The pet food market is fiercely competitive, and a familiar brand name is no longer a guarantee of being discovered. Freshpet, a well-known name in fresh pet food, confronted a disconcerting reality: despite years of strong search engine optimization and established brand awareness, the company was nearly invisible when a consumer asked an AI assistant for a recommendation. This gap exposed a fundamental shift in product discovery, one that requires more than a top ranking in a traditional search engine results page. The company’s response offers a detailed blueprint for any brand waking up to the complexities of generative engine optimization (GEO).
In an on-demand webinar, Brittni Ratliff and Cosima Compton of Intero Digital joined Freshpet’s Steven Elwell to dissect how the team assessed the brand’s AI visibility and built a deliberate GEO program. The discussion moved past generic advice about “writing for AI” and delved into the strategic, editorial, technical, and measurement choices that help machines understand a brand in context without sacrificing usefulness for human readers. The lessons from the session provide a practical framework for marketers facing a similar challenge.
Why Strong SEO Was Not Enough for AI Visibility
Elwell entered the project with a confident assumption. Freshpet had years of content, third-party coverage, and established visibility in the pet food category. He believed this foundation would naturally make the brand relevant to AI-generated results about fresh pet food. It did not. The disconnect showed why marketers cannot treat AI visibility as an automatic extension of traditional rankings or brand awareness.
Compton explained that large language models (LLMs) attempt to understand industries, concepts, and relationships between entities. They do not simply reproduce a conventional search results page. A recognized brand can therefore be absent when the available content does not clearly establish how it relates to a user’s specific question. Freshpet’s experience illustrates why the first step in any GEO program should be observation rather than assumption. Marketers should ask the questions their customers are likely to ask, record what the AI systems return, and identify where their brand is missing, misrepresented, or supported by outdated information.
A Manual Starting Point for AI Visibility Audits
Compton outlined a manual starting point for teams that do not yet use a dedicated AI visibility platform. The process involves building a representative set of at least 20 to 30 prompts that cover the core questions in a given market. Teams then run those prompts across systems such as ChatGPT, Gemini, and Claude. The next step is to record whether the brand appears, which sources are cited, and whether the response is accurate. Weak areas are compared with the corresponding pages on the brand’s site to evaluate relevance, authority, structure, and engagement. Finally, fixes are prioritized for topics closest to revenue, conversion, or a major customer objection.
The Four Pillars of AI Visibility: Relevance, Authority, Structure, and Engagement
Intero Digital presented a framework that breaks down the factors influencing AI visibility into four practical areas. These pillars are most effective when they support one another, rather than operating as disconnected channels.
- Relevance: Publish content that directly answers current audience questions and matches the intent behind them. Compton recommended reviewing pages regularly, especially when they contain changing statistics or other time-sensitive details.
- Authority: Give claims enough context and support to be trusted. This includes clear author credentials, citations to reputable sources, relevant industry coverage, directory listings, and third-party mentions that reinforce what the brand is known for.
- Structure: Make important information accessible to both crawlers and readers. Clean HTML, strong page speed, descriptive headings, concise passages, lists, tables, schema, and direct answers can all help systems parse a page.
- Engagement: Pay attention to how customers and experts discuss the brand across reviews, forums, social platforms, and industry publications. Those conversations can expose concerns and language that deserve a well-sourced answer on the brand’s own site.
Writing to Be Useful, Understandable, and Quotable
One of Elwell’s clearest observations was that much of Freshpet’s legacy content had been written like a magazine article. It was designed to draw a person through a narrative from beginning to end. While that style serves readers, the key facts are harder for an LLM to extract when they are buried in long passages. The answer is not to strip away brand voice or write awkward copy for robots.
The speakers recommended organizing complex ideas into focused sections that can stand on their own. A page can still sound natural while using descriptive headings, direct answers, short paragraphs, and meaningful lists. Elwell described the shift as writing content that can be quoted, not merely read. For Freshpet, that meant revisiting page templates and formatting rules as well as the words themselves. The team simplified elements of its blog presentation, cleaned up HTML, and made it easier to apply appropriate schema.
