Wildcard Is Building the SEO of AI Shopping, and Brands Should Pay Attention

The Macro: Shopping Is Moving Into the Chat Window

ChatGPT launched a shopping feature. Perplexity has product recommendations. Every AI assistant is moving toward commerce. This is not a subtle trend. When hundreds of millions of people start asking AI assistants “what’s the best running shoe under $150?” instead of typing it into a search engine, the entire e-commerce marketing stack needs to be rebuilt.

The parallels to early SEO are almost exact. In 2003, most businesses had no idea how search engine rankings worked. A small number of people figured out the rules early, and those businesses dominated organic traffic for years before everyone else caught up. The same dynamic is playing out with AI shopping. Most e-commerce brands have zero visibility into how AI assistants decide which products to recommend. They don’t know what queries surface their products. They don’t know what attributes the AI is looking at. They don’t know how they compare to competitors in AI-generated recommendations.

The market size here could be enormous. E-commerce brands collectively spend over $200 billion annually on digital advertising and SEO. If even 10% of product discovery shifts to AI assistants over the next three years, that’s a $20 billion reallocation of marketing spend looking for new tools and strategies. The brands that figure out AI commerce optimization first will have a structural advantage, just like the brands that figured out SEO in 2005 or Facebook ads in 2013.

The Micro: Analytics for a Shopping Channel That Didn’t Exist Last Year

Wildcard tracks how products appear in ChatGPT shopping results across thousands of high-intent queries. You can see which queries surface your products, which queries surface your competitors, and what attributes the AI is using to make recommendations. It’s the kind of visibility that e-commerce teams are used to having for organic search and paid ads but currently have zero insight into for AI shopping.

The product has four main capabilities. Query intelligence identifies which shopping questions surface your products and where the gaps are. Attribute optimization analyzes how AI describes your products and helps you refine your data feeds to improve how you show up. Competitive positioning tracks where category leaders rank and what’s driving their visibility. Product performance monitoring watches your placement across your full catalog in AI shopping results.

Kaushik Mahorker is the CEO. He was an engineering manager at Scale AI where he led the GenAI allocation team and built an e-commerce enrichment engine that processed 2.4 million attributes across 400,000 SKUs. Before that, he was at AWS. Yagnya Patel is the cofounder with experience in NLP and knowledge graphs at Tesla and Amazon. They’re a two-person team from YC’s Winter 2025 batch.

The Scale AI background is particularly relevant. Scale is one of the companies that helped train the models now being used for AI shopping. Mahorker was literally working on the e-commerce data that feeds these systems. Understanding how product attributes flow into model training and then into recommendations is exactly the kind of insider knowledge that makes a founding team dangerous in a good way.

They also have something called the Agentic Commerce Protocol, or ACP, which enables instant checkout directly within ChatGPT. If a user asks ChatGPT for a product recommendation and clicks through to buy, ACP handles the transaction without leaving the chat interface. Wildcard handles the technical implementation for brands that want to participate.

One stat from their site jumped out to me: “67% of products lack the attributes AI needs to recommend them.” If that’s accurate, it means the majority of e-commerce catalogs are essentially invisible to AI shopping. Not because the products are bad, but because the product data isn’t structured in a way that AI assistants can parse and recommend. That’s a fixable problem, and the company selling the fix has a clear value proposition.

The Verdict

This feels like the right product at the right moment. AI shopping is going from experiment to real channel, and brands need tooling. Wildcard is building exactly that tooling. The founding team has direct experience with the data and systems that power AI commerce. The product already integrates with Shopify, BigCommerce, WooCommerce, Magento, and Square, which covers the vast majority of e-commerce platforms.

The risk is timing. If AI shopping grows slower than expected, Wildcard is selling optimization for a channel that doesn’t move enough volume to justify the spend. E-commerce brands are pragmatic. They allocate budget based on revenue attribution. If ChatGPT shopping drives $500 in monthly revenue for a brand, that brand is not paying for analytics tooling. The channel needs to reach meaningful scale before the analytics layer becomes a must-have.

The other risk is that the AI assistants themselves start providing this data directly. If ChatGPT launches a merchant dashboard that shows brands how they appear in shopping results, Wildcard’s core product becomes redundant. That said, the AI companies have a lot on their plates right now, and building e-commerce analytics tools is probably not their top priority for the next 12 to 18 months.

At 30 days, I’d want to see how many e-commerce brands have connected their catalogs. At 60 days, whether any brands can attribute meaningful revenue to optimizations made using Wildcard data. At 90 days, the question is whether AI shopping volume is growing fast enough to make this a priority budget item rather than an experimental line item. The thesis is strong. The execution window is probably narrow.