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AI utility app
An AI trading-card scanner that identifies, grades, and prices any card from a single photo, in seconds.
and 10M+ cards scanned
99%+
Scan success rate at scale
~20 sec
To identify, grade, and price a card
10M+
Cards scanned by users
The challenge
Trading-card collectors have a slow, manual problem. To know what a card is worth, you identify it, judge its condition, then hunt comparable sales across multiple marketplaces. It takes minutes per card and expertise most collectors do not have.
The goal was to collapse all of that into one photo. Point your camera at a card, and in seconds get it identified, graded for condition, and priced from real market data. Simple to describe, hard to deliver, because it means combining computer vision, AI grading, and live market pricing into a single flow that is fast enough that a user will wait for it and reliable enough that they trust the number.
What we did
We designed and built Cardly end to end: the app, the AI scanning and grading pipeline, the pricing engine, and the analytics and admin tooling behind it.
We treated speed, accuracy, and reliability as the core product, not features bolted on later. Getting an AI system to identify a specific card from a photo, grade its condition consistently, and return a trustworthy live price is difficult at the best of times. Doing it in seconds, for hundreds of thousands of users, means engineering for scale and for the messy realities of real-world market data. We ran continuous testing and experimentation on the pipeline and the monetization flow, and built the internal tooling to measure every scan rather than guess.
The result
Cardly grew to more than 500,000 users and over 10 million cards scanned, holding a scan success rate above 99% at scale.
The number that matters is that reliability held as volume grew. Keeping an AI system fast and accurate for a handful of test images is straightforward. Keeping it above 99% across millions of real scans, on cards in every condition, is the hard part, and it is the difference between shipping an AI app and running one. That is the work most of the market underestimates, and it is what we do.
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