Retail and eCommerce

Centaur.AI helps companies improve product discovery, personalization, and trust by delivering accurate data labeling and model evaluation at scale.

Trusted by AI leaders from startups to enterprises

Use cases

Reinventing retail and ecommerce with AI-enabled solutions.

Product tagging & enrichment

Product Image Annotation: Tag and localize products in images using bounding boxes, polygons, and attribute labels to train visual search models.

Product Attribute Enrichment: Extract, standardize, and enrich attributes like size, color, material, and brand across catalog listings.

Product Categorization: Classify products into multi-level or multi-label taxonomies to improve search and navigation.

Text Annotation and Content Tagging: Label product titles, descriptions, and reviews for completeness, sentiment, and keyword extraction.

Visual Search Training Data: Create labeled datasets that link images and identify visual similarities for recommendation engines.

Data Quality Validation: Review and correct automated labels, detect duplicates, and ensure compliance with platform policies.

Custom AI Training Sets: Assemble high-quality labeled data for proprietary computer vision and NLP models tailored to retail.

Catalog QA & trust enforcement

Content Verification: Manually review product titles, descriptions, and images to detect inaccuracies, missing details, or policy violations.

Duplicate Detection: Identify and flag duplicate or near-duplicate listings to maintain a clean and consistent catalog.

Attribute Consistency Checks: Validate that attributes like size, color, and material are complete, standardized, and accurately applied across products.

Compliance Tagging: Label listings that contain prohibited items, counterfeit goods, or restricted content to enforce marketplace policies.

Image Quality Assurance: Inspect product photos for clarity, correct angles, and absence of watermarks or inappropriate elements.

Pricing Validation: Compare listed prices against reference data or business rules to catch errors and prevent fraud.

Review Moderation: Annotate and filter user-generated reviews and questions to detect spam, offensive content, or misinformation.

Review classification & summary evaluation

Sentiment Analysis: Label reviews as positive, negative, or neutral to support reputation management and product insights.

Topic Classification: Tag reviews with themes like shipping, quality, sizing, or customer service for granular analysis.

Spam and Fraud Detection: Identify fake, duplicate, or promotional reviews to maintain trust and compliance.

Summary Validation: Evaluate auto-generated review summaries for accuracy, coverage, and clarity.

Toxicity and Moderation Tagging: Flag reviews containing offensive language, personal attacks, or prohibited content.

Feature Extraction: Highlight specific product attributes mentioned by customers to inform marketing and merchandising.

Helpfulness Scoring: Annotate reviews likely to be rated as helpful by future shoppers based on content quality and relevance.

Why Centaur.AI

We deliver unmatched accuracy, speed, and scalability—helping you ship better AI, faster.

Speed and scale

Access millions of annotations weekly from a network of thousands of experts, taking models from exploratory analysis to deployment in weeks, not months.

Quality by design

Experience how label quality is built into the Centaur.AI system, with frequent performance measurement and pay-for-performance incentives.

Advanced dataset insights

Leverage more reliable statistical information to inform your algorithm training plan.

Privacy and security

Worry less with leading privacy and security capabilities.

Quality focused

Lean on Centaur.AI's expertise as the premier annotation company leveraging collective intelligence to ensure quality.

All data types

From individual types to multimodal solutions, get all of your annotation needs in one single platform.

Accelerate your AI development today.