Cotopaxi's Strategy for Agentic AI Product Discovery

Cotopaxi Is Getting Ahead of the Agentic AI Curve in Ecommerce

The outdoor gear industry has always rewarded brands that move early. Whether it's embracing direct-to-consumer sales, expanding into physical retail, or integrating with online marketplaces, the brands that prepare infrastructure ahead of demand tend to win. Cotopaxi, the digitally native outdoor gear brand founded 12 years ago, is applying that same forward-thinking philosophy to one of the most consequential shifts in digital commerce today: agentic AI discovery.

With 22 physical stores, a robust wholesale operation, and a presence across multiple online marketplaces, Cotopaxi has already navigated the complexity of multi-channel retail. Now, the company is turning its attention to something more ambitious — making its product catalog readable, interpretable, and surfaceable by large language models (LLMs) such as OpenAI's ChatGPT and Google's Gemini.

What Is Agentic AI Discovery and Why Does It Matter?

To understand why Cotopaxi's strategy is significant, it helps to understand the distinction between traditional product discovery and what's emerging as "agentic" or contextual discovery.

Traditional SEO and product discovery are keyword-driven. A shopper types "waterproof hiking jacket" into a search engine or a marketplace search bar, and results are ranked based on keyword relevance, authority signals, and listing optimization. It's a largely static interaction — the system matches terms to terms.

Contextual discovery, by contrast, works the way humans actually think and speak. A shopper might ask an AI assistant: "What's the best jacket for hiking in rainy Pacific Northwest conditions in autumn if I run hot but also need packability?" That's not a keyword string. It's a nuanced, multi-variable request that requires the AI to understand product attributes at a deeper level and match them meaningfully to the user's intent.

Agentic AI platforms — AI systems that can take autonomous actions on behalf of users, including researching and potentially purchasing products — rely on this kind of contextual understanding. For a brand's products to appear in those results, the product data itself must be structured in a way that LLMs can ingest, interpret, and reproduce accurately.

Lessons Learned From Marketplace Integrations

Cotopaxi's path toward agentic AI readiness hasn't started from scratch. The company has been doing a version of this work for years through its marketplace integrations. Selling on platforms like Amazon, REI's marketplace, or other third-party channels requires brands to adapt and enrich their product data — adding specific image attributes, refining text-based product descriptions, meeting each platform's unique metadata requirements, and ensuring that product catalogs are clean, complete, and channel-appropriate.

That discipline of structured, enriched, platform-optimized product data turns out to be foundational for LLM readiness as well. The core principle is the same: make your product information machine-readable in whatever format the receiving system demands. What changes is the system doing the reading.

Stephan Jacob, chief global officer and co-founder at Cotopaxi, explained it directly: the company is "fully mindful and aware of the shift from traditional SEO, traditional product discovery, into agentic discovery" and is actively working to ensure that the metadata it surfaces is "structured the right way, so that it can be ingested and reproduced accordingly in the different agentic environments."

The Technical Side: Making Product Catalogs Machine-Readable for LLMs

So what does it actually mean to make a product catalog readable by an LLM? It involves several interconnected layers of data preparation.

Attribute completeness: Every product needs a comprehensive set of attributes — materials, dimensions, weight, intended use cases, weather resistance ratings, sustainability certifications, and more. Sparse listings that rely on shoppers to infer details will be invisible to AI systems that need explicit data to reason about.
Natural language descriptions: Descriptions need to be written in a way that mirrors how humans discuss products in conversation, not just how they search for them. This supports the contextual reasoning LLMs use to match products to nuanced queries.
Structured metadata: Schema markup, structured data formats, and clean taxonomy all help LLMs parse and categorize products reliably. Ambiguous or inconsistent categorization creates noise that reduces discoverability.
Image attributes: AI systems increasingly interpret image-based product information as well. Alt text, image tags, and visual attribute data all contribute to a more complete machine-readable profile of a product.

Jacob noted that Cotopaxi currently leverages partners to manage this data optimization work, and has not yet directly integrated with any LLMs. This staged approach — building the data infrastructure through partners before pursuing direct integrations — is a sensible strategy that allows the brand to refine its data quality without the complexity of maintaining live API connections to rapidly evolving AI platforms.

The Broader Shift: From SEO to AEO (AI Engine Optimization)

What Cotopaxi is doing reflects a broader trend that every ecommerce brand will need to grapple with in the coming years. The discipline of Search Engine Optimization, which has governed digital product visibility for more than two decades, is expanding into what some are beginning to call AI Engine Optimization, or AEO.

AEO involves optimizing content and product data not just for ranking algorithms in search engines, but for the comprehension capabilities of AI models. The goal isn't simply to appear in a list of results — it's to be the product that an AI confidently recommends when a user asks a question that your product answers well.

This has significant implications for how brands think about content strategy, product data governance, and even how they write product copy. Brands that continue to optimize exclusively for keyword-based search while ignoring contextual AI discovery risk becoming progressively less visible as consumer behavior shifts toward AI-mediated shopping.

What Other Brands Can Learn From Cotopaxi's Approach

Cotopaxi's strategy offers a practical roadmap for brands at various stages of AI readiness. The key takeaways are clear.

Start with data hygiene. Clean, complete, well-structured product data is the foundation for everything else — marketplace performance, SEO, and agentic AI discoverability.
Treat each new channel as a data enrichment opportunity. The work done to optimize for one marketplace often transfers to other environments, including AI platforms.
Work with partners to bridge the gap. Not every brand needs to build in-house LLM integration expertise immediately. Leveraging partners who specialize in AI-ready data structuring allows brands to move faster with less risk.
Think in terms of contextual value, not just keywords. The question to ask is not "what keywords does my product rank for?" but "what questions does my product best answer, and is that answer embedded clearly in my data?"

Looking Ahead: The Agentic Commerce Era Is Coming Faster Than Expected

The pace of adoption for AI-assisted shopping is accelerating. Major LLM providers are actively building commerce capabilities into their platforms, and early indications suggest that consumers are increasingly willing to delegate product research and discovery to AI assistants. Brands that have their data infrastructure in order will be positioned to benefit from this shift. Those that haven't will find themselves in the same position as brands that ignored mobile commerce in 2010 or ignored marketplace integration in 2015 — playing an expensive game of catch-up.

Cotopaxi's early and deliberate investment in agentic AI readiness is a reminder that in digital commerce, the brands that treat infrastructure as a competitive advantage, rather than a back-office cost center, consistently outperform. The outdoor gear brand hasn't directly plugged into any LLMs yet — but when the moment is right, its data will be ready. That's the point.