Ecommerce scraping without the upkeep.
Collect the product pages and catalogs your application needs. Extralt handles the crawlers; your team works with structured data.
A product page becomes data you can use.
This recorded Nike extraction includes product details, prices, availability, and seven SKUs. The original URL and observation time travel with the record.
01 / Product page
Nike DNA
Men's Dri-FIT Basketball Shorts
$41.97 USD
- SKUs captured
- 7
- Observed
- 30 Mar 2026 · UTC
02 / Structured capture
{
"title": "Nike DNA",
"brand": "Nike",
"product_id": "HV1878-350",
"min_price": 41.97,
"currency": "USD",
"available": true,
"sku_count": 7,
"extracted_at": 1774874512106
}Selected fields from the recorded capture. The full record includes variant options and offers.
Inspect the full extraction ↗how it works
From your first URL to recurring data.
01
collect
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
normalize & match
Enrich
Normalize the product details, then connect equivalent products and exact variants across stores. Matching is included with enrichment.
03
compare
Explore
Compare prices, follow availability, and inspect assortment overlap. Ask the dashboard agent a question and check the records behind its answer.
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
Build on the data you collect.
Competitor price monitoring
Track public prices, stock, sellers, countries, and offer history for the products and sources your team monitors.
Explore this use case ↗Product data enrichment
Turn extracted pages and imported catalogs into normalized product records with taxonomy, attributes, options, and identifiers.
Explore this use case ↗Assortment analysis
Analyze observed category movement, assortment breadth, brand presence, first-observed products, and seller coverage within your source set.
Explore this use case ↗Cross-seller matching
Resolve the same product across stores so listings, variants, offers, reviews, and price history can be compared.
Explore this use case ↗For 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 URL and observation time, and turn the result into a dataset your team can use again.
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