Fijomi Systems Intelligent search grounded in your own documents.
Retrieval-augmented generation (RAG) lets people ask questions in plain language and get fast, context-aware answers grounded in your own documents and knowledge bases, rather than in the model’s guesses.
What we build
- Connectors that bring in content from the places it already lives.
- Indexing and retrieval tuned to how your documents are written and used.
- Answers that draw on retrieved passages from your own content.
- Private deployment with open-source models where documents are confidential.
Built on a data architecture foundation
Good retrieval starts with good data. Our founder has more than 30 years of experience designing databases and data architectures, across Oracle, SQL Server, PostgreSQL, MySQL and more, and we bring that discipline to how your content is structured, indexed and kept up to date.
Tools we use
We build RAG pipelines with frameworks such as LlamaIndex and LangChain, using commercial or open-source models depending on cost and privacy needs. See our technology stack.
Frequently asked questions
What is RAG?
Retrieval-augmented generation is a technique where the system first retrieves relevant passages from your own content, then asks a language model to answer using those passages, so answers are grounded in your data.
Can RAG run without sending our documents to a public AI service?
Yes. Retrieval and the language model can both run inside your own infrastructure using open-source models. See Private & On-Premises AI.