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案例分析

【AI Visibility Case Study】Strong SEO, Nearly Invisible to AI — 39

Published on March 24, 2026

Case Overview

This case examines a Taiwan-based office chair manufacturer and seating solutions provider. When we completed the company's AI visibility audit, we uncovered a striking contradiction: the company's SEO technical score came in at an impressive 83 out of 100, reflecting years of dedicated investment in traditional search optimization. Yet its PageSpeed performance score sat at just 7, and its overall AI visibility composite score landed at only 39/100 — placing it firmly in the "underdeveloped" tier.

This case perfectly illustrates one of the most common blind spots in modern B2B marketing: ranking well on Google does not mean getting recommended by AI. As procurement decision-makers increasingly turn to platforms like ChatGPT, Claude, and Gemini to find suppliers, AI visibility has become a critical new battleground that no B2B company can afford to ignore.

Score Breakdown

The three-dimensional scoring reveals a clear structural gap, with website performance standing out as the single biggest drag on overall results.

Evaluation DimensionScoreStatus
AI Visibility (Brand Mention Rate)34 / 100⚠️ Needs Improvement
GEO Technical Audit40 / 100⚠️ Needs Improvement
Website Performance (PageSpeed)45 / 100⚠️ Needs Improvement
Composite Score39 / 100🔴 Underdeveloped

The most critical bottleneck is the compounding effect of multiple technical deficiencies. A performance score of 7 means that every visit from an AI crawler hits a severe loading barrier. At the same time, missing structured markup prevents AI systems from accurately understanding the site's content. These two problems work in tandem to systematically suppress the likelihood of the brand being cited in AI-generated answers.

AI Search Visibility Test Results

We submitted queries to four major AI platforms, running 16 industry-relevant searches across categories including "office chair recommendations," "ergonomic chair brands," and "Taiwan office furniture manufacturers." Here is a summary of what each platform returned.

Claude

Across 4 queries, Claude returned 1 positive mention, 1 ambiguous mention, and 2 no-mentions — a mention rate of 50%. The positive mention appeared in a broader query about Taiwan-based office chair manufacturers, where Claude included the company in a list of suppliers worth considering. However, when queries focused on consumer-facing questions such as how to choose an ergonomic chair, the brand disappeared entirely from the response. The ambiguous mention suggested that Claude had limited clarity on the company's product positioning and area of expertise.

ChatGPT

ChatGPT returned 2 positive mentions out of 4 queries, making it the most consistent performer among the four platforms tested. Mentions were concentrated in procurement-oriented queries, where ChatGPT correctly identified the company as a manufacturer. However, descriptions of its product line were thin and lacked the depth needed to support a more confident or detailed recommendation — a direct reflection of limited content assets on the company's website.

Gemini

Gemini also delivered 2 positive mentions, on par with ChatGPT. Notably, Gemini's mentions were driven primarily by queries where it could index content directly from the company's website. This highlights a fragility in the company's current AI visibility: these "passive mentions" are unstable and likely to disappear as AI models are updated or if the site's crawlability issues worsen.

Perplexity

Perplexity returned zero mentions across all 4 queries — a 0% mention rate. This result deserves particular attention. Perplexity's core mechanism relies on real-time web search, making it one of the most responsive platforms to a site's actual crawlable content. When even this platform cannot find and cite a brand, it signals fundamental problems in site crawlability and content structure. The PageSpeed score of 7 is almost certainly a direct contributing factor.

In total, the company received 6 mentions across 16 queries, for an overall mention rate of 37.5%. More telling, however, is where the gaps appear: when buyers ask decision-stage questions like "how do I choose an office chair for my team" or "best seating solutions for a small business office," the brand is almost entirely absent from AI-generated answers.

Competitive Landscape

In AI platform recommendations for office chairs and seating solutions, several brands have already established a strong AI visibility advantage. International names like Herman Miller, Steelcase, and Humanscale appear in ergonomic chair recommendations with near-certainty. In the Taiwan and Asia-Pacific market, brands such as Ergotune and Ergoking are regularly cited.

What these competitors share is a robust library of well-structured buying guides, product comparison articles, and user review content — exactly the kind of material that gives AI systems something to cite. The company in this case study has a clear content gap by comparison, and this is one of the core reasons its AI recommendation frequency remains low. Without closing that gap, the ceiling on AI visibility improvement will remain low regardless of other optimizations made.

GEO Technical Audit

The GEO (Generative Engine Optimization) technical audit evaluated 9 key items. The company passed 5 out of 9, or roughly 56%, leaving significant room for improvement.

