Retail / E‑Commerce
Retail / E‑Commerce
Industry Challenges in Retail AI
- Vast and frequently changing product catalogs
- Need for highly accurate product recognition and classification
- Compliance needs for content moderation across images, text, and videos
- Scalability requirements for high-volume marketplace platforms
- Multi-language and multi-format listing content
How rProcess Supports the Retail & E‑Commerce Ecosystem
We deliver precise AI training data and managed content operations that strengthen product classification, visual search, recommendation engines, and automated retail workflows.
Annotation Services for Retail
- Product detection & classification
- SKU‑level tagging and attribute extraction
- Shelf inventory monitoring
- Packaging & label identification
- Visual quality checks and moderation
- Customer review sentiment analysis
- Product description enrichment & structuring
- Chatbot training for retail support workflows
- Compliance and policy‑violation tagging for text content
- Text extraction from packaging, labels & signage
- SKU barcode and tag detection
- Multi-language scene text processing
- Product documentation digitization
Why Choose rProcess?
Experience With High‑Volume Retail & Marketplace Data
We manage massive SKU catalogs, seller uploads, and customer-generated content with speed and precision.
Outputs Tailored for Search, Recommendations & Product Catalogs
Our annotations are optimized for platforms needing structured product data for AI-driven personalization.
Proven Accuracy Across Thousands of SKUs
We maintain consistent labeling and attribute extraction across diverse product types and categories.
Scalable Annotation Pipelines for Continuous Ingestion
Our teams support daily or weekly data flows across global e‑commerce platforms.
Retail/ecommerce Case Studies
Retailers face frequent stock-outs due to poor shelf visibility and delayed replenishment. Inconsistent tracking of out-of-stock products leads to missed sales opportunities, inventory inefficiencies, and customer dissatisfaction. A real-time, scalable solution was required to monitor shelf gaps and ensure accurate restocking decisions across global retail stores.
- Scanned aisle images to detect missing products
- Annotated out-of-stock sections including price labels
- Extracted key details like product name, SKU/ID, and price
- Ensured >96% accuracy through manual visual QC
- Delivered shelf scan reviews within <12-hour SLA for faster replenishment
- 5M+ shelf images reviewed across global retailers.
- Improved shelf fill rate by ~20%, reducing stock loss.
- >96% accuracy achieved in bounding box annotations.
- <12-hour turnaround time for real-time shelf scan reviews.
AI-driven cashier-less retail systems face challenges in accurately identifying products in shopping carts and baskets. Traditional barcode scanning fails when products are stacked, overlapping, or partially hidden, leading to errors in automated billing and checkout. Retailers needed a reliable solution to improve accuracy and efficiency in real-time product identification.
- Marked each product in shopping baskets using bounding boxes.
- Assigned precise labels to each item for structured identification.
- Ensured accurate detection of overlapping or partially hidden products.
- Used advanced tracking models to manage challenging cases of stacked products.
- 10,000+ shopping baskets annotated and processed.
- High-accuracy detection, classification, and tracking across diverse product types.
- Enabled robust cashier-less checkout systems with seamless customer experience.
- Improved reliability of automated billing by addressing barcode limitations.
E-commerce platforms face challenges with unstructured product descriptions that hinder search, filtering, and personalized recommendations. Missing or inconsistent details like Country of Origin or Fabric/Material reduce taxonomy accuracy, product discoverability, and customer satisfaction.
- Parsing descriptions to locate relevant phrases.
- Highlighting accurate references to Country of Origin or Fabric/Material.
- Applying appropriate attribute labels.
- Ensuring quality checks for cases where attributes were missing.
- 5K+ Product Listings Processed
- 10+ Years of Retail Product Annotation Expertise
- >95% Product Labeling Accuracy
- Captured Key Labels: Country of Origin, Fabric/Material
- Improved taxonomy, search relevance, and product discovery, enhancing the overall customer experience.
