Energy optimisation — Pharmaceutical
Reinforcement-learning control of chillers, compressors and furnaces. Delivered for a pharmaceutical client in Kutch.
- Location
- Kutch
- Year
- 2026
- Duration
- 20 weeks
- Team
- 4 engineers
- 12%
- energy saved
- 16.4 mo
- payback
- 556
- t CO₂/yr avoided
The challenge
Data existed but lived in spreadsheets and legacy systems, so decisions were made on instinct rather than evidence.
What we built
A phased rollout: a measured pilot first, then scale-out. The core runs on Ray RLlib with Python, integrated into existing systems through TimescaleDB.
Delivery timeline
Phase 1
Discovery sprint and site survey
Phase 2
Prototype built and tested in the Motera lab
Phase 3
Pilot on site with before/after measurement
Phase 4
Scale-out, training and handover
Highlights
- Remote monitoring and over-the-air updates included
- Designed, built and supported from Motera, Ahmedabad
- Before-and-after measurements agreed with the client up front
- Integrated with existing PLC, ERP or GIS systems — no rip-and-replace
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