Ecommerce product scraper
for structured
product data.
Collect public ecommerce pages without writing crawler code. Keep source-backed Captures, then add Enrich and cross-store matching when your workflow needs them.
Scraping projects rarely fail on the first request. They fail later, when the page changes, variants move, price fields do not line up, and nobody trusts the spreadsheet anymore. Extralt is built for that later part.
no-code start
No-code where it helps. Real crawler code underneath.
Start with what you need to know
Give Extralt a public product or catalog URL and choose its country instead of starting with selectors and browser scripts.
Open the work when needed
Technical teams can inspect jobs, captures, enriched records, matched products, APIs, SQL, and exports.
Use AI at build time
AI writes the extractor. Recurring extraction runs as compiled Rust on Extralt's engine without interpreting every page again.
direct answer
Fetching a page is the easy part. Reusing the data is where the work starts.
Pages change
Selectors break. JavaScript moves fields around. Variants hide behind interactions. Category pages drift.
Product data is messy
Every store names fields differently. Prices, offers, sellers, stock, options, reviews, and identifiers need one shape.
Raw data is not the answer
Teams still need records they can audit, export, query, and connect to products they already track.
how it works
What happens after you point Extralt at a source.
01
source evidence
Extract
AI writes the crawler for each source. Recurring jobs run as compiled Rust and keep the URL, timestamp, and source facts with every capture.
02
items and relationships
Enrich
Turn each successful Capture into one Item, then connect equivalent Products and exact Variants to source Listings and Offers before the committed scope becomes ready.
03
beta access
Explore
Inspect current markets, changes, Variants, Offers, and history. Use the available API, SQL, and Capture export paths where their scopes fit.
deliverables
What comes back.
Original evidence
Captured pages
URLs, source facts, raw product fields, raw offers, review summaries, run metadata, and timestamps.
Clean records
Enriched Items
One page-grain Item per Capture with normalized language, taxonomy, attributes, options, identifiers, embedded commerce data, review aggregates, and lineage.
Market view
Cross-store relationships
Products and exact Variants connected to source Listings, Stores, Reviews, and observed Offer history during Enrich.
Your workflow
Dashboard, API, SQL, exports
Use the available views and API, analyze through SQL where its scope fits, or export raw Captures as JSON or Parquet.
comparison
Where Extralt fits.
| Option | Good for | What Extralt adds |
|---|---|---|
| Generic scraper APIs | Fetching pages, rendering JavaScript, proxy handling, and low-level request infrastructure. | Extralt handles collection, then turns the result into ecommerce records, matched listings, and data your team can inspect. |
| No-code scrapers | Quick exports from a handful of sources with little technical setup. | Extralt keeps the no-code start, but underneath it runs generated crawler code, a maintained extraction engine, and a reusable product dataset. |
| Internal Playwright or Scrapy | Teams with engineering time, narrow source lists, and full ownership of retries, parsing, and monitoring. | Extralt takes over scraper maintenance and gives you the downstream ecommerce data model without the usual glue code. |
| Price monitoring dashboards | Fixed workflows where the dashboard is the product and the user does not need direct access to the tables or exports. | Extralt is for teams that want the workflow and the data behind it: captures, records, listings, offers, API, SQL, and exports. |
use cases
Scraping feeds the questions people actually ask.
Competitor price monitoring
Track public prices, stock, sellers, countries, and offer history for the products and sources your team monitors.
Open the workflowProduct data enrichment
Turn extracted pages and imported catalogs into normalized product records with taxonomy, attributes, options, and identifiers.
Open the workflowMarket intelligence
Analyze observed category movement, assortment breadth, brand presence, first-observed products, and seller coverage within your source set.
Open the workflowCross-seller matching
Resolve the same product across stores so listings, variants, offers, reviews, and price history can be compared.
Open the workflowFor implementation details, start with Extract, then follow the pipeline into Enrich. For a broader buying guide, see the ecommerce web scraping tools comparison.
Frequently asked questions
Start with the ecommerce pages you need.
Collect public product data, keep the original evidence, and turn the result into a dataset your team can use again.