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Artificial IntelligenceDeliveredEnergy optimisation

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
Interactive 3D · drag to rotate
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

  1. Phase 1

    Discovery sprint and site survey

  2. Phase 2

    Prototype built and tested in the Motera lab

  3. Phase 3

    Pilot on site with before/after measurement

  4. 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

Have a problem that needs a machine built for it?

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