Product Pages at Catalog Scale
A reseller’s catalog grown from 10K to over 1M products, every listing built by agents: a product image, a real title, key features, a description, and an FAQ.
The problem.
A large catalog is mostly empty text fields. A product lands with a manufacturer part number where its title should be, blank specification rows, no description, and no answers to the questions buyers actually ask. Every gap is a page that does not rank and a customer who leaves.
For a reseller the gap grows every day. Supplier feeds bring tens of thousands of new products at a time, and no team writes that many pages by hand.
The system.
Agents read what the product already carries (images, part numbers, scattered specifications) and build the page out: a product image, a full title, key features, a long-form description, and an FAQ covering assembly, capacity, compatibility, and returns. Every page is structured for search, from its title to its FAQ.
Around the pages, the same system categorizes every product, tracks competitor prices and suggests prices, flags opportunity products, and catches anomalies in a feed before they reach the store. It also keeps stock and orders in sync with the Shopify store, and customer-facing chatbots answer the stock and order questions buyers ask.
In operation.
A script runs every day and turns tens of thousands of new products into finished pages in under an hour. It runs on Kubernetes, sized for that throughput.
People stay in the loop. Changes go through human approval, and managers get a daily brief by email reporting what the system did. On the customer side, the chatbots collect feedback, let customers report errors, and open a support ticket when a person needs to step in.
The catalog grew from 10K to over 1M products on this system. It costs about a third of one product engineer’s salary, at thousands of times that engineer’s output. Development is complete, and every part of it runs in production today.
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