RAG systems that answer from
your data, accurately, with citations
Retrieval-augmented generation pipelines that ground every answer in your own documents, knowledge base, or database, built with proper chunking, embeddings, and source tracking so answers are accurate, not hallucinated.
RAG done right, accurate retrieval,
not just a vector search demo.
A RAG system is only as good as its retrieval. We design the chunking strategy, embedding model, and ranking logic around your actual content, not a generic tutorial pipeline.
Vector Database Architecture
Pinecone, pgvector, or Weaviate, set up with the right indexing strategy, metadata filters, and hybrid search so retrieval stays fast and relevant as your data grows.
Chunking & Embedding Strategy
Content-aware chunking (not arbitrary character splits) and the right embedding model for your domain, legal, medical, technical, or general business content.
Citation & Source Tracking
Every answer links back to its source document and passage, so users can verify what the AI told them instead of just trusting it blindly.
Document Intelligence
PDF, Word, spreadsheet, and scanned document ingestion, turning unstructured files into a searchable, AI-queryable knowledge base.
Retrieval Evals & Tuning
Measured retrieval precision and recall against real queries, re-ranking and tuning until the system retrieves what actually answers the question.
Knowledge Base Chatbots
Customer-facing or internal chatbots that answer from your documentation, policies, or support history, with a clear fallback when the answer isn't in the data.
06Multi-Source RAG
Retrieval across multiple data sources, databases, APIs, and documents combined into a single grounded answer, with source-level access control.
07RAG Performance Optimisation
Latency and cost optimisation for existing RAG systems, caching, smaller embedding models, and retrieval pruning without losing answer quality.
Most RAG demos fall apart the moment real documents hit them
A RAG pipeline that works on three sample PDFs in a notebook often breaks on a real document set, inconsistent formatting, duplicate content, and edge cases. We design for that from day one.
Measured Retrieval Quality
We test retrieval precision against real queries before launch, not just "it looked right when I tried it."
Verifiable Answers
Source citations on every answer mean users, and you, can verify the AI isn't making things up.
Built for Your Data
Chunking and embedding strategy tailored to your actual content type, not a generic tutorial pipeline.
Full Code Ownership
The pipeline, prompts, and vector store configuration are yours, no vendor lock-in, no black box.
From raw documents to a reliable RAG system in 4 stages
Every RAG build follows the same disciplined process, because retrieval quality is won or lost in the details, not the demo.
Ingest, Index, Retrieve, Evaluate
Understand and prepare your source data
We review document formats, volume, and update frequency, then build the ingestion pipeline to match.
Chunk, embed, and index for retrieval
Content-aware chunking and embedding, indexed into the vector database with the metadata your use case needs.
Build the retrieval and answer pipeline
Hybrid search, re-ranking, and prompt design so the model generates answers grounded in retrieved passages.
Test against real queries, then ship
Retrieval and answer quality measured against a real query set before the system reaches your users.
RAG Systems, Frequently Asked Questions
Answers to the questions US & UK clients ask us most.
What is a RAG system and how does it work?
A RAG (Retrieval-Augmented Generation) system connects a large language model to your own documents, knowledge base, or database. When a user asks a question, the system retrieves the most relevant content using vector search, then the LLM generates an answer grounded in that content, with citations, instead of guessing. This makes answers accurate, up to date, and specific to your business.
How is a RAG system different from a custom-built ChatGPT?
A custom-built ChatGPT is the chat interface and model layer; a RAG system is what makes it answer correctly from your private data. We usually deliver both together: a retrieval-augmented backend so your custom ChatGPT responds only from your approved documents, reducing hallucinations and keeping answers on-brand.
Which vector databases do you use for RAG development?
We build production RAG pipelines on Pinecone, pgvector (Postgres), Weaviate, Qdrant, and Chroma, choosing the right one for your data volume, budget, and hosting requirements. We handle chunking strategy, embeddings, re-ranking, and citation tracking end to end.
Can a RAG system work securely with our private internal documents?
Yes. We can deploy RAG systems inside your own cloud or on infrastructure you control, with access rules, data isolation, and no training on your data. This is a common requirement for US and UK clients in finance, healthcare, and legal, and we build to those standards.
How long does it take to build a production RAG system?
A focused production-ready RAG system typically takes 3 to 6 weeks depending on data sources, integrations, and security needs. We start with a fixed-scope build so you get a working, measurable system rather than an open-ended demo.