Particl

Particl is a price comparison tool. Particl gives ecommerce teams SKU-level competitor data across 20,000+ retailers, covering sales, pricing, promotions and assortment. AI categorization plus a Claude and ChatGPT connector let teams query the market in plain language.

Particl is a market intelligence platform for ecommerce teams that want to see what competitors are actually selling, pricing and promoting. It collects product revenue, pricing, promotion and assortment data from more than 20,000 retailers and 10,000 plus monitored competitors, then uses AI categorization to organize that firehose into something a merchandising, growth or product team can act on. The promise is narrow and practical: know what to launch, how to price it, and when to promote. The product is organized around five workstreams. Competitor research tracks best sellers, pricing strategy and inventory quantities down to the SKU. Product research surfaces trending products and market leaders in real time. Assortment analysis shows where brands are expanding or cutting back, and where your catalog overlaps with theirs. Benchmarking and white space analysis highlights the colors and categories that appear most profitable. A promotions view monitors competitor social channels and connects launches, restocks and discounts to sales movement. Prebuilt company pages for brands such as Lululemon, SKIMS and Rad Power Bikes show how the data reads in practice. The most distinctive feature is the Claude and ChatGPT connection. Through an MCP-enabled client, teams can ask plain language questions such as which top is Lululemon's best seller or which cosmetics brands are growing fastest, and get an answer with a cited source rather than a dashboard to dig through. That puts Particl closer to a data layer for AI assistants than to a classic reporting suite, though the standard web app remains the main workspace. It is built for retailers and direct-to-consumer brands, mainly in apparel, beauty, consumer goods, home, outdoor and jewelry, plus the agencies and analysts who support them. Buyers comparing it with general ecommerce analytics tools should note that Particl looks outward at the market rather than inward at their own store metrics, so most teams will run it alongside their existing analytics stack rather than instead of it.