Manthan - Agentic BI Analyst For Enterprise

Manthan turns enterprise data into auditable intelligence without locking companies into a proprietary BI stack. Teams can connect CSVs, databases, cloud storage, or SaaS tools, then define business metrics once through a governed semantic contract. Instead of guessing what “revenue,” “active customer,” or “margin” means, Manthan uses organization-approved definitions before answering every query. Ask questions in natural language like: “Why did Q3 margin decline?” “Which accounts are most at risk?” “What sectors are absorbing the most capital?” “Generate a dashboard for churn analysis.” Manthan plans investigations, validates every query against the dataset schema, generates SQL and Python workflows automatically, and returns explainable results with full auditability. Key capabilities Governed semantic layer with typed dataset contracts AI-generated SQL with schema validation Stateful Python analysis for forecasting, clustering, and statistical testing Interactive dashboards and visualizations Clarification workflows for ambiguous business logic Cross-session memory for ongoing investigations Click-to-audit traceability for every metric and result Self-hosted and model-agnostic architecture Every answer is fully traceable. Users can inspect metric definitions, applied filters, generated SQL, dataset versions, rows scanned, and analysis workflows directly from the interface. This makes Manthan usable in environments where explainability, governance, and trust matter. Unlike closed enterprise BI copilots that lock organizations into proprietary ecosystems, Manthan is fully open-source, self-hosted, model-swappable, and infrastructure-independent. Organizations own their analytical trust layer instead of renting it from a vendor.