Particl

Particl pulls together the signals that normally live in a dozen tabs: what competitors charge, when they discount, which SKUs actually move, and how their assortments overlap with your own. Its AI categorization layer normalizes messy retail catalogs into comparable product types, so a team can benchmark its own products against the market without hand-building spreadsheets. The platform is positioned for the whole commercial loop — knowing what to launch, what to price, and what to promote next.

Day to day the product is organized around a handful of workflows: competitor research down to SKU-level sales and inventory quantities, product research for spotting trends and market leaders, assortment analysis to find overlap and excess, benchmarking and white-space analysis for profitable colors and categories, and promotion monitoring that tracks discounts, restocks and launches alongside social activity. Alerts, filters, historical time ranges and exportable reports keep the data flowing into the tools teams already use.

Particl has also leaned into agentic access. An MCP integration lets Claude, ChatGPT or any compatible AI client query competitor data in natural language and return tables or analyses, and a marketing asset library collects competitors' emails, ads and campaigns for teams that need creative reference alongside the numbers. Historical depth, user seats and export volume scale by plan, with a custom tier for larger organizations wanting API access and data licensing.