The webinar also surfaced an easy-to-miss technical issue: valuable content can exist on a page but remain difficult for crawlers to access. Freshpet found that product reviews and Q&A content delivered through JavaScript were not readily visible to LLMs. The takeaway is to audit the rendered experience, not just the editorial inventory. Marketers should check crawler controls, JavaScript dependencies, robots directives, page speed, and whether the most important answers are available in parseable HTML.
How Does GEO Differ from Traditional SEO?
During the Q&A, Compton addressed a common question about the relationship between GEO and SEO. He said teams should not expect GEO improvements to create a negative relationship with traditional SEO. Keyword alignment, headings, crawlability, credible sourcing, and clear site architecture remain valuable. Better-structured pages may support both AI visibility and conventional search performance, even as zero-click behavior changes traffic patterns. The two disciplines are complementary, not competitive.
Using Relevant Third-Party Authority, Not Just Big-Name Coverage
The speakers drew a useful distinction between having backlinks and building contextual authority. A mention in a major publication can still be valuable, but an industry-specific source may do more to establish a brand’s relationship to a specialized topic. Elwell noted that a credible pet industry publication can reinforce Freshpet’s expertise in ways a broad national outlet may not.
Ratliff added that the surrounding coverage matters. If every mention uses the brand name as anchor text and points to the homepage, LLMs receive less context about the specific subjects the brand should be associated with. Earned media works harder when the article itself accurately explains the brand’s expertise and supports a relevant on-site resource. The goal is contextual authority, not just link volume.
Treating Community Discussion as Research, Not a Battlefield
Reddit and other community platforms matter because customers use them to exchange candid opinions, and LLMs can draw from those conversations. But the webinar did not recommend that every brand jump into every thread. Freshpet takes a listening-first approach. Elwell said pet nutrition can be as emotionally charged as conversations about baby food, so arguing with individual users would not serve the brand. Instead, the team looks for recurring questions or misconceptions that its experts can address with authoritative content elsewhere.
That distinction is important: community monitoring is not only reputation management. It can become an input for content planning. When the same concern appears repeatedly, marketers can create a clear, evidence-backed resource that customers and AI systems can both find. This turns a reactive listening exercise into a proactive content strategy.
Building a Prompt Set and Prioritizing the Gaps
Freshpet’s program is much larger than the manual starting point described earlier. Elwell said the team was tracking 475 prompts across the major LLMs and grouping them into topic categories. That structure lets the team spot clusters of weak answers, create content for related questions, and involve subject matter experts where their credentials can strengthen the response. The point is not to copy Freshpet’s prompt count. It is to build a set broad enough to represent the customer journey, then organize it so the findings lead to specific work.
Measuring Progress Without Pretending Attribution Is Simple
AI visibility does not map neatly to the old model of ranking, click, and conversion. Elwell explained that Freshpet’s primary site often sends customers to retailers, making direct revenue attribution difficult. Its subscription business is easier to measure because users can complete a transaction in a logged-in ecommerce experience. The team watches referral traffic from LLM citations, downstream actions, and modeled relationships between AI exposure and direct visits.
Elwell cautioned against getting too far ahead of observable clicks with projected attribution. That is a useful standard for any GEO program: combine directional indicators, visibility trends, and business events, but be explicit about what the data can and cannot prove. The goal is to build a measurement framework that is honest about its limitations while still providing actionable insights.
Practical Tactics for GEO Success
Several other answers from the Q&A reinforced a practical approach. When LLMs keep citing stale information on a page, the best strategy is to update that existing resource rather than creating overlapping pages about the same subject. For complex topics, a pillar-and-cluster structure ensures each subtopic has a clear home and supporting pages connect logically. The type of prompt and the freshness of information affect whether an LLM relies on trained knowledge or conducts an active search. Marketers can use AI tools to critique content, but the feedback is only meaningful when it is grounded in business, audience, search, and competitive context.
Freshpet’s experience shows that GEO is not a single markup change or content format. It is a coordinated process: learn how the brand appears, make its expertise easier to verify, remove technical barriers, build contextual authority, and measure the outcomes with appropriate caution. The framework that Intero Digital presented—relevance, authority, structure, and engagement—gives marketers a concrete way to diagnose their current state and prioritize fixes. The full webinar provides the speakers’ complete framework, Freshpet examples, implementation details, and rapid-fire answers to audience questions. For any brand watching its visibility shift in the age of AIaa-generated answers, the playbook is now available.
