How Cotopaxi Is Preparing Its Data for Agentic AI Discovery
Cotopaxi is restructuring its product data to thrive in an agentic AI world — here's what that means for the future of ecommerce discovery.
How Cotopaxi Is Getting Ahead of the Agentic AI Ecommerce Shift
The ecommerce landscape is undergoing one of its most significant structural changes in years. Traditional keyword-based search is giving way to something far more nuanced — agentic AI discovery. And outdoor gear brand Cotopaxi is already taking steps to make sure it doesn't get left behind.
Founded 12 years ago as a digitally native brand, Cotopaxi has since expanded into a multichannel retailer with 22 physical stores, a robust wholesale operation, and a presence across multiple online marketplaces. Now, the brand is looking even further ahead — preparing its product data infrastructure to integrate seamlessly with large language models (LLMs) like Google's Gemini and OpenAI's ChatGPT.
The move signals a broader strategic awareness: the next frontier of product discovery won't be driven by search bars and static keywords. It will be driven by AI agents that understand context, intent, and natural human language. Brands that aren't ready will simply be invisible.
What Is Agentic AI Discovery and Why Does It Matter?
To understand why Cotopaxi's data strategy is so forward-thinking, it helps to first understand what agentic AI discovery actually means — and how it differs from the product discovery models most retailers are used to.
Traditional product discovery is static and keyword-driven. A shopper types "waterproof hiking jacket" into a search engine or a retailer's site, and an algorithm surfaces products that match those exact terms. It's a system built around precise, pre-defined inputs.
Contextual or agentic discovery works entirely differently. Instead of parsing keywords, AI agents interpret full sentences, conversational phrasing, and nuanced intent. A user might ask an AI assistant, "What's a good jacket for hiking in the Andes in November?" — and the AI would need to understand climate, activity type, and product suitability to return a useful answer.
Stephan Jacob, chief global officer and co-founder of Cotopaxi, described the distinction clearly in a conversation with Digital Commerce 360: static discovery is keyword-driven, while contextual discovery relies on natural human language. For an AI agent to surface the right product at the right moment, it needs to ingest structured, machine-readable product data — not just a title and a price.
Lessons Learned From Marketplace Integration
Cotopaxi didn't arrive at this data-readiness strategy from scratch. The brand's experience selling across multiple online marketplaces gave it a practical education in what channel-specific data optimization actually looks like.
Each marketplace — whether it's Amazon, REI, or any other platform — has its own unique requirements for product listings. That means retailers need to tailor both image-based and text-based attributes to meet the specific standards of each channel. A product listed on one marketplace might need different sizing attributes, different image formats, or different descriptive language than the same product listed elsewhere.
This process of continuous, channel-specific data enrichment turns out to be directly applicable to LLM integration. Just as marketplaces require structured, platform-specific product data, AI agents require machine-readable catalogs built around the attributes those models prioritize.
For Cotopaxi, the operational muscle built through years of marketplace management is now being applied to the emerging challenge of agentic AI readiness. It's not a reinvention — it's an extension of an already-established data discipline.
Making Product Data Machine-Readable for LLMs
The core technical challenge Cotopaxi is tackling is making its product catalog intelligible to AI systems. That requires more than just clean product descriptions. It means structuring metadata in a way that LLMs can ingest, interpret, and reproduce accurately across different agentic environments.
Jacob told Digital Commerce 360 that Cotopaxi is "fully mindful and aware of the shift from traditional SEO, traditional product discovery, into agentic discovery" and is actively structuring its metadata to be surfaced correctly in those new environments.
This involves identifying which attributes are most relevant to the LLMs that will be acting as discovery intermediaries. Things like material composition, use-case suitability, climate ratings, sustainability credentials, and activity-specific features become far more important in a contextual AI environment than they might be in a simple keyword-search context.
Currently, Cotopaxi is doing this work through third-party partners rather than through direct integrations with LLM providers. While the brand has not yet plugged directly into any of the major AI platforms, the groundwork is being laid to make that transition as seamless as possible when the time comes.
The Broader Implications for Ecommerce Brands
Cotopaxi's approach carries important lessons for any retailer thinking about long-term discoverability. A few key takeaways stand out.
Data quality is now a competitive moat. As AI agents become more influential in the product discovery journey, brands with rich, well-structured, machine-readable product data will have a significant advantage over those relying on thin or poorly organized catalogs.
Multichannel experience is a training ground. Retailers that have already navigated the complexity of marketplace integrations are better positioned to adapt to LLM requirements. The skills transfer more directly than many might expect.
SEO strategy needs to evolve — now. The shift from keyword-based to contextual discovery isn't a future problem. Brands that wait until agentic AI platforms are fully mainstream before updating their data strategies will already be behind. Preparation needs to happen before the moment of demand.
Partnerships can accelerate readiness. Not every brand needs to build in-house LLM expertise from day one. Cotopaxi's use of partners to manage data structuring shows that there are accessible paths to AI readiness that don't require massive technical investment upfront.
A Glimpse Into the Future of Product Discovery
What Cotopaxi is doing today may look like a niche technical initiative, but it points toward a fundamental shift in how consumers will find and buy products within the next few years. As AI assistants become more embedded in everyday shopping behavior — helping users research, compare, and purchase without ever visiting a traditional search engine or storefront — the brands that have invested in contextual data readiness will be the ones that show up in those conversations.
The shift from static to contextual discovery isn't just a change in technology. It's a change in the nature of the relationship between product information and buyer intent. Brands that understand this — and act on it early, as Cotopaxi is doing — will be far better positioned to capture demand in an AI-mediated commerce world.
For ecommerce operators of all sizes, the message is clear: your product data is no longer just for search engines and marketplace algorithms. It's increasingly for AI agents, too. Structuring that data with care, depth, and machine-readability isn't optional anymore — it's the foundation of future discoverability.