Our models crunch I-V curve data as well as images

Using concepts such as advanced image preprocessing, and harmonic(quantum) neural networks, we're able to integrate the physics of material degradation with the pure number crunching game of machine learning.

Why physics integrated machine learning?

Lab scale ML models working with well-behaved lab environment data, just can't do a reliable job when deployed over drones or robotic cameras exposed to environmental disturbances and non-uniform conditions.

They often end up missing faults or give false positives, leading to massive loss in accuracy and underreporting. However, our models are able to reliably deliver accurate results.

Why do we use drones?

1. Speedy execution, autopilot at its finest

   Drones zip through inspections at a fraction of the time required for manual methods. Perfect data capturing in the blink of an eye.

2. Extremely low cost per MW inspection

   Zero manpower costs.

3. Enhanced safety

   Solar PV farms operate at high voltages. Use of drones ensures zero human proximity to the solar panels, eliminating the risk of electrocution or other human/equipment risks associated with manual inspection.

4. Comprehensive coverage at all hours of the day (and night)

   Everything gets covered, every difficult corner, in one take. Daytime as well as night time EL imaging can be effortlessly executed.

5. Consistent results

   Accurate and reliable findings adhering to IEC standards, acceptable by all stakeholders. Angle of imaging, time of imaging, and all the process variables can be controlled to the T.

6. Automated workflows & data processing

   Users (insurers) can trigger inspections or curate flight plans remotely from anywhere, capture as much data, with as much granularity as they want. The drones are autonomous and don't need any human intervention. Tell our platform what you want to be looking at, we'll get you detailed insights and supporting data.