This is a Retrieval-Augmented Generation system, grounded in a sample database. Pick an industry, ask a question, and watch it find the exact records, follow the relationships between them, and answer — who, what, how much — with every fact cited back to your data.
Connect your databases, documents and systems — nothing is invented.
Your question finds the most relevant records across every table.
It joins and chains across records — relational, multi-hop lookups.
A grounded reply, with citations pointing back to the source rows.
The demo above simulates a real Retrieval-Augmented Generation pipeline. In production, your knowledge base is built and queried through six stages — turning raw, messy data into grounded, cited answers.
Nothing is memorised or invented. The model only ever sees what retrieval hands it from your data.
Connect everything you already have — structured databases and tables, plus unstructured files like PDFs, documents, emails and tickets.
Sanitise, de-duplicate and normalise the content, then split long documents into passages (chunking) so each piece is retrievable.
Turn the content into vector embeddings and build the index — a vector database for meaning, a knowledge graph for relationships, or both together.
Your question is embedded too, then matched against the index to pull the most relevant records and passages — following links for multi-hop questions.
The retrieved context is handed to a large language model, which reads only that evidence and composes the answer — grounded, never guessed.
You get a clear, grounded reply with citations — every fact traceable back to the exact source record, so it can be audited and trusted.
Personal and sensitive data is protected before it ever reaches the LLM — and every response is screened after generation, before it reaches a user. The model only sees masked, permission-checked context.
We connect to your databases, documents and tools — and ship a private, cited assistant your team can trust.