Our technology
RAG & Knowledge Search
Chat with your own documents: answers drawn from your manuals, contracts, procedures and tickets, each one with a link to its source.
Retrieval-augmented generation (RAG) connects a language model to your own knowledge: manuals, contracts, procedures, drawings, tickets and e-mail archives. Employees and customers ask in plain language and get an answer built from your documents, not from the public internet.
Every answer cites the passages it used, so a person can check it in seconds. Access rights are respected, so people see only what they are allowed to see, and the index stays on your own systems.
Research on arXiv
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- Dense Passage Retrieval for Open-Domain Question Answering
- Retrieval-Augmented Generation for Large Language Models: A Survey
Used in
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