RAG Systems
Retrieval-augmented systems that let an AI assistant answer questions grounded in your own documents and knowledge base, with sources attached to every answer.
Overview
Retrieval-augmented systems that let an AI assistant answer questions grounded in your own documents and knowledge base, with sources attached to every answer.
The problem
General-purpose language models don't know your internal documents, policies or knowledge base, and can produce confident-sounding but incorrect answers about anything specific to your business.
Our solution
We break your documents into retrievable chunks, index them for semantic search, and connect retrieval to generation so every answer is grounded in real source material — traceable back to exactly where it came from.
Key capabilities
What we build.
How we build it
Our process.
Document Ingestion & Chunking
Embedding & Indexing
Retrieval Pipeline Design
Grounded Generation
Evaluation & Source Attribution
Document Ingestion & Chunking
Embedding & Indexing
Retrieval Pipeline Design
Grounded Generation
Evaluation & Source Attribution
Technology
Built with purpose-chosen tools.
Business value
Answers a business can actually trust, because every one of them can be traced back to a real document instead of a model's memory.
Use cases
- Internal knowledge assistants
- Customer support grounded in documentation
- Legal and policy document search
- Research and knowledge discovery
- Technical documentation assistants
FAQ
Common questions.
RAG systems stay current by re-indexing documents as they change — no retraining required, so answers reflect your latest information.
Yes — source attribution is built in by default, so every answer can point back to the specific passage it was generated from.