The context
With the rise of EVs, shifting buyer preferences and sharp regional differences, India's automotive market needs faster, sharper insight. A leading automotive intelligence firm set out to turn VAHAN registration data into real-time competitor tracking and predictive analytics.
What was holding them back
VAHAN data varies by state in format and availability.
No model, fuel-type or district-level views.
No AI forecasts to guide planning.
Reliance on monthly reports slowed market response.
Dashboards were not tailored to different user roles.
What they set out to achieve
- Ingest and normalise data from 1,300+ RTOs every day
- Real-time dashboards for every team
- Machine-learning sales trend forecasts
- Micro-level insight down to state, district and RTO
- Secure, scalable access on any device
What we built
Live data sync
Daily VAHAN data pulled and standardised through ETL pipelines.
Vehicle-segment views
2W, 3W, 4W and heavy-vehicle views by brand, fuel and trend.
Regional insights
Analytics down to state, district and RTO level.
Smart forecasting
ARIMA, LSTM and XGBoost models project sales trends.
Fuel-mix tracker
Adoption patterns and fuel-preference shifts by region.
Alerts & scenarios
Real-time alerts, what-if modelling and peer tracking.
The results
- EV penetration tracking guided model roll-outs and infrastructure planning
- Production aligned to forecast demand, reducing overstock
- Leaders moved from static MIS to weekly, dynamic insight reviews