Lunai data science starts with a decision owner. If nobody will change a purchase order based on the forecast, we do not build the model. When they will, we productionise it: features, monitoring, rollback.
Problems we take on
We favour classical, explainable methods unless the lift from deep models is proven on your data, not a public benchmark.
- Demand and inventory forecasts by SKU-location
- Churn, credit and collections scores with reason codes
- Maintenance windows from sensor and work-order history
- Price and mix experiments with guardrails
From notebook to night job
Feature stores, training schedules, and a simple UI or API for the planner who uses the number.
- Backtests your finance partner can argue with
- Drift alerts when the world moves
- Human override with an audit trail
Applied data science from Lunai is measured in inventory days, downtime hours or recovered rupees in India businesses.