Industries → Retail
Retail
Unified customer and inventory data that powers personalisation and demand planning.
22%
Fewer stockouts
Days → Hours
Forecast refresh time
200+
Stores on one model
Featured use case
A problem we solved
The problem, solved
A multi-region retailer ran demand forecasting on spreadsheets refreshed days apart. We unified POS, promotional, and weather data into one daily-refreshed model, cutting stockouts by 22%.
The challenge
Store-level demand forecasts relied on spreadsheets and static rules. POS, promotional, and weather data lived in separate systems with no shared model, and forecast refreshes took days — too slow to react to demand shifts.
How we transformed it
Karsient built a unified demand-signal pipeline blending POS transactions, promotional calendars, and weather data into a single feature set on Databricks, replacing spreadsheet-driven planning with an automated, governed model refreshed daily.
Architecture
How data moves through a Retail platform
Sources through to outcomes — the layers, and the live movement of data between them.
Live data flow · sources to outcomes
Benefits
What the business gained
One consistent demand model instead of per-region spreadsheets
Daily forecast refresh instead of a days-long manual cycle
Planner time redirected from data assembly to exceptions
A shared feature store reused across planning teams
What's next
Where this is heading
The same demand-signal pipeline is now being extended into dynamic pricing pilots, reusing the feature store built for forecasting rather than starting a new data project from scratch.
More industries
Where else we work
Let's talk