- The business received large volumes of handbag product images from multiple sellers, each varying in quality, angle, and style.
- Manual review of each image was slow and created bottlenecks in the product listing process.
- Pricing was inconsistent across similar product categories, leading to lost revenue and buyer confusion.
- The team spent too many hours on routine tasks that could be automated.
Cynoteck built an end-to-end image-classification and pricing-intelligence system powered by machine learning.
Image Quality Assessment: We built a model that automatically checks each incoming image — correcting orientation, assessing usability, and filtering out poor-quality photos before they reach the team.
Brand and Style Recognition: A second classification model identifies the brand, style, and category of each product directly from the image, removing the need for manual tagging.
AI-Powered Price Prediction: Using historical resale data, the system generates a recommended price for each product. This means pricing decisions are consistent, data-driven, and no longer dependent on individual judgment.
Seamless Workflow Integration: The entire pipeline runs automatically in the background, surfacing only flagged items for human review.
Faster Operations
Manual image review and pricing effort was reduced by 70–80%, freeing the team to focus on higher-value work.
Consistent Pricing
Every product is now priced based on historical resale trends, reducing guesswork and improving buyer trust.
Faster Time to Market
Products move from submission to live listing significantly faster, improving seller satisfaction and platform competitiveness.
