inspection software5 min read
Your Inspection Software Is Great at Paperwork. It Should Be Catching Faults.
Most inspection software digitizes the checklist and stops there. The next step is software that looks at the photo and tells the crew what is wrong, before they leave the site.
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computer vision5 min read
We Pointed Off-the-Shelf AI at Real Inspection Photos. It Detected Almost Nothing on Four of Five Fault Types.
Before we built custom models, we tried the shortcut: ask a powerful general-purpose vision AI to find faults with no training. Here is what it found, what it missed, and why "train on your own data" wins for field inspection.
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computer vision5 min read
From Trained Model to Your Crew's Phone
Training a custom fault-detection model is only half the job. Here's how Forge packages that model to run directly on a field worker's phone, and how we proved the on-device version still scores detections almost exactly like the full server version.
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computer vision5 min read
Can You Trust Your Field Photos? Catching the "Photo of a Screen" Trick
Every inspection program quietly assumes the photo is real. We built a detector that catches staged re-captures about 90% of the time, on an ordinary CPU.
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on-device OCR4 min read
Why Your Field App Should Read Serial Numbers Without a Signal
Crews work in dead zones, yet reading a serial number still tends to demand a cloud round-trip. We tested fully offline reading: 98-99% accuracy on short codes.
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low-data training5 min read
You Don't Need Thousands of Photos to Train a Reliable Inspection Model
The biggest fear teams have is the labelling mountain. We tested how few photos it takes: strong accuracy from as few as 15 examples per defect type.
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computer vision4 min read
We Ran a General-Purpose AI Against a Model Trained on Real Faults. The Accuracy Gap Was Enormous.
We ran the head-to-head on real field images: a general-purpose AI vs a model trained on real faults. Here are the numbers, and what they mean for your costs.
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