Use cases / Ecommerce web scraping
Ecommerce scraping without the upkeep.
Collect the product pages and catalogs your application needs. We handle the crawlers; your team works with structured data.
Crawlertempo.example · FR
Built, run and repaired by us
- Source
- Store API · JSONfound by recording the page
- Proven
- 3 live pages26 of 26 fields on each
- Run · 1 Sep
- 12,316 Captures164 failed pages, no credits
- Repaired · 3 Nov
- crawler v2new product template, proven again
↳ no selectors, proxies or retries on your side
The output
A product page becomes data you can use.
This recorded Nike extraction includes product details, prices, availability and 7 SKUs. The original URL and observation time travel with the record.
Product pagenike.com
$41.97
Nike DNA · Men's Dri-FIT Basketball Shorts
- SKUs
- 7 captured
- Observed
- 30 Mar 2026 · UTC
- Currency
- USD
↳ https://www.nike.com/t/dna-mens-dri-fit-basketball-shorts-hVGm16/HV1878-350
CaptureSelected fields
{
"title": "Nike DNA",
"brand": "Nike",
"product_id": "HV1878-350",
"skus": [
{
"identifiers": {
"gtin": "00198482162061",
"mpn": "HV1878-350"
},
"offers": [
{
"price": {
"amount": "41.97",
"compare_at_price": "60",
"currency": "USD",
"unit": "major"
},
"availability": {
"in_stock": true,
"quantity": "In stock"
},
"condition": "new",
"seller": "Nike",
"seller_type": "1p"
}
]
}
],
"sku_count": 7,
"extracted_at": 1774874512106
}↳ The full record includes variant options and offers.
How it works
From your first URL to recurring data.
AI writes the crawler for each website, and compiled Rust runs it again whenever you need fresh data. Enrich and Explore turn the pages into a dataset you can compare.
01 Extract
Collect product data. Skip the scraper upkeep.
AI writes a crawler for each website and proves it on real pages; then it runs as plain code whenever you choose. Each product page becomes a Capture: what the page said, its URL and when it was seen.
Product pagetempo.example
Homme › Chaussures › Baskets
NIKE Air Max 90 Black
134,99 €
Taille 42
En stock
Livraison offerte dès 50 €
↳ 1 Capture · /products/airmax90-black-42 · 1 Sep 09:00
02 Enrich
Different stores. Comparable products.
AI turns each Capture into an Item: clean fields in English, one price format, a standard category. Items are then matched across stores into products, variants, listings and offers, with their reviews and changes.
Itemtempo.example
- brand
- NIKE → Nike
- size
- Taille 42 → EU 42
- price
- 134,99 € → €134.99
- stock
- En stock → In stock
- category
- Sneakers
↳ Nike Air Max 90 · Black · EU 42 · 3 offers
03 Explore
Your product data. Answers you can inspect.
4 Analyses of the data you collect: price position, price movements, availability changes and assortment overlap. For anything else, ask the agent: it queries your data and shows its work. Each number links back to the pages it came from.
Price position7 Sep
−5.5%
Air Max 90 at north.example against the median of field and tempo
↳ 3 offers · 3 pages
Comparison
Where Extralt fits.
Each approach is the right one for some teams. Here is what each is good for, and what we add.
| 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. |
Coverage
Will it work on your stores?
Yes, once we have proven a crawler on the website. Here is what that covers, what we check first and what we do not do.
Works
- Brand stores, retailers and marketplaces, with each seller’s offer.
- Prices, currency and stock as a shopper in the store’s country sees them, from residential IPs there.
- Protected sites: anti-bot challenges are retried with a new fingerprint and IP, at no extra cost.
- Native routes for Shopify, WooCommerce and Wix; elsewhere, the store’s own API where it has one.
Checked first
- Every new website: its crawler must extract every required field from 3 live product pages before your first run.
- Start covers Shopify, WooCommerce and Wix stores; Scale adds custom crawlers for other stores.
- A redesign: the run stops before bad records reach your data, and the crawler is rebuilt and proven again.
Not a fit
- Pages behind a login or a customer account.
- Data a page does not show: sales, traffic or market share.
- Pages that are not ecommerce products or catalogs.
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.
Product data enrichment
Turn extracted pages and imported catalogs into normalized product records with taxonomy, attributes, options, and identifiers.
Assortment analysis
Analyze observed category movement, assortment breadth, brand presence, first-observed products, and seller coverage within your source set.
Cross-seller matching
Resolve the same product across stores so listings, variants, offers, reviews, and price history can be compared.
FAQ
Questions about ecommerce scraping.
Go deeper
More on ecommerce scraping.
Guide
Ecommerce web scraping: products, prices, SKUs and sellers
Guide
The best ecommerce web scraping tools
Guide
Runtime LLMs vs generated crawlers
Comparison
An Apify alternative for ecommerce scraping
Comparison
A Bright Data alternative for product data
Comparison
A Firecrawl alternative for product extraction
Start with your stores
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.
Start for free · No credit card · Plans from $30/month