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. Competitive pricing across the open web. Product availability from non-cooperating merchants. Alternatives from outside any single catalog. Ground truth, not self-reported merchant claims.

Traditional scrapers broke constantly. Every time a retailer updated their product page layout, someone had to fix the selectors. Teams were spending more time maintaining scrapers than actually using the data. The new wave of AI-powered tools helped with adaptability, but introduced a different problem: running an LLM on every single page doesn't scale, and at high volumes, the inference costs eat into any ROI the data provides.

We kept asking ourselves: what if there was an alternative?

An Alternative Approach

That question became our name. Extralt comes from combining "extraction" and "alternative." We wanted to build something fundamentally different, and we wanted to focus exclusively on ecommerce.

The insight was simple: AI should run once, not on every page. Most vibe scraping tools work by pointing an LLM at each page and asking it to extract data, which is powerful but expensive at scale. We realized that the AI doesn't need to see every page. It just needs to understand the site's structure once, then generate code that can extract data at machine speed.

By focusing on ecommerce, we can optimize for the patterns that matter: product catalogs, pricing data, availability, variants, and the structured listings that define online retail. The result is faster extraction, more consistent data, and schemas that work across any ecommerce site.

How It Works

The flow is straightforward:

Schema: Every extraction uses a comprehensive ecommerce schema automatically: product names, prices, availability, images, variants, identifiers. No configuration needed. The same schema works across any ecommerce site.
Robot generated: Our AI agent analyzes the target website, mapping its structure, discovering how to find relevant pages, and determining the best extraction method (Chrome rendering, HTTP requests, or direct API calls). It then builds a robot, a compiled Rust crawler optimized for that specific site, encoding everything it learned into native code.
Data extracted: The robot runs at machine speed, extracting structured product data without any further AI involvement. Thousands of pages per minute, no per-page inference costs.

When the website changes, we can rebuild the robot. You don't maintain selectors. You maintain intent.

Interested in seeing this in action? Join our waitlist for early access.

What You Can Build

Extralt is designed for teams that need ecommerce data at scale:

Price intelligence: Track competitor pricing across thousands of products. Know when prices change, identify patterns, and respond faster than your competition.
Competitive analysis: Monitor product catalogs, inventory levels, and availability across your market. See what competitors are stocking, what's selling out, and where there are gaps.
Catalog enrichment: Pull structured product data to enrich your own listings. Get better images, more complete descriptions, and standardized attributes.
Brand monitoring: Track where your products appear across resellers and marketplaces. Ensure pricing compliance and identify unauthorized sellers.
Market research: Aggregate listings and pricing data across an entire category or vertical. Understand market dynamics without the data wrangling.

Who We Built This For

Brands who need to monitor their products across resellers. Track pricing compliance, identify unauthorized sellers, understand how your products are positioned in the market.

Retailers who need competitive intelligence at scale. Monitor competitor pricing, track availability, and identify assortment gaps before they become problems.

Agent builders powering the next generation of ecommerce applications. AI shopping assistants need structured, reliable product data to compare options and make recommendations. Extralt provides the discovery layer that sits before checkout.

Analysts who need market data without building infrastructure. Research pricing trends, category dynamics, and competitive positioning without maintaining scrapers.

The 4 E's: From Raw Data to Market Insight

Extraction is just the beginning. We're building Extralt as a complete pipeline for ecommerce intelligence:

Extract — Get raw data from any ecommerce site. Product listings, prices, availability, images. This is what we're launching with.

Enrich — Normalize to English, classify with standardized taxonomy, generate embeddings, match to canonical products. Turn raw extractions into structured intelligence. (Coming soon)

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

Explore — Query the graph. Search across your catalog, find the best prices, discover alternatives, track products across the market. (Future)

The core principle: you pay to build (Extract + Enrich). You explore for free (Extend + Explore).

Get Early Access

We're currently in private beta, working with early users to refine the experience. If you need ecommerce data at scale, whether for competitive intelligence, catalog enrichment, or powering AI-driven shopping experiences, we built Extralt for you.

Join the waitlist and we'll reach out when we're ready.

For the data behind the shift to AI-powered extraction, read Web Scraping in 2026: Why AI-Generated Crawlers Are Winning.