Predictive analytics & decision intelligence for Madan Mohan Gradings.
Madan Mohan Gradings — producer of Swastik Brand Aata and Kakaji Brand Wheat — generated large volumes of sales data with no digital infrastructure to act on it. Forecasting, distributor prioritisation, and expansion planning ran on instinct. They needed traceable, automated decisions without disrupting daily operations.
Stand up a decision-intelligence layer: an autonomous agent network on top of cleaned data pipelines that handles forecasting and logistics by reading existing CRM streams, with full audit trails on every decision.
Enterprise architecture
Schema mapping & Airflow DAGs
Fragmented sales data was the blocker. ML-managed Airflow DAGs clean, structure, and merge disparate datasets into a unified, encrypted pipeline inside a private VPC.
Multi-agent Python clusters
Two specialised agent clusters in Python.
Demand forecasting agent runs multi-signal time-series analysis to predict SKU-level demand by region.
Distributor-logic agent ranks distributors using context-aware risk modelling.
Geolocation routing
Spatial algorithms cross sales density with logistics cost to output specific geographical expansion targets directly into executive dashboards.
SOC 2 Type II architecture
The full ingestion-to-output pipeline was designed against SOC 2 Type II controls for confidentiality, integrity, and availability.
Immutable audit logging
Every distributor-prioritisation decision is recorded immutably; executives can trace exactly why a given distributor was chosen.
MLOps drift monitoring
Forecast accuracy is tracked continuously. When it drops below threshold, the system alerts engineers and queues recalibration.
Role-based access
IAM-governed RBAC restricts dashboard and raw-data access by geography, so branch managers see only their authorised regions.
Business outcomes
The decision-intelligence pipeline runs every morning at 04:00 IST. Airflow DAGs process the previous day's pan-India sales data with no human intervention. The MLOps drift-monitor has triggered automatic recalibrations twice to absorb supply-chain disruptions, holding forecasting accuracy above 96%. What started as a predictive-analytics project is now the operational nervous system routing thousands of tons of Swastik Brand Aata to demand each week.