AI Product Intelligence: The New Backbone of Online Discovery
AI product intelligence platforms are software systems that use artificial intelligence to analyse product data and images, automatically generate structured descriptions, metadata and tags, and optimise these elements so products are easier to discover, understand and recommend across e‑commerce sites, traditional search engines and AI-powered search engines. The key takeaway is that AI product discovery is shifting from being a marketing add-on to a core infrastructure need: if your product content is not optimised for algorithms, your brand is invisible. Startups like Sowilo and PageMind are betting that retailers will no longer treat product descriptions as copywriting, but as machine-readable signals that must be engineered. Their recent funding rounds show that investors agree this is where the next wave of e-commerce product intelligence will be built.
Sowilo’s Catecut Turns Fashion Images into Machine-Readable Products
Sowilo’s Catecut product intelligence platform takes a blunt stance on fashion e-commerce: if an item’s attributes are locked inside an image, it barely exists to algorithms. Catecut uses artificial intelligence to identify fashion products and their design attributes from retailer product images, then automatically generates product titles, descriptions, metadata and tags so retailers can populate and optimise online product pages. This is AI product content optimization aimed squarely at closing the gap between human-friendly visuals and machine-readable data. The global Shopify app launch is strategic, not cosmetic; it positions Catecut inside the default workflow for thousands of fashion merchants, enabling multilingual, brand-specific product content, image alt text and metadata tuned for search and generative AI discovery. In effect, Catecut is trying to become the invisible layer that translates every dress, shoe or necklace into a structured object AI systems can rank and recommend.
PageMind Targets the AI Search Paradigm Shift
Where Sowilo starts from images, PageMind starts from intent. With €1.2 million in new funding, the company is building an AI platform to help e-commerce businesses optimise product content for how items are discovered, understood and recommended across digital channels and AI-powered search engines. PageMind analyses consumer behaviour and search intent to generate product descriptions, buying guides, comparison pages and FAQs, and deploys conversational AI assistants while tuning product visibility for AI search platforms. In other words, it assumes that discovery will increasingly happen through systems like ChatGPT, Gemini and Perplexity rather than old-school keyword searches, and it designs content for those systems first. As Jaume Portell argues, "Traffic no longer depends exclusively on traditional search engines or performance marketing campaigns, but increasingly on AI systems that interpret, recommend and synthesise information from multiple sources." PageMind is betting that retailers who treat AI systems as the new storefront will win the next era of AI product discovery.
Why Retailers Suddenly Care About Structured, AI-Ready Product Data
The rush toward platforms like Catecut and PageMind exposes a painful truth: most online stores remain optimised for human browsing and legacy search, not for AI recommendation engines. As consumers increasingly rely on AI systems to research and compare products, poorly structured data means products will be ignored or misrepresented. Retailers have long underinvested in e-commerce product intelligence, treating titles, tags and FAQs as low-priority chores. That complacency is no longer viable. AI-powered search engines and generative assistants read product information across multiple sites, compare it and decide what to surface. If a retailer’s catalog lacks clear attributes, rich descriptions and contextual guides generated with AI product content optimization in mind, it falls behind smarter competitors. Sowilo’s focus on fashion attributes and PageMind’s focus on search intent both point to the same conclusion: discovery is now awarded to whoever feeds algorithms the cleanest, richest signals.
From Tools to Category: The Emerging Future of AI Product Discovery
The funding flowing to Sowilo and PageMind is more than a win for two startups; it marks the rise of AI product intelligence as a distinct layer in the e-commerce stack. Both companies plan to use their capital to advance platform development and expand internationally, indicating that demand for smarter discovery tooling is not local or niche. The practical impact for everyday shoppers will be subtle but significant: better product matches, clearer descriptions, and more relevant recommendations across sites and AI assistants. For retailers, the message is blunt: treating product content as an afterthought is now a strategic risk. AI product discovery will favour brands that treat metadata, attributes and intent signals as first-class assets. The next phase of online retail will not be decided by who has the loudest ads, but by who teaches AI systems to understand their products best.
