Case Overview
This case features a manufacturer and distributor of veterinary medicines and feed additives operating in a competitive regional market. After completing a full AI visibility audit, we uncovered a striking contradiction: the company had outstanding traditional SEO performance—scoring 92 on PageSpeed's SEO sub-metric—indicating a solid foundation in conventional search optimization. Yet the moment we evaluated its presence within AI-driven search environments, performance dropped sharply. The overall composite score came in at just 45 out of 100, placing AI visibility potential in the "moderate" tier.
This gap reveals a critical reality that many businesses have yet to confront: ranking well on Google does not guarantee that you will be recommended by generative AI platforms like ChatGPT, Claude, or Gemini. For companies in the animal health and feed additive sector facing growing digital transformation pressure, this visibility gap is no longer something that can be deferred.
Composite Score Breakdown
The audit evaluated three core dimensions to map the company's strengths and weaknesses clearly:
| Evaluation Dimension | Score | Notes |
|---|---|---|
| AI Brand Mention Rate | 40 / 100 | Inconsistent cross-platform mentions; nearly absent in industry-level queries |
| GEO Technical Audit | 27 / 100 | Critical structured markup missing; AI crawlers struggle to interpret content |
| Website Performance (PageSpeed) | 70 / 100 | SEO sub-score of 92 is impressive, but performance sub-score of 47 drags down the overall result |
The most significant bottleneck is at the GEO technical level. A score of 27 means the website lacks the structured signals that AI systems rely on to "read" and categorize content. Even when brand queries occasionally surface a mention, industry-intent queries—the kind a buyer uses when they don't already know a brand name—return almost nothing. If a procurement manager asks an AI, "Where can I find feed additive suppliers for livestock?" this company simply does not appear in the recommended results.
AI Search Visibility Testing
We submitted queries to three major AI platforms—Claude, ChatGPT, and Gemini—running four queries per platform for a total of 12 queries. Each set included both direct brand searches and industry-context queries designed to simulate real buyer behavior.
Claude
On brand queries, Claude produced only vague mentions and failed to articulate the company's product positioning or area of specialization, indicating that the AI's understanding of the brand is shallow. For the two industry-context queries, one returned a vague mention and the other returned nothing at all. In practical terms, this means that when a veterinary clinic procurement officer or livestock farm manager asks Claude for veterinary medicine or feed additive supplier recommendations, this company is effectively outside Claude's field of view.
ChatGPT
ChatGPT delivered relatively better results: the brand query earned a positive mention, and one of the two industry queries also returned a positive mention—suggesting the brand does maintain some informational presence online. However, the second industry query returned no mention, revealing that coverage is inconsistent. Depending on how a buyer phrases their question, there remains a high probability that this company will be overlooked entirely.
Gemini
Gemini's results followed a similar pattern to ChatGPT: a positive mention on the brand query, a positive mention on one industry query, and no mention on the other. Gemini appears to have some baseline awareness of the brand, but cannot reliably surface it across varied industry-intent queries.
Across all 12 queries, the company was mentioned 6 times, yielding a raw mention rate of 50%. However, when we isolate industry-intent queries—the scenarios where a buyer has no prior brand awareness and is simply searching by product category—only 2 of 8 queries returned any mention. The real-world market exposure opportunity is far lower than the headline number suggests. This is the core blind spot that animal health companies must address in the age of AI search.
Competitive Landscape
During industry-query testing, AI platforms recommended as many as 7 competing brands, spanning both established domestic veterinary medicine manufacturers and international feed additive suppliers. The brands that earned consistent AI recommendations shared several common characteristics: comprehensive structured data markup, original and authoritative technical content (such as ingredient white papers and application case studies), and a higher frequency of citation by industry media and third-party sources.
For the company in this case study, the presence of these 7 competitors means that every time a prospective buyer turns to an AI platform with a question, it is a missed commercial opportunity. The competitive window remains open, but the lead time available for late movers is narrowing.
GEO Technical Audit
The GEO (Generative Engine Optimization) technical audit assessed 9 key indicators. The company passed only 3, for a pass rate of 33%—well below the recommended threshold. The full results are as follows:
| Technical Item | Status |
|---|---|
| Schema JSON-LD Structured Markup | ✗ Not implemented |
| Sitemap | ✓ Implemented |
| Meta Description | ✓ Implemented |
| OG Tags (Social Preview Tags) | ✗ Not implemented |
| Canonical URL | ✗ Not implemented |
| HTTP/2 Protocol | ✗ Not enabled |
| Title Tag | ✓ Implemented |
| H1 Tag | ✗ Not implemented |
| Bare Domain 301 Redirect | ✗ Not implemented |
The most critical missing element is Schema JSON-LD structured markup. This is one of the primary signals AI systems use to determine whether a website is worth citing. Without Schema markup, AI crawlers cannot automatically identify what products the company offers, what its service scope is, or what trust signals support the brand—directly causing it to be absent from industry-level query results.
