LLM Chat.
Ask questions about your data in plain English. Get answers grounded in your organization's own datasets, with full source traceability.
What it does.
LLM Chat gives every user in your organization the ability to query data without writing SQL or navigating complex dashboards. Users ask questions in natural language and receive answers sourced directly from your connected datasets.
Every response includes citations back to the underlying data, so analysts can verify answers and decision-makers can trust what they see. Conversation memory means follow-up questions build on prior context.
Key features.
Ask questions like "What were our top 5 programs by spend last quarter?" and get structured, accurate responses drawn from your data.
Every answer includes references to the specific tables, rows, and calculations used. Full auditability for compliance-sensitive environments.
Follow-up questions build on context from the current session. Ask "Break that down by region" without restating the original query.
Ingest policy documents, SOPs, and internal wikis to extend the chat beyond structured data. Ground responses in your institutional knowledge.
Configure topic boundaries, data access rules, and response policies. Prevent the model from answering outside its authorized scope.
Users can flag incorrect or incomplete answers. Feedback flows into a review queue for continuous improvement of response quality.