Business Challenge
A leading global home furnishing e-retailer faced challenges in building accurate AI/ML models for image understanding. Unstructured, inconsistently labeled image data limited their ability to power personalized recommendations, visual search, and rich product experiences. As a result, product discovery, engagement, and conversion rates were below potential.
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The Solution
Iksula partnered to deliver large-scale data labeling and annotation for AI/ML training. Our team meticulously tagged product images with attributes like category, color, texture, material, and style. This high-quality labeled dataset was used to train advanced image extraction and recognition models integrated into their personalization engine.
Business Impact
Improved Personalization: Highly accurate product recommendations based on visual attributes and preferences.
Enhanced Discovery: More relevant search results with automatic visual tagging and filtering.
Better User Experience: Dynamic product galleries tailored to shopper behavior and visual intent
AI Model Accuracy: Over 90% precision in image classification, reducing manual intervention.
Technology Stack
AI & ML: Custom vision models, PyTorch, TensorFlow
Data Labeling: Human-in-the-loop annotation platforms, QA automation
Integration: Direct pipeline to personalization and search system
Workflow Tools: Custom labeling dashboards, cloud-based model training pipelines
