Case Study - Demeter: Geo-CRM for market expansion
Demeter is a geo-CRM built for CloudKitchens. It aggregated census data, maps, parking density, and machine learning insights to show sales reps where to buy the next kitchen space.
- Client
- CloudKitchens
- Year
- Service
- Data engineering, Machine learning, CRM development
Overview
In 2018, CloudKitchens was scaling fast. But finding the right buildings was a grind: sales reps hunted by instinct, chasing incomplete lists, often missing the best spots.
We built Demeter, a geo-CRM that pulled together U.S. Census data, maps, parking density metrics, and third-party feeds. At its core was an ML pipeline (built on Ludwig) that scored and ranked properties by expansion potential.
Instead of cold scouting, reps saw interactive maps with clear “hot zones” for acquisition. The result: faster expansion, fewer missed opportunities, and a repeatable system that turned gut feel into data-driven strategy.
What we did
- Data ingestion (Census, maps, parking APIs)
- Machine learning pipeline (Ludwig)
- Geo-visualization dashboards
- Custom CRM integration
- Less time scouting locations
- 70%
- Faster market entry
- 3x
- Data sources unified
- 100+
- Geo-CRM purpose-built for kitchens
- 1st