RAG Pipelines & Knowledge Bases
I build Retrieval-Augmented Generation systems that give your AI agents access to your organisation's knowledge in real time. Instead of relying on what a model was trained on, RAG lets agents pull in relevant documents, records, and data at the moment they need it — grounding their responses in your actual business context.
Your knowledge is the competitive advantage
Foundation models know a lot about the world, but they know nothing about your business until you give them access.
Every organisation has proprietary knowledge locked up in documents, databases, wikis, and the heads of experienced staff. RAG bridges the gap between general-purpose language models and this domain-specific knowledge. When an agent needs to answer a question about your products, policies, or processes, it retrieves the relevant information from your knowledge base and uses it to generate an accurate, grounded response.
The difference between a helpful AI assistant and one that hallucinates confidently is almost always the retrieval layer. I build RAG systems that are tuned for precision — retrieving the right context, not just the most similar text. This involves careful attention to how documents are chunked, how embeddings are generated, and how retrieval results are ranked and filtered before they reach the model.
What I build
End-to-end retrieval systems from document ingestion through to agent-ready context delivery.
Document ingestion and chunking
Automated pipelines that process your documents — PDFs, web pages, Confluence wikis, Google Docs, Notion databases — into clean, well-structured chunks. I use chunking strategies tailored to your content type, whether that's semantic splitting, hierarchical chunking, or sliding windows with overlap.
Vector database setup
Selection, configuration, and deployment of the right vector store for your scale and requirements. I work with Pinecone, Weaviate, pgvector, Qdrant, and ChromaDB, choosing based on your existing infrastructure, query patterns, and operational preferences.
Context-aware retrieval
Retrieval logic that goes beyond naive similarity search. I implement re-ranking, metadata filtering, multi-query strategies, and contextual compression to ensure the model receives the most relevant information for each specific query rather than a pile of vaguely related text.
Knowledge base maintenance
Systems that keep your knowledge base current as documents change. Incremental ingestion detects new and updated content, re-processes affected chunks, and updates embeddings without requiring a full rebuild. Stale content is flagged or removed automatically.
Hybrid search strategies
Combining vector similarity search with traditional keyword search for better recall across different query types. Some questions are best answered by semantic matching; others need exact term matching. Hybrid search handles both gracefully.
Evaluation and quality assurance
Systematic testing of retrieval quality using relevance scoring, ground truth datasets, and end-to-end answer evaluation. I build feedback loops that identify retrieval failures so the system improves over time rather than degrading silently.
Hiring me for RAG Pipelines & Knowledge Bases
A retrieval system over your documents and records, evaluated on real questions, that your agents and chat interfaces can query. Where general embeddings miss your domain, I can fine-tune the embedding model or reranker.
Who it's for
- Teams whose answers are buried in documents, wikis and tickets
- Agents that need your knowledge, not what the model was trained on
- Existing RAG setups that return the wrong passages
- Organisations that need the knowledge base hosted privately
What you get
- An ingestion pipeline: parsing, chunking and metadata
- A vector or hybrid search index
- Retrieval wired into your agents, chat UI or MCP
- A retrieval eval on real questions
- A process for keeping the index current
What I need from you
- Access to the documents and data sources
- A list of real questions and who asks them
- Access rules: who may see what
- A contact who knows the content
How it runs
Typically 4–8 weeks, depending on how many sources and formats.
- Week 1
Sources and questions
Inventory the content and collect real questions people ask.
- Weeks 2–4
Ingestion and index
Parse, chunk and index; choose search strategy.
- Weeks 3–6
Evaluate and tune
Measure retrieval on the question set and tune.
- Weeks 5–8
Integrate and deploy
Connect to agents or chat and hand over.
Scoped after a short call and quoted as a fixed price, paid against milestones.