AI-Driven eCommerce Platform
A scalable commerce platform with AI-assisted catalog operations that helps teams classify, enrich, validate, and manage product data more efficiently as catalog volume grows.
AI-assisted
Product enrichment and classification
Quality-controlled
Catalog issues surfaced before publishing
Scalable
Workflows designed for growing product volume
16 weeks
From discovery to production-ready platform
The challenge
Product catalog growth was creating a data-quality problem
The client’s product catalog was growing across multiple categories and data sources.
Product information arrived with inconsistent titles, categories, attributes, descriptions, images, and formatting. Merchandising teams spent significant time manually cleaning and standardizing product records before they could be published.
Incomplete attributes affected product discovery, duplicate records created operational confusion, and inconsistent categorization made it harder to maintain a reliable storefront experience.
The client needed a more scalable approach to catalog operations without giving up control over how products appeared to customers.
What we built
An AI-assisted catalog operations platform with human review built in
We built a commerce platform that uses AI to assist with repetitive catalog-management tasks including classification, attribute extraction, description generation, product mapping, image tagging, and duplicate detection. Incoming product data is analyzed and normalized before it reaches the storefront. AI-generated suggestions can be reviewed by merchandising users before publication, allowing the business to automate repetitive work without giving up editorial control. Data-quality checks also identify incomplete, inconsistent, or potentially duplicate records so teams can resolve issues before they affect customers.
Product Classification
Suggests appropriate categories and taxonomy mappings from product information.
Catalog Enrichment
Extracts attributes and helps generate structured product titles, descriptions, and supporting metadata.
Data Quality & Duplicate Detection
Flags missing information, inconsistent attributes, and potential duplicate product records.
Merchandising Review
Provides a workspace where users can review, approve, or edit AI-generated recommendations before publishing.
Technologies used
The results
More scalable catalog operations with stronger product-data consistency
- Product records can be classified and enriched more consistently.
- Attributes can be extracted from supplier or source data into structured fields.
- Product descriptions can be generated using catalog context and configured standards.
- Potential duplicate records can be surfaced for review.
- Missing or inconsistent data is identified before products are published.
- Merchandising teams spend less time on repetitive catalog preparation.
- Human review remains available for AI-assisted changes.
- The platform provides a stronger foundation for expanding catalog volume and additional commerce workflows.
Have a similar workflow to fix?
If manual review or a first-in-first-out process is slowing your team down, we'd be glad to talk through whether a similar approach could work for you.