Scalability Challenges in Retail Image Quality Control
Teezily is a French-based, European print-on-demand (POD) platform founded in 2013 that allows users to create and sell custom apparel without upfront costs. The platform enables creators to launch clothing campaigns, with Teezily managing production, shipping, and customer support.
As marketplace activity increased, Teezily faced growing operational challenges in maintaining consistent image quality across large volumes of user-uploaded product content.
- Manual image review bottlenecks: Quality control workflows relied heavily on manual moderation, slowing product approval and increasing operational workload.
- Inconsistent image quality across listings: Low-quality or non-compliant images negatively impacted product presentation and customer experience across the platform.
- Difficulty scaling moderation workflows: Growing upload volumes made it increasingly difficult for moderation teams to review images efficiently and consistently.
- Limited automation in visual quality assessment: Existing workflows lacked AI/ML and other intelligent systems that can evaluate image quality automatically.
An AI-powered image quality engine was required to automate moderation workflows, improve quality consistency, and support scalable marketplace operations.
Developing an AI-Powered Image Quality Detection Engine
Addressing these challenges required more than traditional rule-based moderation workflows. A platform capable of analyzing visual content intelligently, detecting quality issues automatically, and scaling across large image volumes was essential.
Our team engaged as a strategic product engineering partner to design and implement a computer vision–based quality detection engine tailored to retail e-commerce operations.
1. Building an Image Quality Detection Engine with Computer Vision
Addepto developed a computer vision system capable of automatically analyzing uploaded product images and identifying common quality issues such as blurriness, poor cropping, and image distortion.
Automated image evaluation significantly reduced the need for manual moderation while improving the consistency of visual quality control across the platform.
2. Leveraging VGG-Based Deep Learning Models
The solution was built using the VGG convolutional neural network architecture, a state-of-the-art deep learning model known for strong image recognition performance.
VGG-based models enabled the platform to detect image quality issues with high accuracy while maintaining reliable performance across diverse user-uploaded content.
3. Training the Model on Real Marketplace Data
To improve detection accuracy, the AI model was trained using actual images uploaded to the Teezily platform. This allowed the system to learn marketplace-specific quality patterns, common user mistakes, and operational quality standards directly from real-world data.
Domain-specific model training improved the platform’s ability to identify low-quality content within realistic retail and e-commerce scenarios.
4. Incorporating Rule-Based Quality Validation
In addition to AI-driven image analysis, the platform included predefined quality control rules designed to enforce specific marketplace standards and detect unacceptable content early in the validation process.
A hybrid validation approach combining AI models with rule-based checks improved moderation consistency while reducing false positives and operational risk.
5. Delivering an Integration-Ready Backend Solution
The final platform was delivered as a ready-to-integrate software module that connects directly with Teezily’s e-commerce backend systems for real-time image validation workflows.
Seamless backend integration enabled automated quality checks during product uploads while supporting scalable marketplace operations and future AI-driven moderation capabilities.
Teezily had no in-house AI team, but the company was deeply aware of AI potential, so the collaboration was very smooth. We decided to harness the sota computer vision algorithms and build an AI engine able to be incorporated into the Teezily eCommerce system.
Wojciech Drężek
Data Scientist – Addepto
Leveraging Computer Vision AI to Accelerate Market Growth
The partnership between Addepto and Teezily transformed manual image moderation workflows into an intelligent and scalable AI-powered quality control system. Marketplace teams can now review and validate product images more efficiently while maintaining higher visual quality standards across the platform.
By preventing low-quality images from reaching customers, the platform also enhanced product presentation and overall user experience across the e-commerce environment.
Before
- Manual image moderation and quality review workflows
- Inconsistent product image quality across listings
- Slow approval processes for uploaded content
- Limited scalability for high-volume moderation operations
After
- AI-powered automated image quality detection
- Standardized and consistent visual quality control
- Faster image validation and approval workflows
- Scalable moderation processes powered by computer vision
The automated workflows also reduce administrative overhead and allow recruiters to focus more on strategic talent engagement and relationship-building activities.
As part of KMS Technology, Addepto continues to deliver enterprise-grade AI consulting services that help organizations optimize complex operational workflows through practical and business-focused AI innovation.
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