Introduction
Beyond ecommerce scraping. Extract, enrich, and compare product data across stores.
Give Extralt a public product or catalog URL and a country. Extract uses source-specific compiled extraction logic to create structured, source-backed Captures. Enrich turns each successful Capture into one normalized Item, then connects Products, exact Variants, store Listings, observed Offers, Reviews, and Stores before the Enrichment becomes ready. Explore provides current-market, change, Variant, Offer, and history views over those records.
Who is Extralt for?
Extralt is for technical teams that need reusable ecommerce product data from public sources without owning source-specific crawler code:
- Price intelligence -- monitor competitor pricing across the open web
- Catalog enrichment -- fill gaps in your product data with external sources
- Assortment analysis -- compare observed catalogs and exact matched Variants
- Product research -- reuse source evidence, taxonomy, identifiers, and history
What can you do with it?
Extralt's pipeline has three stages. Explore is in beta.
| Stage | What it does | Status |
|---|---|---|
| Extract | Run managed, source-specific extraction and produce Captures | Available |
| Enrich | Produce one normalized Item per successful Capture and publish its analytical projection | Available |
| Explore | Inspect overview, current markets, changes, Variants, Offers, and history | Beta |
The pipeline is useful incrementally. Captures can be inspected and exported without Enrich. Items can be queried directly, while an Enrichment remains active until its committed scope is ready for Explore.
How it works
Extralt uses an LLM-based harness to generate source-specific extraction logic, then compiles and validates that logic before it is used for repeated runs. Extraction itself runs as compiled code rather than sending every product page to an LLM.
You provide a URL and a country. Extralt either reuses a compatible Robot or creates a Robot and starts its internal build. Once it is completed, create a Run manually, create a recurring Schedule, or ask the dashboard agent to prepare an approval plan. The agent does not build Robots and never executes a state-changing plan without dashboard approval.