What is SCOPR.AI?
SCOPR.AI is designed to assess roofs, automatically focusing on their quality and structure. It looks at key features like how straight the eaves, ridges, and tiles are. By using AI and computer vision, we aimed to make roof inspections faster and more efficient.
But how do you create an AI model that works quickly and accurately in real-time?
Building this wasn’t easy. We ran into several challenges along the way, from making the AI fast enough to work on mobile devices to ensuring it could be used across different platforms. Here’s how we tackled these problems.
1. Making the AI model fast and accurate
For SCOPR.AI to be useful, it needed to be both precise and quick. We had to balance accuracy with speed.
Solution: We used special techniques to compress the AI model and clean up the data it processes. This helped maintain high accuracy while making the AI faster and more efficient.
2. Getting AI to run on mobile devices
Mobile devices have limited power, which makes running AI models tricky. If the model is too big, it slows down and drains the battery.
Solution: We converted our AI model into a smaller version (using TensorFlow Lite) and applied techniques to reduce its size without sacrificing accuracy. This allowed it to work smoothly in real time.
3. Improving AI accuracy
AI models sometimes make mistakes, especially in computer vision (the technology that allows AI to “see” objects). We wanted to ensure our AI made the best possible assessments.
Solution: We tested different AI structures, used pre-trained models to boost accuracy, and fine-tuned the way the AI processes images.
4. Making AI predictions understandable
Raw AI data isn’t always easy to interpret. It gives results, but those results need to be structured into meaningful insights.
Solution: We applied mathematical analysis to the AI’s output, turning the raw data into clear, useful information.
5. Connecting AI to different platforms
SCOPR.AI needed to work on different platforms, from mobile apps to web applications, without technical hiccups.
Solution: We linked our AI (built in Python and TensorFlow) with Flutter for mobile and Django for backend operations, ensuring smooth communication between systems.
6. Making AI scalable
An AI model needs a solid system behind it to handle requests and deliver predictions in real-time.
Solution: We built an API (a digital bridge between systems) using Django, allowing the AI to work seamlessly with different applications.
7. Keeping AI up to date
AI models become less accurate over time if they aren’t updated with new data.
Solution: We created a system that allows the AI to learn and improve over time by retraining with fresh data.
Conclusion
Building SCOPR.AI was a journey full of challenges, but each obstacle taught us something new. From making AI mobile-friendly to ensuring it stays accurate over time, we learned how to balance speed, efficiency, and scalability. If you’re working on an AI project, these lessons can help you build smarter, faster, and more adaptable solutions.
