A diamond retailer whose matching process depended on expert gemologists comparing stones by eye — accurate on a good day, inconsistent across experts, and unable to keep pace with data volume.
“We needed software that could see what our experts see — and do it consistently, every time.”
- 01
Subjectivity: traditional matching relied on individual judgment, producing inconsistent results between gemologists.
- 02
Volume: manual processing of diamond features couldn't scale.
- 03
And there was no automated model to predict the degree of resemblance between stones at all.
Classification & clustering
A similarity engine combining several techniques where each earned its place: a Random Forest classifier for attribute-based classification, K-Means clustering to group similar stones
Visual & color analysis
GrabCut feature extraction for precise visual analysis, and histogram intersection with HSV/LAB color analysis for color similarity.
Instant similarity scores
The output: a similarity score per comparison, generated instantly.
AI Technology Stack
Accuracy
Consistent across every evaluation, no off days
Faster evaluations
Freeing expert time for judgments that need a human
Consistent results
Instant AI-driven comparisons transformed CX
Let's build AI that sees what your experts see — consistently, at scale.
No obligation. Just a conversation about what's possible with AI.