Most ecommerce data is locked inside walled gardens or filtered through merchant feeds. Sellers report what they want you to see. Extralt gets what's actually there.

We extract structured product data from any ecommerce site, normalize it to a universal schema, and match the same product across sellers. Four stages: Extract crawls sites and produces consistent structured data. Enrich translates to English, classifies with the Shopify taxonomy, pulls out category-specific attributes, and matches products across sellers. Extend finds the same product on different sites, surfaces alternatives, and links complements. Explore lets you search, compare prices, and run analytics across everything. You pay for Extract and Enrich. Extend and Explore are free.

We built the extraction engine because scraping ecommerce is a maintenance nightmare. Traditional scrapers break when sites change layout. AI scrapers adapt but cost too much to run on every page. So we use AI once to generate each crawler, compile it to Rust, and run native code from there. Fast, nothing to maintain.

Today, teams use it for competitor pricing, MAP compliance, catalog benchmarking, market research, and building data products on our API. Over time, AI agents will need this data too. Checkout protocols like OpenAI's ACP and Google's UCP handle how agents pay for things, but both only see products from merchants who submit feeds. The rest of the web is invisible to them. Extralt covers that. Independent product discovery from the open web, not merchant self-reporting.