E-Commerce Brand Triples AI-Referred Traffic in 90 Days

The Client: Ridgeline Supply Co.

Ridgeline Supply Co. is a specialty outdoor gear and apparel brand based in Boulder, Colorado. Founded eight years ago by Casey Marsh, the company has built a strong reputation in the backcountry community, with annual revenue of $4.2M and a loyal customer base. On Google, Ridgeline performs exceptionally well—ranking on page 1 for over 40 keywords, from "ultralight hiking boots" to "alpine climbing layers."

But there was a problem Casey discovered in early 2025. When he asked ChatGPT to recommend "the best specialty outdoor gear brands for backcountry hiking," Ridgeline didn't appear. Instead, the AI model returned Patagonia, Arc'teryx, REI, and the usual household names. Despite eight years of brand building, strong organic search visibility, and hundreds of passionate customer reviews, Ridgeline was invisible to AI systems. And that invisibility was costing the company real revenue.

The Challenge: E-Commerce AVO Is Different

E-commerce visibility optimization (AVO) operates on fundamentally different principles than local business AVO or B2B SaaS. There's no geographic anchor to leverage. You're competing directly with massive brands in crowded categories where AI models have learned—through billions of training tokens—to default to the most well-known names. The question Casey posed to SurfAI was simple but profound: can a $4M specialty brand carve out meaningful AI visibility against household names?

The Baseline Audit: A Visibility Crisis

We began with a comprehensive AI Visibility Audit. What we found was striking:

What the Audit Revealed

• Product schema was missing entirely. AI models had no structured understanding of what Ridgeline actually sold, how it was categorized, or how it compared to competitors. Each product page looked like any other e-commerce site to a machine learning model.
• All content was product-focused. There were no expertise pieces, no "how to choose" guides, no educational content. This meant Ridgeline couldn't rank for the informational queries that AI models rely on to build context and authority.
• Brand mentions were self-referential. Almost all online mentions of Ridgeline came from their own social media channels. There was no press coverage, no gear review site features, no editorial validation from recognized authorities in the outdoor space.
• Entity naming was inconsistent. Across 18 directories and platforms, the brand appeared as "Ridgeline Supply," "Ridgeline Supply Co.," and "Ridgeline Outdoor." This fragmentation confused entity linking algorithms and diluted the brand's authority signal.

The 90-Day Strategy: Three Coordinated Phases

We designed a three-phase, 90-day roadmap that addressed each visibility gap simultaneously. This wasn't a "quick wins" approach—it was a systemic rebuilding of how AI systems understood and valued Ridgeline.

The Results: Beyond Expectations

After 90 days, the impact was measurable and significant:

More specifically: queries for "backcountry pack comparison," "best hiking boots for alpine," and "ultralight layering systems" now consistently returned Ridgeline recommendations. The brand went from appearing in 3% of relevant AI queries to being mentioned in 31% of them. And this wasn't through paid placement—it was through editorial credibility.

The Key Insight: Editorial Credibility Is the Real Unlock

The schema implementation was necessary but not sufficient. Product structured data helps AI understand what you sell, but it doesn't tell the model why to recommend you. The real breakthrough came when authoritative outdoor publications—OutdoorGearLab and Section Hiker—began citing Ridgeline in their own content. AI models learn from what trusted sources say. When a publication with domain authority in the outdoor space writes about Ridgeline's climbing layers or recommends their packs, that creates a signal that no amount of product schema alone can generate.

For e-commerce brands competing against giants, this is the strategic insight: you can't paid your way to AI visibility, and you can't simply optimize your way there either. You have to become a source of authority that other credible sources want to cite.

What This Means for E-Commerce Brands

The Ridgeline case study has three critical implications for any e-commerce brand navigating AI visibility:

1. Schema Is Table Stakes, Not a Differentiator

Implementing Product schema is essential. AI models need to understand your inventory, pricing, and availability in machine-readable format. But every major e-commerce platform now uses structured data. Schema implementation gets you in the game but doesn't win it. You need the next layer.

2. Editorial Placement Is the Competitive Advantage

Small brands can't compete with Nike, Patagonia, or REI on brand recognition. But they can beat them on editorial relevance. When you've published five authoritative guides that review publications cite, when you're the expert that journalists call for quotes, you're signaling authority in a way that pure brand size cannot match. AI models recognize and reward this.

3. Content Strategy Scales Faster Than Paid Media

Paid search and paid social can create quick spikes, but they're perpetually expensive and stop working the moment you stop spending. A well-executed content and editorial strategy compounds. Each guide you publish, each interview you land, each external citation you earn makes the next one easier to secure. After 90 days, Ridgeline had momentum.