About Extralt
Extralt started from a recurring problem: getting reliable product data from the web was harder than it should be.
Traditional scrapers broke constantly. Runtime AI extraction was too slow and expensive for repeated collection. Merchant feeds were useful, but they did not replace independent observations from public product pages.
The alternative was to use AI to understand a source and generate a purpose-built extractor, then run compiled code for repeated collection. That gives Extralt the adaptability of AI at build time without putting a model call in every extraction.
That approach became Extralt. Extraction is the foundation, not the final deliverable.
Beyond extraction
The real opportunity is what comes after extraction: normalizing data to a canonical form, classifying products with standard taxonomies, preserving source evidence, and making the resulting records easier to inspect and reuse.
Today, Extralt manages extraction from supported public product and catalog pages, preserves the source evidence, and enriches the resulting records into one ecommerce-native model.
Customers keep access to the raw and processed data. Enrich adds cross-store relationships, while Explore provides ways to inspect the resulting dataset.
What we believe
Teams should not have to choose between maintaining scrapers and buying a closed analytics product. They should be able to collect the pages they need, inspect the source evidence, and keep the raw and processed records for their own workflows.
Extralt is therefore deliberately ecommerce-only. It is built for technical founders, product engineers, and data teams that need recurring product, price, availability, or assortment observations across stores.
The current pipeline is documented on the products page, including the scope of Extract, Enrich, and Explore.