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Manthan - Agentic Buisness Analyst for Enterprise
Deployed on Vultr Backend - https://66.245.207.186 , Using Vultr Serverless Inference and Vector Storage. Manthan thinks like an analyst. The agent reads your question, picks the right tool from its toolbox, and works through the answer step by step — a quick SQL aggregation when you ask "what's our revenue this quarter," a multi-step Senior Data Scientist Level Python forecast when you ask "what's it going to be next quarter," a chart or a full Interactive PowerBI Style dashboard when the answer is better shown than told. The reasoning is powered by MiniMax M2.7 running on Vultr Serverless Inference. Every figure it cites is checked against a real tool call before it ships, so nothing is fabricated. We use Vultr Vector Storage to give the agent a memory — it remembers the assumptions, definitions, and business meanings specific to your data, so the second time you ask, you don't have to re-explain. The whole thing ships as one Docker image; spin it up on Vultr in one go via the bundled cloud-init. Drop in a CSV, point it at a Postgres warehouse, or plug a Stripe / Salesforce / HubSpot / Shopify / Notion API key into the Apps tab — Manthan ingests the source, profiles it, and exposes it to the agent as a first-class entity. From there, anyone in the company can ask. For a small team, that's the analyst you couldn't afford to hire, available 24x7 and math-perfect. For a large org, it lifts the "pull me the numbers" ticket queue off your data team so they can do the strategic work only humans should be doing.
19 May 2026
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.
19 May 2026