Additionally, the missing H1 tag and unset Canonical URL create ambiguity when AI systems attempt to parse page topics, further weakening AI visibility across the board.
Website Performance
Website performance is a foundational condition for AI crawlers to fully index a site's content. The company's PageSpeed performance sub-score of 47 falls within the range that requires improvement. Low performance scores typically indicate uncompressed images, incomplete caching strategies, or unoptimized front-end code—issues that can cause AI crawlers to abandon a crawl session prematurely, leaving portions of the site's content unindexed.
It is worth emphasizing: although the SEO sub-score of 92 reflects well-configured basic SEO tags, the low performance score indirectly undermines overall AI visibility. Within the GEO optimization framework, site speed is not merely a user experience concern—it directly determines whether AI systems can fully comprehend your brand's content. We recommend setting a short-term goal of raising the performance score to 70 or above.
Expert Recommendations
Based on the audit findings, we have identified three priority improvement areas:
Recommendation 1: Implement Schema Structured Markup So AI Can Understand Your Products
The website currently has no Schema JSON-LD markup whatsoever—this is the primary reason the GEO technical score sits at just 27. For companies in the veterinary medicine and feed additive space, three Schema types are especially valuable: Product, Organization, and FAQPage. These help AI systems connect the brand to specific ingredients, indications, and regulatory compliance attributes, significantly increasing the likelihood of appearing in industry-intent query results. Right now, the SEO score is holding the overall rating up while the GEO technical layer is nearly empty.
Recommendation 2: Create Authoritative Content That AI Platforms Want to Cite
When answering industry questions, AI platforms prioritize content that is original, deeply informative, and data-rich. With only 2 appearances across 8 industry-intent queries, the company currently has insufficient authoritative content for AI systems to draw from. Content types such as feed additive application case studies, ingredient efficacy comparisons, and livestock management guides are the core assets needed to displace the 7 competitors currently occupying the AI recommendation slots.
Recommendation 3: Fix Technical Foundations to Remove Barriers for AI Crawlers
Issues such as the bare domain being inaccessible, HTTP/2 not being enabled, and missing OG Tags may each seem minor in isolation, but cumulatively they reduce the trust score AI crawlers assign to the website. These are low-hanging fruit at the technical level—addressing them promptly can produce meaningful gains in the overall GEO technical score within a short timeframe.
AI Search Trends in the Veterinary Medicine and Feed Additive Industry
Procurement decisions in the veterinary medicine and feed additive industry have traditionally depended on highly specialized supplier relationships and product certification frameworks. However, the growing adoption of AI search is quietly reshaping where that procurement journey begins.
Historically, veterinary clinics, livestock farm owners, and aquaculture operators seeking new suppliers relied primarily on trade exhibitions, peer referrals, or direct outreach to known brands. Increasingly, however, procurement personnel—particularly managers at small-to-mid-sized farms or newly practicing veterinarians—are turning first to ChatGPT, Gemini, or Claude with questions like: "What feed additives are commonly used for respiratory disease in pigs?" or "Which GMP-certified veterinary medicine manufacturers should I consider?" or "What should I look for when choosing a probiotic feed additive?"
The defining characteristic of this query behavior is that the buyer has not yet settled on a brand—they are in an open evaluation phase. This is precisely the moment when AI visibility has the greatest influence on purchasing outcomes. If a company's brand information, product ingredient descriptions, and regulatory compliance documentation are presented on the website in AI-readable structured formats, the brand stands a genuine chance of entering the buyer's consideration set at this critical decision point.
One factor worth special attention: the veterinary medicine industry operates under strict regulatory oversight, making the accuracy of product information paramount. AI systems, when selecting content to cite, favor pages with clear sourcing, explicitly stated ingredients, and defined indications. This means that companies in this space who build product information pages aligned with regulatory documentation standards—and pair them with Schema markup—will gain a natural competitive advantage in GEO optimization. High-credibility, well-structured content is exactly what AI platforms are most inclined to reference.
The opportunity window remains open: AI visibility investment across the veterinary medicine sector is still in its early stages. Companies that complete GEO optimization first will build a first-mover advantage over the next 12 to 18 months that will be difficult for competitors to overcome.
Find Out Where Your Brand Stands in AI Search
Is your brand being recommended, vaguely mentioned, or completely absent when buyers ask ChatGPT, Claude, or Gemini? Use our tool to get an initial diagnosis in under 5 minutes.
➤ Free AI Visibility Self-Assessment Tool — Test your brand's exposure across AI platforms right now
➤ Book a Free Results Consultation — Have a consultant walk you through the data and outline industry-specific optimization steps
For more AI visibility case studies and analysis, visit the Joseph Intelligence Case Study Index.
Disclaimer
This article is based on anonymized data from an actual audit engagement. All information that could identify the specific company has been removed. AI platform responses are probabilistic in nature and may vary across different query sessions or time periods. Technical audit results and performance scores represent a snapshot taken at a specific point in time.