RAG systems that answer from
your data, accurately, with citations
Retrieval-augmented generation pipelines grounded in your documents, not guesses. Built for accuracy in production, not demos.
What Goes Into a Production RAG System
Every RAG build we ship covers these fundamentals, tuned to your actual content, not a generic tutorial pipeline.
Vector Database Architecture
Pinecone, pgvector, or Weaviate, indexed and tuned so retrieval stays fast and relevant as your data grows.
Chunking & Embedding Strategy
Content aware chunking and the right embedding model for your domain.
Citation & Source Tracking
Every answer links back to its source so you can verify it.
Document Intelligence
PDFs, spreadsheets, and scanned files turned into a searchable knowledge base.
Retrieval Evals & Tuning
Precision and recall measured against real queries, not guesswork.
Knowledge Base Chatbots
Customer or internal chatbots that answer only from your data.
Multi Source RAG
One grounded answer pulled from multiple data sources and APIs.
Most RAG demos fall apart the moment real documents hit them
A pipeline that works on three sample PDFs often breaks on messy, real-world data. We design for that from day one.
Measured Retrieval Quality
Tested against real queries before launch, not eyeballed.
Verifiable Answers
Every answer cites its source, so nothing is taken on faith.
Built for Your Data
Chunking and embeddings tuned to your actual content.
Full Code Ownership
The pipeline and prompts are yours. No lock in.
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.
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 with the metadata your use case needs.
Build the retrieval and answer pipeline
Hybrid search, re ranking, and prompt design so answers stay 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.