Introducing Extralt: The Market Intelligence Layer for Ecommerce

We've been building Extralt for the past year. Today, we're showing you what we've made: the market intelligence layer for ecommerce.

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 broke constantly. A retailer updates their product page layout, someone has to fix the selectors. 10-15% of crawlers need weekly fixes, and engineering teams spend 20-30% of their time just keeping existing scrapers running.

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, variants. One schema, any site.

How it works

Every extraction uses the same ecommerce schema: product names, prices, availability, images, variants, 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 automatically 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. Know when prices change and respond before your competition does. Whether you're enforcing MAP pricing policies or just keeping an eye on the market, 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.

Brand monitoring. Track where your products appear across resellers and marketplaces. Find unauthorized sellers. Catch pricing violations.

Market research. Aggregate pricing data across an entire category. Understand what's happening without building a data pipeline to get there.

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 compliance, unauthorized sellers, 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, generate product variants. Each capture becomes structured, queryable product intelligence. (Coming soon)

Extend. Find alternate listings (same product, different sellers), alternative products (different products, same need), and complements (frequently bought together). (Future)

Explore. Query the graph. Search across sellers, find the best prices, track products across the market. (Future)

You pay to build (Extract + Enrich). You explore for free (Extend + Explore).

Get early access

We're in private beta. If you need ecommerce data at scale, we built this for you.

Sign up to get started.

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.