How Cotopaxi Is Preparing Its Data for Agentic AI Discovery

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

The way consumers find and buy products online is changing faster than most retailers anticipated. While search engine optimization has long been the backbone of product discovery, a new paradigm is emerging — one driven not by keywords, but by conversational, context-aware AI agents. Outdoor gear brand Cotopaxi is among the forward-thinking retailers already repositioning their data infrastructure to meet this shift head-on.

Founded 12 years ago as a digitally native brand rooted in social impact and outdoor adventure, Cotopaxi has grown from an e-commerce startup into a multi-channel retail operation. Today, the brand operates 22 physical stores, sells through wholesale partnerships, and maintains a presence on major online marketplaces. Now, it is setting its sights on the next frontier: agentic AI platforms powered by large language models (LLMs) such as Google's Gemini and OpenAI's ChatGPT.

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

To understand why Cotopaxi's data strategy is significant, it helps to understand the difference between traditional product discovery and agentic discovery.

Traditional, or "static," product discovery is keyword-driven. A consumer types a phrase like "insulated hiking jacket" into a search engine or marketplace search bar, and results are served based on how well a product listing matches those specific terms. This model has dominated e-commerce for decades and has given rise to the entire science of search engine optimization.

Contextual, or "agentic," discovery works differently. Instead of matching keywords, AI agents use natural language to understand what a shopper is actually looking for — processing full sentences, nuanced descriptions, and conversational queries. A user might ask an AI assistant, "What's a good lightweight jacket for hiking in Patagonia in October that I can also wear to dinner?" An agentic AI system processes that complex request and surfaces relevant products based on deep attribute understanding, not just surface-level keyword matching.

For retailers, this shift is profound. Products that are not structured in a way that LLMs can ingest and interpret will simply not appear in agentic discovery results — regardless of how well-optimized they are for traditional search.

Cotopaxi's Multi-Channel Data Approach Laid the Groundwork

Cotopaxi's readiness for the agentic AI era is not accidental. It grew organically from the brand's experience managing product data across multiple sales channels.

Selling on multiple marketplaces — from Amazon to specialty outdoor retailers — requires Cotopaxi to optimize product listings for each individual channel. Every platform has its own requirements for product attributes, whether those are image specifications, material descriptions, size charts, or use-case tags. Cotopaxi has learned to build rich, layered product data that can be adapted to meet the specific demands of each environment.

That same discipline, it turns out, is exactly what's needed to make products discoverable by LLMs. The key is ensuring that a product catalog is machine-readable and that the metadata is structured in a way that AI systems can ingest, interpret, and reproduce accurately in response to user queries.

Stephan Jacob, Chief Global Officer and co-founder at Cotopaxi, explained the brand's philosophy clearly when speaking 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."

The Role of Structured Metadata in LLM-Driven Commerce

At the heart of Cotopaxi's strategy is a commitment to structured, high-quality metadata. This goes beyond simply writing good product descriptions. It involves tagging products with precise, relevant attributes that an AI agent can understand in context.

For example, rather than describing a backpack as "durable and versatile," structured metadata might specify waterproof rating, carrying capacity in liters, intended use cases such as day hiking or multi-day expeditions, compatible frame sizes, material composition, and sustainability certifications. Each of these attributes gives an LLM something concrete to work with when matching a product to a nuanced user query.

The shift mirrors a broader trend in e-commerce data management: moving from content written for human readers to content that serves both human readers and machine interpreters simultaneously. The brands that master this balance will have a significant competitive advantage as AI-assisted shopping becomes mainstream.

Leveraging Partners to Bridge the Gap

Cotopaxi has been transparent about the fact that it has not yet directly integrated with any LLM platforms. Instead, the brand currently relies on partners to facilitate the translation of its product data into formats that are compatible with AI discovery environments.

This partner-led approach is a practical and scalable way for mid-sized brands to navigate a rapidly evolving landscape without bearing the full technical burden in-house. It also reflects a savvy awareness that the specific requirements of each LLM platform are still being defined — staying agile through partnerships allows Cotopaxi to adapt as standards emerge.

Lessons Other Retailers Can Learn from Cotopaxi's Strategy

Cotopaxi's journey offers several actionable insights for e-commerce brands of all sizes looking to prepare for agentic AI discovery.

• Invest in product attribute depth now. The richer and more precise your product data, the better positioned you are for both current marketplace algorithms and future AI agents. Generic descriptions leave value on the table in every channel.
• Think in use cases, not just categories. AI agents respond well to use-case-driven attributes. Instead of tagging a jacket as "outerwear," describe the specific conditions and activities it is designed for.
• Treat metadata as a strategic asset. Product data is no longer just an operational necessity — it is a competitive differentiator. Brands that invest in structured, consistent, high-quality metadata will win in AI-driven discovery environments.
• Monitor LLM requirements as they evolve. Each major AI platform is developing its own standards for how product data should be structured. Staying informed and agile is essential.
• Consider partnerships to accelerate readiness. Not every brand has the in-house technical resources to directly integrate with LLM platforms. Partnering with specialists can speed up the transition without sacrificing quality.

The Bigger Picture: SEO Is Evolving, Not Dying

It would be a mistake to interpret the rise of agentic AI discovery as the death of SEO. Rather, SEO is expanding. The fundamental goal — making products findable and relevant to the people who need them — remains the same. What is changing is the mechanism by which discovery happens and the type of data that powers it.

Brands like Cotopaxi that have built strong data foundations and embraced multi-channel optimization are discovering that those investments translate naturally into agentic AI readiness. The discipline of thinking carefully about how products are described, tagged, and categorized pays dividends across every discovery channel — from Google search to AI shopping assistants.

As agentic AI platforms become an increasingly important entry point for online shopping, the brands that have prepared their data infrastructure will capture disproportionate visibility. Cotopaxi's proactive approach is a model worth studying — and a signal that the future of e-commerce belongs to those who understand that great products alone are not enough. How those products are described, structured, and surfaced to intelligent systems will determine who wins in the next era of digital retail.