E-commerce data from 100k stores that is refreshed daily
Hi HN! I'm building Agora, an AI search engine for e-commerce that returns results in under 300ms. We've indexed 30M products from 100k stores and made them easy to purchase using AI agents. After launching here on HN, a large enterprise reached out to pay for access to the raw data. We serviced the contract manually to learn the exact workflow and then decided to productize the "Data Connector" to help us scale to more customers. The Data Connector enables developers to select any of our 100k stores in the index, view sample data, format the output, and export the up-to-date data. Data can…
In plain words
Agora provides developers access to e-commerce data from 100,000 stores covering 30 million products, refreshed daily. The Data Connector tool lets developers select stores, preview data, customize output format, and export results as CSV or JSON. The platform indexes product information, stock levels, and reviews from Shopify, WooCommerce, Squarespace, Wix, and custom stores, handling the technical challenge of recrawling the entire dataset every 24 hours. It serves businesses needing current e-commerce data at scale.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN! I'm building Agora, an AI search engine for e-commerce that returns results in under 300ms. We've indexed 30M products from 100k stores and made them easy to purchase using AI agents. After launching here on HN, a large enterprise reached out to pay for access to the raw data. We serviced the contract manually to learn the exact workflow and then decided to productize the "Data Connector" to help us scale to more customers. The Data Connector enables developers to select any of our 100k stores in the index, view sample data, format the output, and export the up-to-date data. Data can be exported as CSV or JSON. We've built crawlers for Shopify, WooCommerce, Squarespace, Wix, and custom built stores to index the store information, product data, stock, reviews, and more. The primary technical challenge is to recrawl the entire dataset every 24 hours. We do this with a series of servers that "recrawl" different store-types with rotating local proxies and then add changes to a queue to be updated in our search index. Our primary database is Mongo and our search runs on self-hosted Meilisearch on high RAM servers. My vision is to index the world's e-commerce data. I believe this will create market efficiencies for customers, developers, and merchants. I'd love your feedback!
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