Technical ItemStatus
Schema JSON-LD Structured Markup✓ Configured
XML Sitemap✓ Configured
Title Tags✓ Configured
Meta Descriptions✗ Missing
OG Tags (Social Sharing Markup)✗ Missing
Canonical URLs✗ Missing
HTTP/2 Protocol✗ Not Enabled
H1 Heading Tags✗ Missing
Naked Domain 301 Redirect✗ Not Configured

The three missing items most worth prioritizing are: missing H1 tags, which prevent AI crawlers from quickly identifying the core topic of each page; missing OG Tags, which means the brand loses control of how its content appears when shared on social platforms, indirectly reducing citation opportunities; and missing Canonical URLs, which can cause duplicate indexing issues that dilute brand authority signals. Additionally, if the naked domain redirect issue is not resolved, some AI crawlers may be unable to fully access the company's website content.

Website Performance

Website performance is the most urgent issue uncovered in this audit. The company's website scored just 7 out of 100 on PageSpeed — a score that signals severely slow page load times, creating a poor experience for both human visitors and AI crawlers alike.

The contrast is striking: an SEO technical score of 83 shows that the company's website has a reasonable level of keyword tagging and foundational SEO configuration. But a performance score of 7 means those SEO efforts are being heavily offset by speed problems. AI platform crawlers, when indexing a site, will frequently skip or reduce crawl frequency for pages that time out or respond slowly. This directly explains why real-time search AI platforms like Perplexity returned zero mentions of the brand.

Common performance improvement paths include image compression and format conversion to modern formats like WebP, enabling browser caching, lazy-loading non-critical resources, and activating HTTP/2 (currently disabled) to accelerate resource delivery.

Expert Recommendations

Based on the audit data, we identified three high-leverage improvement priorities.

Recommendation 1: Treat Performance as a Foundation, Not a Feature

A PageSpeed score of 7 is not simply a "poor user experience" problem — it is a crack in the foundation of the entire AI visibility strategy. When AI crawlers consistently encounter slow load times on every visit, the probability of the brand being cited drops systematically, regardless of how well other technical elements are configured. This must be fixed before any other optimization work is prioritized; otherwise, improvements to content and structured markup will deliver diminished returns.

Recommendation 2: Close the Content Asset Gap Strategically

AI platforms currently have very limited content from this company to draw on when recommending office chair brands. Competitors have already claimed the role of "answer provider" in AI training data and live indexes through long-form content like office chair buying guides, ergonomic vs. standard chair comparisons, and small business seating budget planning articles. Closing this content gap requires a systematic content architecture plan — it cannot be addressed by publishing a few articles in the short term.

Recommendation 3: Strengthen Brand Identity Through Structured Markup

While Schema JSON-LD is already in place, the absence of H1 tags, OG Tags, and Canonical URLs creates ambiguity around the brand's identity in the eyes of AI systems. When AI platforms evaluate whether to cite a brand, they look for consistency across multiple signals. Incomplete foundational markup undermines the brand's perceived credibility and professional image in AI-generated contexts. Filling these gaps is a necessary condition for improving the consistency and frequency of AI recommendations.

AI Search Trends in the Office Chair and Seating Solutions Industry

The way procurement decisions get made in the office chair and seating solutions industry is undergoing a quiet revolution. Traditionally, B2B purchasing in this space relied heavily on sales visits, trade show floor experiences, and word-of-mouth referrals. Since 2023, however, corporate buyers have begun using AI assistants extensively for initial supplier screening. A procurement manager tasked with sourcing 50 chairs for a new office is now quite likely to open ChatGPT first and ask for recommendations on ergonomic office chair brands before building a comparison shortlist.

This behavioral shift carries profound implications for manufacturers. In the past, participation in trade shows and channel partner relationships were sufficient to maintain brand visibility. In the AI search era, if a brand is absent from the AI's knowledge base, it risks being eliminated from consideration at the very first step of the procurement process — before a buyer has even visited the company's website.

The growing interest in workplace health is also generating new search demand worth capturing. As companies become more focused on employee wellness, HR departments are actively searching for answers to questions like "how do I choose ergonomic seating for my team" and "how should we budget for office chair upgrades." The brands that show up in AI answers to these questions are almost exclusively those that have invested consistently in content marketing. In the current Taiwan office chair market, the number of local brands that appear reliably in AI search results is very small — representing both a threat and a significant opportunity.

