

Related Case Studies

SaaS

Support Bot RAG

RAG system over 500+ support docs. Bot answers with accurate citations. Deflected 60% of tickets, response time from 24-48hrs to instant.

Timeline:2 weeks
Tech Company

Internal Wiki AI

Claude-powered search over Confluence, Notion, Google Docs. 200+ daily queries, 90% employee satisfaction.

Timeline:2 weeks

Ready to Build Your RAG System?

Schedule a 30-minute call to discuss your document sources and how RAG can make them AI-accessible.