Retail & E-commerce AI Data
High-quality training data for product classification, visual search, recommendation engines, and in-store analytics — helping retailers deliver smarter shopping experiences.
Build AI That Understands Commerce
From product catalog enrichment to checkout-free store technology, retail AI requires vast amounts of precisely labeled data across images, text, and behavioral signals. Centric Labs delivers production-scale annotation for product taxonomies, visual search, shelf analytics, and customer intent classification — enabling the personalized, intelligent shopping experiences consumers demand.
- Product taxonomy labeling across 10,000+ SKU categories
- Visual search annotation with attribute tagging
- Sentiment and review classification at scale
- In-store computer vision for shelf and shopper analytics
- Multilingual product content across global markets
Retail AI Annotation Services
From catalog to checkout — labeled data for every retail AI application.
Product Classification
Multi-level taxonomy labeling for product catalogs with attributes like color, size, material, brand, and style. We maintain consistency across millions of SKUs and handle edge cases like bundles, variants, and seasonal items.
Visual Search
Fine-grained image annotation for visual similarity, style matching, and "shop the look" features. We label product boundaries, visual attributes, and contextual tags that power image-based product discovery.
Review & Sentiment
Aspect-level sentiment analysis, helpfulness scoring, and spam detection across product reviews. We label specific product aspects (quality, fit, value) with nuanced sentiment to power review summarization and recommendation engines.
Shelf Analytics
Planogram compliance, out-of-stock detection, and price tag recognition from in-store camera feeds. We annotate product facings, shelf positions, and competitor presence for retail execution monitoring.
Checkout-Free Stores
Person tracking, item pick-up/put-back detection, and cart association for autonomous checkout systems. We label video sequences frame-by-frame with precise temporal boundaries for action recognition models.
Recommendation Data
Relevance scoring, preference labeling, and collaborative filtering ground truth for recommendation engines. We create human-judged relevance datasets that complement behavioral signals for hybrid recommendation systems.
Elevate Your Retail AI With Better Data
Whether you're building visual search, product AI, or in-store analytics — start with a free pilot to experience our quality and speed.