For manufacturers with strong production capabilities but limited brand awareness, this is a critical window to build AI visibility before the competition catches up. While international brands hold a commanding lead in general brand recognition, local manufacturers have a genuine opportunity to establish AI citation advantages in localized queries — searches for Taiwan-based office chair manufacturers, custom enterprise seating solutions, or locally made ergonomic chairs. This window will not stay open indefinitely. As more competitors recognize the importance of AI visibility and begin investing systematically, the advantage of early movers will become increasingly difficult to replicate.

For more AI visibility insights across manufacturing and B2B industries, explore our case study article index, where we publish real audit data and optimization strategies across multiple sectors.

How Does Your Brand Appear in AI Search?

The challenge this office chair company faces — solid SEO performance paired with critically low AI visibility — is extremely common among small and mid-sized B2B businesses. Traditional website optimization strategies are no longer sufficient on their own to meet the demands of the AI search era.

There are two ways to quickly assess where your brand stands:

  • 🔍 Free AI Visibility Self-Audit Tool — Get a preliminary assessment report in 5 minutes and understand your brand's baseline visibility across major AI platforms.
  • 📅 Book a Free Results Consultation — Speak one-on-one with one of our GEO strategists to walk through your audit data and get concrete, industry-specific recommendations.

Disclaimer

This article is based on anonymized real audit data. All information that could identify the specific company has been removed. AI platform responses are inherently variable — queries submitted at different times may return different results. Technical audit scores and performance metrics represent a point-in-time snapshot.

FAQ

Why would a company with a high SEO score still have poor AI visibility?
SEO and AI visibility measure different things. Traditional SEO optimizes for how search engine crawlers index and rank pages based on keywords and backlinks. AI visibility measures how often and how accurately AI platforms like ChatGPT, Claude, and Gemini reference your brand in their generated answers. A company can rank well on Google while being nearly absent from AI recommendations — especially if it lacks structured content, has slow page load speeds, or has missing technical markup like H1 tags and Canonical URLs that help AI systems understand and trust the site.
How does website speed affect AI visibility and brand mentions?
Website speed has a direct impact on AI visibility because AI crawlers — particularly those used by real-time platforms like Perplexity — will skip or reduce crawl frequency for pages that load slowly or time out. A PageSpeed score as low as 7/100 means AI systems frequently cannot access the site's content at all, which drastically reduces the chance of the brand appearing in AI-generated recommendations. Improving load speed is one of the highest-leverage actions a company can take to improve its AI visibility.
Which AI platforms are most important for B2B companies to appear in?
For B2B companies, the four most important AI platforms to monitor are ChatGPT, Claude, Gemini, and Perplexity. Each has a different user base and content indexing mechanism. ChatGPT and Claude are widely used for general business and procurement research. Gemini integrates with Google's search ecosystem. Perplexity relies heavily on real-time web indexing, making it particularly sensitive to a site's crawlability. Achieving consistent AI visibility across all four platforms requires both strong technical foundations and well-structured content assets.
What is GEO and how is it different from traditional SEO?
GEO stands for Generative Engine Optimization. While traditional SEO focuses on improving a website's ranking in search engine results pages, GEO focuses on making a brand's content understandable, credible, and citable by AI language models. This includes implementing structured markup like Schema JSON-LD, ensuring H1 tags and Meta Descriptions are in place, maintaining Canonical URLs to avoid duplicate content issues, and creating the kind of authoritative long-form content that AI systems draw on when generating answers. GEO does not replace SEO — it extends it for the AI search era.
How can an office chair or furniture manufacturer improve its AI search presence?
Office chair and furniture manufacturers can improve AI visibility through three main strategies. First, fix technical foundations: ensure fast page load speeds, implement H1 tags, Meta Descriptions, OG Tags, Canonical URLs, and enable HTTP/2. Second, build content assets that directly answer buyer questions — buying guides, ergonomic chair comparisons, budget planning articles, and use-case scenarios are the types of content AI platforms cite most frequently. Third, strengthen brand signals by maintaining consistent structured markup and earning mentions from credible industry sources. Manufacturers with strong production capabilities but limited online content have a significant opportunity to gain AI visibility before their competitors invest in this area.
How long does it take to improve AI visibility scores after making technical and content changes?
AI visibility improvement timelines vary depending on the platform and the type of change made. Technical fixes such as improving PageSpeed, adding H1 tags, and implementing Canonical URLs can begin positively affecting AI crawlability within weeks. Content-driven improvements — such as publishing structured buying guides or comparison articles — typically take one to three months to be indexed and factored into AI recommendations. For real-time platforms like Perplexity, improvements can be reflected relatively quickly once the site becomes properly crawlable. Building consistent AI visibility across all major platforms generally requires a sustained effort over three to six months.

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