Skip to content
Artificial IntelligenceR&DEnergy optimisation

Energy optimisation — Hospital

Reinforcement-learning control of chillers, compressors and furnaces. Delivered for a hospital client in Nagpur.

Location
Nagpur
Year
2025
Duration
10 weeks
Team
7 engineers
Interactive 3D · drag to rotate
18%
energy saved
15.9 mo
payback
2,526
t CO₂/yr avoided

The challenge

The hospital team was relying on manual effort that could not keep up with volume, and every miss had a direct cost.

What we built

We owned the whole stack — hardware, firmware, models and dashboards — using Ray RLlib, Python, BACnet so there was one accountable team.

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

  • Operator training delivered in Gujarati, Hindi and English
  • Documentation, drawings and source handed over at close
  • Remote monitoring and over-the-air updates included
  • Integrated with existing PLC, ERP or GIS systems — no rip-and-replace

Have a problem that needs a machine built for it?

Tell us the constraint — line speed, accuracy, environment, deadline. An engineer will read it and reply with a straight answer, including when the answer is that you don't need us.

We reply to every enquiry within one business day.