AI Rooftop Solar Assessment

AI-powered analysis of rooftop solar potential from satellite and aerial imagery

The Problem

“Slow, costly rooftop solar site assessment bottleneck”

Organizations face these key challenges:

Manual rooftop measurements and shading analysis create long lead times and inconsistent quality across assessors and regions

High rate of wasted site visits and redesigns due to inaccurate roof geometry, obstructions, or structural constraints discovered late

Inaccurate production and savings estimates lead to mispriced proposals, customer mistrust, and higher cancellation rates after site survey

Impact When Solved

50–80% fewer pre-sale truck rolls, saving $150–$400 each and freeing field crews for construction work2–5 day assessment cycle reduced to minutes, improving lead-to-proposal speed and lifting conversion by ~5–15%30–60% reduction in design iterations and change orders through more accurate roof plane and shading-driven yield estimates

The Shift

Real-World Use Cases

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.

Time-SeriesEmerging Standard

8.5

Artificial Intelligence in Renewable Energy Optimization

This is like giving a wind farm or solar plant a very smart autopilot. It studies weather, demand, prices, and equipment behavior, then constantly tweaks how the system runs so you get more clean energy for less money and wear-and-tear.