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🧠 AI-Powered Accuracy

Our AI Measures Roofs With ~98% Accuracy

On average, our AI is off by just 54 sqft when compared to a manually measured roof. That is less than 2 bundles of shingles.

📝 77% more accurate since 2024 and still improving
Example showing what a 66 square foot roof measurement error looks like

Validated using real roof measurements

✓ Tested against 7,000+ manually measured roofs
✓ AI accuracy improving with every model release
✓ Highest accuracy on typical residential homes

Average AI measurement error across 7,000+ manually measured roofs.

🧠 AI That Keeps Getting Smarter

77% More Accurate Since 2024, And We Are Not Done Yet

Our AI has reduced average error from 231 sqft down to just 54 sqft, with a target of 33 sqft (1 bundle of shingles) by Jan 2027.

Actual Error
Goal: 1 bundle (33 sqft)
Projected (Jan 2027)

Every new AI model release gets closer to our goal of 33 sqft average error by Jan 2027, approximately 1 bundle of shingles across all roof sizes.

🧠 AI Model Validation

AI Predicted vs Manually Measured Roof Size

Each dot is a roof. The closer the dots are to the diagonal line, the more accurate our AI is. As you can see, they cluster tightly.

Perfect prediction

Each point represents a roof measured by the AI system and compared against a manually measured roof.

🧠 AI Error Distribution

Most Roofs Are Well Under Our 54 sqft Average Error

This chart shows how our AI error is distributed across thousands of roofs. The majority cluster far below the 54 sqft average, with most under 50 sqft of error.

Across 7,000+ roofs, our AI averages just 54 sqft error. Most measurements are even closer than that.

🧠 AI Accuracy By Roof Size

Our AI Is Most Accurate On Typical Residential Homes

For most homes under 3,500 sqft, our AI averages under 54 sqft error. Larger, more complex roofs have slightly higher variance but still deliver strong results.

Roof measurement accuracy by roof size examples

For smaller roofs under 1,300 sqft, our AI averages just 32 sqft of error, less than 1 bundle of shingles. As our models improve, we expect all roof sizes to reach this level.

🧠 Methodology

How We Test Our AI

Mean Absolute Error (MAE) is the average difference in sqft between what our AI measures and what a human measures by hand. Our current MAE is 54 sqft.

Our AI predictions are compared against thousands of manually measured roofs.
We evaluate accuracy using dozens of metrics. Mean Absolute Error (MAE) is currently the primary one.
Results are broken down by roof size, region, and complexity to identify where the model performs best and where it can improve.
Deep Dive

Technical Details

For investors and technical readers looking for more depth.

Our evaluation dataset consists of 7,000+ residential roofs that have been manually measured by professional estimators. Each address is geocoded and matched against our AI predictions to compute error metrics.
Our AI models are trained on high-resolution aerial imagery combined with 3D data. We use a combination of computer vision and deep learning techniques to detect roof planes, edges, and penetrations. Each model iteration is trained on an expanded and improved dataset.
Complex roofs with many facets, dormers, valleys, and penetrations present more opportunities for measurement variance. Simple hip and gable roofs are inherently easier for our AI to measure precisely. Larger structures also tend to have more architectural complexity.
We use Mean Absolute Error (MAE) as our primary metric. For each roof in the evaluation set, we calculate the absolute difference between the AI-predicted area and the manually measured area, then average across all roofs. This gives a single, easy-to-understand number representing average measurement error in square feet. Our current MAE is 54 sqft.
Most measurement providers rely on 2D satellite imagery with assumptive pitch calculations and standardized waste factors. Instant Roofer uses actual 3D data to measure roof geometry, eliminating the guesswork involved in estimating pitch and waste from a flat image. This means our AI measurements reflect the true surface area of the roof rather than a mathematical estimate based on assumed angles. Additionally, our AI models are continuously evolving. We release new model versions regularly, and each release delivers measurable accuracy improvements.
For smaller roofs under 1,300 sqft, our AI already averages just 32 sqft of error, less than 1 bundle of shingles. Based on our current improvement trajectory, we project reaching 33 sqft average error across all roof sizes by late 2026 to early 2027. Larger and more complex roofs will take longer due to increased architectural complexity, but each new AI model release delivers measurable improvements.

See the Accuracy for Yourself

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