Introducing Extralt: The Market Intelligence Layer for Ecommerce
What Extralt does for ecommerce teams: extract product pages, enrich records, match products across sellers, and turn open-web data into market intelligence.
Extralt is an ecommerce product data platform. It extracts product pages from the open web, enriches them into structured product records, matches the same products across sellers, and makes the resulting dataset usable for market intelligence, price monitoring, below-MAP evidence, catalog enrichment, and AI product discovery.
The short version: raw ecommerce pages in, reusable product intelligence out.
For tactical guides, start with ecommerce web scraping, competitor price monitoring, product data enrichment, and ecommerce market intelligence tools.
The Problem
Ecommerce runs on data that's hard to get reliably. What are competitors actually charging? What's in stock at retailers who don't share inventory feeds? What alternatives exist outside any single catalog? The answers are on the open web, but getting them is the problem.
Traditional scrapers break constantly. A retailer updates their product page layout, someone has to fix the selectors. Teams end up maintaining brittle extraction code instead of using the product data.
Then came the AI-powered tools. They adapted to layout changes, which was genuinely useful. But they run an LLM on every single page. At 100 pages, that's fine. At a million pages, the inference costs eat whatever ROI the data was supposed to provide.
An Alternative Approach
That frustration became our name. Extralt comes from combining "extraction" and "alternative." We wanted to build a third way, and we wanted to focus exclusively on ecommerce.
The insight: AI should run once, not on every page. Most vibe scraping tools point an LLM at each page and ask it to extract data. That's powerful but expensive at scale. The AI doesn't need to see every page. It just needs to understand the site's structure once, then generate code that runs without it.
Focusing on ecommerce means we can optimize for a known shape of data: product catalogs, pricing, availability, SKUs, and offers. One schema, any site.
How it works
Every extraction uses the same ecommerce schema: product names, prices, availability, images, SKUs, offers, identifiers. No configuration. You don't pick fields or write mappings.
Extralt maintains a growing library of production crawlers for popular ecommerce sites. For sites already covered, you get instant access. For new sites, our AI analyzes the target website, figures out its structure, and generates a robot: a compiled Rust crawler with everything it learned baked into native code. New crawlers are ready in minutes.
The robot runs without AI. No LLM in the loop. Entire catalogs in minutes, not hours.
Extralt monitors extraction quality across the library and rebuilds crawlers when sites change. You don't maintain selectors. You maintain intent.
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What you can build
Price intelligence. Track competitor pricing across thousands of products. Whether you're running MAP checks or building pricing intelligence software workflows, it starts with reliable data.
Competitive analysis. Monitor what competitors are stocking, what's selling out, and where the gaps are. Product catalogs, inventory levels, availability across your market.
Catalog enrichment. Pull structured product data to fill in your own listings. Better images, more complete descriptions, standardized attributes, and category paths. See the product data enrichment guide.
Brand monitoring. Track where your products appear across the reseller and marketplace pages you monitor. Preserve seller, price, URL, and timestamp evidence for your review workflows.
Market research. Aggregate pricing, assortment, availability, and seller data across an entire category. See the market intelligence tools guide for how this category is evolving.
Who we built this for
Brands who sell through resellers and need to know what's happening to their products out in the wild. Pricing evidence, seller context, and how you're positioned relative to the competition.
Retailers who need competitive intelligence at scale. What are competitors charging, what's in stock, where are the assortment gaps.
Agent builders working on AI shopping assistants. Your agent needs structured product data to compare options and recommend products. Extralt is the discovery layer that sits before checkout.
Analysts who want market data without building infrastructure. Pricing trends, category dynamics, competitive positioning. The data, not the pipeline.
The 4 E's
Extraction is step one. We're building a full pipeline:
Extract. Raw data from any ecommerce site. Product listings, prices, availability, images. This is live now.
Enrich. Normalize to English, classify with standardized taxonomy, extract attributes and market signals, and create Listings and Offers. Each capture becomes structured, queryable product intelligence.
Extend. Find alternate listings (same product, different sellers), alternative products (different products, same need), and complements (frequently bought together).
Explore. Query the graph. Search across sellers, find prices, track products across the market, and expose product intelligence to analysts and agents.
You pay to build: Extract costs 2 credits per successful product-page Capture, and Enrich costs 1 credit per Capture processed. You explore for free: Extend and dataset reads in Explore are free for your own dataset.
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If you need ecommerce data at scale, we built this for you.
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Frequently asked questions
How is Extralt different from traditional web scrapers?
Traditional scrapers use fixed CSS selectors that break when a website updates its layout. Extralt maintains a growing library of production crawlers compiled to Rust. Popular sites are already covered. For new sites, AI generates a crawler in minutes. Extraction quality is monitored automatically, and crawlers are rebuilt when sites change.
How is Extralt different from AI-powered scraping tools?
Most AI scraping tools run an LLM on every page. That works at small scale but gets slow and expensive fast. Extralt runs AI once at build time, then runs compiled code at extraction time. No per-page inference costs.
What data does Extralt extract?
Product names, prices, availability, images, SKUs, offers, sellers, identifiers, source URLs, and timestamps. The same schema applies across ecommerce sites, so downstream systems do not need a new parser for every source.
How fast is Extralt?
The crawlers are compiled Rust binaries. Full product catalogs in minutes.
For the data behind the shift to AI-powered extraction, read AI Web Scraping in 2026.