AI-Powered Quality Assessment Platform
Overview
The Challenge
An AI platform was built to address a major gap in the collectibles industry where manual assessment processes were costly, inconsistent, and inaccessible. The goal was to automate this process using computer vision and machine learning, delivering fast, standardized, and affordable quality ratings. Our team developed an AI-based web platform where users upload images of their collectible items. The system processes the image using OpenCV, evaluates features like centering, edges, corners, and surface condition, and predicts a quality grade from 1 to 10 using a custom-trained deep learning model. Along with the predicted grade, users receive visual insights into how each quality factor contributed, offering both transparency and trust.
Our Approach
How We Did It
Analyzed
Defined assessment logic based on industry standards and collected image datasets.
Engineered
Built a custom CNN model with OpenCV preprocessing to evaluate item condition.
Delivered
Launched MVP with Dockerized backend, EC2 deployment, and S3-based image storage.
Impact
The Results
Within weeks, the platform offered users a self-serve tool for quality assessment, democratizing access to a once-exclusive service. The platform is now used by hobbyists and sellers to verify item quality quickly and cost-effectively, reducing dependency on slow manual reviews.
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