How Cotopaxi Is Preparing Its Data for Agentic AI Discovery

How Cotopaxi Is Preparing Its Product Data for the Age of Agentic AI Discovery

The way shoppers discover products online is changing — fast. For years, search engine optimization meant stuffing product pages with keywords and hoping Google's algorithm would reward your efforts. But a new era is emerging, one driven not by static keyword matching but by intelligent, conversational AI platforms that understand context, nuance, and natural human language. Outdoor gear brand Cotopaxi is already getting ahead of this shift, and their strategy offers a compelling blueprint for ecommerce brands of every size.

From Humble Beginnings to a Multi-Channel Powerhouse

Founded just 12 years ago, Cotopaxi has grown from a digitally native startup into a robust multi-channel retailer. The brand now operates 22 physical stores across the United States, sells wholesale through retail partners, and distributes products through major online marketplaces. That kind of growth doesn't happen by accident — it requires disciplined data management, channel-specific optimization, and a willingness to evolve alongside emerging technologies.

Today, Cotopaxi is turning its attention to one of the most significant shifts in ecommerce since the rise of mobile shopping: the emergence of agentic AI platforms as product discovery and purchase channels. Rather than waiting for the technology to mature before adapting, the brand is actively restructuring its product data infrastructure to meet this new reality head-on.

What Is Agentic AI Discovery — and Why Does It Matter?

To understand why Cotopaxi's approach is so forward-thinking, it helps to understand what agentic AI discovery actually means and how it differs from traditional product search.

Traditional, or "static," product discovery is keyword-driven. A shopper types "waterproof hiking jacket" into a search bar, and an algorithm matches those keywords against indexed product listings. The system is fast and scalable, but it's also rigid. It doesn't understand intent beyond the literal words entered.

Contextual discovery, by contrast, leverages the natural language processing capabilities of large language models (LLMs) like OpenAI's ChatGPT and Google's Gemini. Instead of matching keywords, these systems interpret meaning. A shopper might ask, "What's a good lightweight jacket for a winter hike at altitude where it might rain?" and an agentic AI platform can parse that full sentence, understand the conditions described, and recommend products that genuinely fit the scenario.

Stephan Jacob, chief global officer and co-founder of Cotopaxi, explained the distinction clearly in a conversation with Digital Commerce 360: "We're fully mindful and aware of the shift from traditional SEO, traditional product discovery, into agentic discovery and are embracing that in ensuring that the metadata that we surface is structured the right way, so that it can be ingested and reproduced accordingly in the different agentic environments."

That quote encapsulates both the challenge and the opportunity. The brands that structure their data correctly now will be the ones whose products surface when shoppers ask AI assistants for recommendations tomorrow.

Lessons Learned from Marketplace Integrations

Cotopaxi didn't arrive at its agentic AI strategy in a vacuum. Much of the groundwork was laid through the brand's experience selling on multiple online marketplaces. Each marketplace — whether Amazon, REI's digital storefront, or another platform — has its own specific requirements for product listings. That means Cotopaxi's team has had to optimize product details, including both image-based and text-based attributes, for each individual channel.

This process, while operationally demanding, has produced a valuable byproduct: a disciplined, attribute-rich product catalog that is highly adaptable. By learning to tailor product data for different marketplace environments, Cotopaxi has built the internal muscle memory needed to do the same for LLMs.

The parallel is closer than it might initially appear. Just as a marketplace requires certain fields to be populated in certain ways before it will surface a product to shoppers, LLMs require product catalogs to be machine-readable and structured around the attributes those models prioritize. The underlying workflow — identify what the platform needs, structure your data accordingly, optimize for discovery — is essentially the same.

Making Product Catalogs Machine-Readable for LLMs

So what does it actually mean to make a product catalog "machine-readable" for an LLM? At its core, it means moving beyond sparse product descriptions and toward rich, semantically meaningful metadata.

For Cotopaxi, this involves focusing on the specific attributes that models like Gemini and ChatGPT use when evaluating and recommending products to users. This includes things like:

• Detailed material and construction specifications that answer performance-related questions
• Use-case and activity tags that allow AI models to match products to described scenarios
• Natural language descriptions written in the way real shoppers speak, not just in keyword clusters
• Structured data formats that can be cleanly ingested by third-party AI systems and platforms

Cotopaxi currently relies on partners to manage this integration layer, and Jacob noted the brand has not yet directly connected to any LLM platforms. But the foundational work of structuring and enriching product data is already underway — and that preparation is precisely what will make direct integration possible and effective when the time comes.

What Other Ecommerce Brands Can Learn from Cotopaxi

Cotopaxi's proactive stance offers a clear lesson for other retailers: the brands that wait until agentic AI commerce is fully mainstream before updating their data strategies will already be behind. The time to build machine-readable, semantically rich product catalogs is now, before LLM-driven discovery becomes a dominant channel.

Start by auditing your existing product data for completeness and contextual richness. Are your descriptions answering the questions real customers ask, or are they just lists of features? Do your attributes allow an AI model to confidently recommend your product in response to a nuanced, real-world scenario? If not, there is work to be done.

The shift from keyword-based to contextual discovery is not a distant possibility — it is already happening. Brands like Cotopaxi that are actively structuring their metadata for agentic environments are positioning themselves to win in the next era of ecommerce. For everyone else, the clock is ticking.