The Challenge
A supply-chain planner asking "which distribution points are at stockout risk this month" needs an answer that spans inventory data, forecast data, and risk scoring — three separate dashboards, each with its own drill-down path. Getting a straight answer means opening all three and cross-referencing by hand, then doing it again for the next question.
Planners doing this daily, at warehouse and SKU-level granularity, end up spending more time navigating dashboards than making decisions — and the same underlying question gets re-investigated from scratch every time someone else on the team needs the same answer.
“The question is never really "what's the stock level" — it's "is this a stock problem, a forecast problem, or a lead-time problem," and that's a decision, not a lookup.”
The Solution
The Saarthi Supply Chain Copilot Agent lets a planner ask the question directly and answers grounded in the platform's own governed data layers — inventory, forecast, and risk — citing exactly which dataset and refresh it drew the answer from.
The reason this needs to be agentic rather than a single lookup is that a real planning question almost never maps to one data source. "Is this a stock problem, a forecast problem, or a lead-time problem?" is a decision the agent has to make before it can answer — querying the relevant layer or layers, retrieving from each, and reconciling them into one grounded response, rather than returning whatever a single index happens to match.
What This Pattern Typically Delivers
Planners get one grounded, cited answer instead of three separate dashboard drill-downs. Because every answer is scoped to the platform's existing governed data layers, nothing about this widens who can see what — it just makes what's already there actually answerable in plain language.