Findmine

Findmine is a paid, custom quote-only AI Ecommerce platform best evaluated by mid-market and enterprise fashion retailers that need automated outfit generation, complete-the-look merchandising, and styling-led cross-selling; it is likely worth shortlisting when styling automation is a revenue priority, but not when transparent self-serve pricing or broad ecommerce personalization is required.

Findmine focuses on a specific retail problem: turning apparel, footwear, accessories, and adjacent lifestyle catalogs into coordinated outfit recommendations that can appear across ecommerce experiences. The platform is positioned for merchandising, digital commerce, and visual styling teams that want to scale product pairing beyond manual stylist workflows. In the reviewed record, Findmine is categorized as AI Ecommerce and Fashion Tech, founded in 2014, and best suited to retailers with sizable catalogs, frequent seasonal assortments, and enough traffic or order volume to justify a vendor-led implementation.

Pricing should be treated as enterprise sales-led. The reviewed sources did not confirm a public starting price, per-seat price, usage rate, package tier, free tier, or free trial. The only supportable pricing summary is custom quote-only, with likely variables including catalog size, traffic, ecommerce touchpoints, implementation scope, integrations, support expectations, and contract terms. That makes Findmine harder to compare early against self-serve recommendation tools, but it is normal for specialized retail AI deployments where catalog ingestion, merchandising rules, and site integration affect the final scope.

Five verifiable facts from the reviewed record are central to the evaluation: Findmine's public website is listed as https://www.findmine.com; the sales/demo path is listed as https://www.findmine.com/contact-us/; the company founding year is recorded as 2014; public pricing was checked on 2026-05-26 and no fixed price was confirmed; and vendor-published case-study material in the record references TSC, Cubavera, John Varvatos, and Perry Ellis, including a TSC reference to more than 24 million generated outfits. Those customer and volume claims should still be validated directly with Findmine because the visible structured record does not include permanent case-study URLs for every reference.

The strongest fit is a retailer that wants complete-the-look recommendations to improve attach rate, average order value, conversion, or styling content coverage, and is prepared to run a measured pilot. The weaker fit is a small ecommerce team that needs instant setup, public pricing, broad search infrastructure, or fully documented developer APIs. Security and procurement teams should request current documentation for SSO, RBAC, audit logging, encryption, data retention, privacy, DPA terms, and compliance attestations because those controls were not publicly confirmed in the reviewed sources.