Lasso: AI agents for ecommerce data | PIM built for the AI era

Use Case 1: Supplier Data Normalization

Problem: Retailers often receive product data in fragmented formats like inconsistent spreadsheets, PDFs, or unstructured feeds from various vendors.
Solution: Lasso's AI agents automatically recognize patterns and map columns from raw sources into a structured database, extracting attributes even from messy text.
Example: A department store importing thousands of SKUs from five different footwear brands can standardize all sizing, material, and color attributes into a single format instantly.

Use Case 2: Automated SEO Catalog Enrichment

Problem: Manually writing unique, search-optimized descriptions and filling in missing technical attributes for large catalogs is time-consuming.
Solution: The platform crawls the web and supplier sites to find missing data and generates SEO-focused product descriptions that align with brand guidelines.
Example: An electronics retailer uses Lasso to automatically generate 500 unique product descriptions and populate technical specs for a new category launch.

Use Case 3: Competitive Market Monitoring

Problem: Tracking competitor pricing, stock levels, and new product launches across multiple websites is difficult to do manually at scale.
Solution: The tool deploys agents to monitor specific competitor sites, providing real-time data on price changes and inventory status via dashboard or API.
Example: A beauty brand monitors three major rival sites to receive alerts whenever a competitor lowers the price of a core skincare product.