RAG Systems
Custom RAG system development for enterprises. Knowledge base chatbots, document Q&A, and AI search solutions grounded in your actual data.
What I do
End-to-end RAG system development covering enterprise search, knowledge base chatbots, document Q&A, and vector database setup. Building accurate, reliable AI systems grounded in your data.
Enterprise Search
AI-powered search across your documents and knowledge base. Find information instantly using natural language.
Knowledge Base Chatbots
Chatbots that answer questions using your documentation. Accurate responses grounded in your actual content.
Document Q&A
Ask questions about your documents and get instant answers. PDFs, Word docs, and internal knowledge.
Vector Database Setup
Setting up and optimising vector databases for semantic search. Pinecone, Weaviate, and self-hosted solutions.
Data Pipeline Development
Building pipelines to process and embed your documents. Automated updates when content changes.
Accuracy Optimisation
Improving retrieval accuracy and response quality. Tuning embeddings, chunking strategies, and prompts.
Problems I solve
Helping businesses unlock their knowledge with AI-powered search and Q&A. Accurate, grounded AI responses that improve productivity and reduce support workload.
The Problem
Employees spending too much time searching for information
The Solution
RAG systems enable instant answers from your knowledge base. Natural language search finds relevant information in seconds.
The Problem
Customer support overwhelmed with repetitive questions
The Solution
Knowledge base chatbots handle common queries instantly. Accurate answers sourced from your documentation.
The Problem
AI chatbots providing inaccurate or made-up information
The Solution
RAG grounds AI responses in your actual documents. Citations and sources ensure accuracy and trustworthiness.
The Problem
Valuable knowledge scattered across documents and systems
The Solution
RAG systems unify knowledge from multiple sources. Single interface to access all organisational knowledge.
Technologies I work with
Modern RAG and vector search technologies. Using proven frameworks and databases to deliver accurate, reliable AI systems.
LLM Providers
Vector Databases
Frameworks
Embeddings
Document Processing
Backend
How it works
A structured approach to RAG development ensuring accurate retrieval and generation. Regular testing and tuning throughout.
Discovery
Understanding your knowledge sources, use cases, and accuracy requirements. Assessing document types and volumes.
Architecture Design
Designing RAG architecture, selecting vector database, and planning document processing pipeline.
Development
Building document pipelines, setting up vector database, and implementing retrieval and generation.
Testing & Optimisation
Testing retrieval accuracy, tuning embeddings and prompts, and deploying with monitoring.
Frequently asked questions
Common questions about RAG systems. If you have a specific question not covered here, feel free to get in touch.
RAG (Retrieval-Augmented Generation) combines information retrieval with AI generation. It grounds AI responses in your actual documents, preventing hallucinations and ensuring accuracy. Essential for enterprise AI applications requiring reliable information.
RAG can process PDFs, Word documents, text files, HTML pages, Confluence pages, Notion docs, and more. We can also integrate with databases, APIs, and other data sources.
Accuracy depends on document quality and system tuning. Well-implemented RAG systems achieve high accuracy with proper chunking, embedding selection, and prompt engineering. We include citations so users can verify sources.
Yes. Modern embedding models and LLMs support multiple languages. We can build multilingual RAG systems that retrieve and respond in the user language.
We build automated pipelines that detect document changes and update the vector database accordingly. This ensures the RAG system always has current information.
Yes. RAG systems can be integrated via APIs into your existing applications, websites, chat platforms, or used as standalone tools. We build to your integration requirements.