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Smarter Routing. Verified Service Quality. Predictable Staffing.

Gyan helps Heating, Air-conditioning, and Building Services streamline routing, validate service quality, and forecast staffing demand, so they can deliver better service, reduce costs, and scale reliably.

How We Can Help Your Company

Smarter Routing

Manual scheduling, inefficient routes, and last-minute disruptions waste time and fuel every day.

Our AI route optimization solution reduces drive time, fuel costs, scheduling conflicts, while meeting service levels. It automatically assigns the right technician to the right job while adapting in real time to emergencies, delays, and last-minute changes.

Image by Deva Darshan
HVAC Technician Working

Verified Service Quality

Companies capture millions of service photos each year, but reviewing and assessing them manually is slow, error-prone, costly, and disconnected from daily operations.

Our AI solution automatically analyzes images for proof of service and state of asset reports, thus making quality assurance scalable and practical. It delivers clearer insights into system health, improves customer retention, enables upselling and proves service quality on every job.

Predictable Staffing

Current forecasting processes are inaccurate, cumbersome, and unable to keep up with changing demand.

Our AI forecasting solution helps you anticipate demand before peak seasons hit and plan staffing with confidence. It creates accurate workforce predictions reduce overtime, prevents understaffing, and supports growth by giving you the foresight needed for recruiting, onboarding, and training.

AC Maintenance Task

Case Study:
Servicing Staff Forecasting

Key Challenges for Client
  • Current forecast processes are inaccurate and cumbersome
  • Recruiting, onboarding and training lead time is long and  requires better forecasting to support growth in demand

The Goals
  • Timely servicing of SLAs
  • Balance shortages with overstaffing on an annual basis to minimize cost
  • Overtime operates below a preset threshold
  • Employee burnout must be managed below a preset level
Our Solution
  • Demand forecasting model taking into account historical data of servicing time, travel time, maintenance schedule, seasonality, etc.
  • Staff forecasting model, based on the demand forecast.
  • Capability to run dynamic simulations. What-if analysis and recommendations.
  • Cross-location forecasting / optimization capability
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Schedule

Ready to leverage AI to reduce costs and to scale reliably?

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