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Product capturedRaw page→38 fields
E-commerce data extraction
★★★★★
4.8 / 5
★★★★★
4.8 / 5
Custom e-commerce data, fully managed.
Track products, prices, promotions, availability, sellers, search position, and reviews across marketplaces and retailer sites. Forage AI handles source discovery, product matching, extraction, normalization, QA, refreshes, and delivery.
60M+
URLs extracted yearly
100M+
Listings monitored frequently
99.7%
Data accuracy
Real-time
Delivery
Sample schema
Every product, every field, captured for you.
Choose the fields, formats, and cadence your team needs. Each product can be delivered as a structured record with its source and full history.
Product & pricing
- Titles, brands, and categories
- SKU, GTIN, and MPN codes
- Current and list price
- Promotions and discounts
- Regional prices and currency
- Full price history
Availability & offers
- Stock status and inventory
- Seller and marketplace offers
- Buy box ownership
- Shipping cost and speed
- Fulfillment type
- Regional availability
Content & media
- Descriptions and spec tables
- Product images and video
- Variant options and sizes
- Category and breadcrumb paths
- Rich and A+ content
- Cross-sell placements
Reviews & ratings
- Star ratings and counts
- Full review text
- Reviewer metadata
- Q&A sections
- Sentiment classification
- Rating trends over time
Need more fields?
We'll provide custom-built datasets.
Built for e-commerce
Storefronts shift daily, your data keeps up.
Our extraction learns each storefront before it delivers, adapts when templates change, and ships product records your team can use as-is.
Mapped
The catalogs others can't cover.
500M+websites crawled, mapped, monitored
Marketplaces
DTC storefronts
Category pages
Product pages
Review sections
+ your list
Field-level
Your fields, filled and typed.
Your schema
sku
price
stock_status
rating
Forage returns
pricedecimal
in_stockboolean
ratingfloat
captured_atdatetime
Auto-remapped
Any layout. One schema.
{}✓
One stable schema
Filtered
Only records that pass QA.
⧉
Duplicate listings collapsed to one product
8,412
◷
Variant SKUs matched to their parent
3,190
↻
Suspect records held for human review
214
Delivered on your schema
Your cadence
Fresh on your cadence.
One-off exports
24% fresh
Managed refresh
99% fresh
Continuous
Hourly
Daily
Weekly
+ your window
Confidence-scored
Matched across marketplaces.
0.96
Why Forage AI
The only e-commerce data partner you'll need.
Any data point on any storefront: price, stock, content, sellers, or reviews.
Adaptive extraction that adjusts itself when a storefront gets redesigned.
Fresh pricing captured on your cadence, from continuous to weekly.
QA'd records checked by automated rules and human review before delivery.
Compliant extraction aligned with site terms and data protection frameworks.
Enterprise scale across millions of SKUs and thousands of storefronts.
Use cases
Extract what actually matters.
Product data drives more decisions than most teams realize. Here's what our clients actually pull.
Price intelligence & repricing
Feed repricing engines and margin models with competitor prices, promotions, and buy-box moves captured on schedule.
Stock & availability
Track in-stock rates, sellouts, and delivery promises across competitors, marketplaces, and your own resellers.
Assortment & catalog gaps
Compare rival catalogs against your own to spot missing brands, ranges, and price points worth stocking.
Reviews & brand protection
Mine ratings and review text for product feedback, and watch reseller prices for MAP violations.
Resources
All resources →
Latest and hottest in e-commerce data.
Researching your way through data extraction? Start here.
Coverage can span public marketplaces, retailer websites, DTC storefronts, category-specific sites, and other approved sources.
Each program is scoped by source, geography, category, seller, page type, and required fields. A source review confirms the available coverage and identifies any source-specific limitations before rollout.
Yes. The pipeline can use product identifiers and descriptive attributes such as SKU, GTIN, UPC, MPN, brand, model, size, pack count, color, and variant details to connect competitor listings to the correct catalog record.
Match rules, confidence thresholds, and exception handling are tested against a representative sample before the program scales.
Refresh cadence is set by field and source rather than applying one schedule to the entire dataset.
High-change fields such as price, promotion, and availability can be monitored more frequently, while catalog attributes and reviews can follow a different schedule. The final cadence is based on source volatility, coverage volume, and the workflow using the data.
The first stage uses representative sources and products to test coverage, product-match rate, field completion, duplicate rate, freshness, and extraction accuracy.
Production deliveries then pass field-level validation, anomaly checks, deduplication, and expert review for exceptions. Quality thresholds and acceptance criteria are agreed before the full rollout.
The client defines the target sources, required fields, taxonomy, business rules, refresh cadence, and delivery requirements.
Forage AI runs source discovery, collection, product matching, normalization, QA, monitoring, source maintenance, and delivery. The resulting data is sent in the agreed format and cadence into the client's workflow.
Start foraging
The first call is to understand your project.
Tell us about your business and project requirements.
- Data audit on your actual target sites
- A tailored plan scoped to your use case
- Sample product dataset before you commit
Get in touch