How Cotopaxi Is Preparing Its Data for Agentic AI Discovery

How Cotopaxi Is Preparing Its Data for Agentic AI Discovery

The world of ecommerce is shifting beneath the feet of even the most digitally savvy brands. For outdoor gear company Cotopaxi, that shift is not something to fear — it's something to plan for, invest in, and embrace. As agentic AI platforms like Google's Gemini and OpenAI's ChatGPT begin to play a larger role in how consumers discover and purchase products, Cotopaxi is making sure its product data is ready to meet that moment.

Founded 12 years ago as a digitally native brand, Cotopaxi has grown considerably. The company now operates 22 physical retail locations, sells wholesale to retail partners, and distributes its products across multiple online marketplaces. That multichannel complexity has given Cotopaxi a practical education in data optimization — and those lessons are now being applied to the emerging frontier of AI-driven commerce.

From Marketplace Integration to Agentic AI Readiness

To sell effectively across different online marketplaces, Cotopaxi has long had to tailor its product data to meet each platform's unique requirements. That means enriching product listings with specific text-based and image-based attributes that each channel demands. The process is detailed, deliberate, and ongoing.

What Cotopaxi has recognized is that this same discipline applies directly to large language models. Whether a brand is plugging into Amazon, a specialty retailer's platform, or an AI assistant capable of making purchase recommendations on a consumer's behalf, the underlying need is identical: structured, high-quality, machine-readable product data.

Stephan Jacob, chief global officer and co-founder at Cotopaxi, explained the company's thinking in a conversation with Digital Commerce 360. According to Jacob, success in agentic AI environments requires making the product catalog readable by machines in the way each LLM requires of retailers — not just searchable by humans.

Understanding the Difference: Static vs. Contextual Discovery

One of the most important distinctions Jacob highlighted is the difference between static product discovery and contextual product discovery. This distinction sits at the heart of why brands need to rethink how they structure and surface their product metadata.

Static discovery is the traditional keyword-driven model. A shopper types "waterproof hiking jacket" into a search bar, and an algorithm surfaces products based on how well those keywords match. It's a model that has shaped SEO strategy for over two decades.

Contextual discovery is different in a fundamental way. When a consumer interacts with an AI assistant and says something like, "I'm planning a week-long hiking trip in Patagonia in March and I need a jacket that can handle wind and rain but won't be too bulky in my pack," the LLM needs to interpret that entire request in natural human language and match it to relevant products. Keyword density alone won't get a brand's products surfaced in that kind of interaction.

For Cotopaxi, preparing for this means structuring metadata so that it can be ingested and reproduced accurately across different agentic environments. The attributes that matter to an LLM may include technical specifications, use-case descriptions, sustainability credentials, fit information, and a host of other contextual signals that go well beyond a product title and bullet points.

Jacob's Strategic Vision: Embracing the Metadata Shift

"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," Jacob told Digital Commerce 360.

That statement reflects a broader strategic awareness that many brands have yet to develop. While most ecommerce businesses are still optimizing for traditional search engines, forward-thinking companies like Cotopaxi are already building infrastructure for the next era of discovery. Jacob confirmed that the brand currently works with partners to manage this process and has not yet directly integrated with any LLM platforms, but the groundwork is being laid.

Why This Matters for the Broader Ecommerce Industry

Cotopaxi's approach offers important lessons for any brand operating in ecommerce today. The rise of agentic AI is not a distant trend — it is already influencing how consumers seek product recommendations, compare options, and make purchase decisions. Brands that treat their product data as a strategic asset will be better positioned to compete in this environment.

Several key principles emerge from Cotopaxi's strategy that other retailers should consider adopting:

• Invest in structured metadata: Every product attribute, from material composition to intended use case, should be clearly defined, consistently formatted, and machine-readable. Vague or incomplete product data will be invisible to AI discovery systems.
• Think in natural language, not just keywords: LLMs interpret queries the way humans phrase them. Brands should ensure their product descriptions speak to real-world use cases, customer needs, and contextual situations — not just target search terms.
• Treat each channel as its own data environment: Just as Cotopaxi customizes its product data for each marketplace, AI platforms will likely have their own requirements and weighting systems. Flexibility and channel-specific optimization will become critical competencies.
• Partner strategically: Cotopaxi has opted to work with specialized partners rather than building direct LLM integrations from scratch. For most brands, leveraging experienced data and technology partners is a practical path to getting AI-ready without overwhelming internal resources.
• Act now, not later: The brands that begin preparing their data infrastructure today will have a meaningful advantage when agentic AI commerce becomes mainstream.

The Bigger Picture: Agentic Commerce Is Already Here

AI-powered shopping assistants are not a future concept. Consumers are already using tools like ChatGPT and Gemini to research products, compare brands, and ask for recommendations before they ever visit a brand's website. For brands that aren't visible in those conversations, sales opportunities are already being lost.

Cotopaxi's willingness to openly discuss its data strategy reflects a level of maturity and foresight that positions the brand well for what comes next. By building on the lessons it has already learned from multi-marketplace integration, the company is translating practical experience into competitive advantage.

As the ecommerce landscape continues to evolve, the brands that thrive will be those that understand data not merely as a backend concern, but as the foundation of every customer interaction — whether that interaction happens on a website, a marketplace, or inside an AI agent's response. Cotopaxi has clearly understood that. The question is whether the rest of the industry will follow before it's too late.