Case Overview
This case examines an electronic components and connector distribution company serving industrial, telecommunications, and automation systems integrator clients. After completing a full AI visibility audit, we uncovered a striking contradiction: the company's website achieved a perfect 100 on PageSpeed's SEO sub-score, yet received zero mentions across all AI platforms when tested with industry-relevant queries. In other words, years of traditional SEO investment had not translated into visibility in the AI search era. The overall score for this audit came in at 58/100, placing the company at a "moderate" AI visibility potential rating with clear structural room for improvement.
Score Breakdown
The three evaluation dimensions tell a consistent story: AI mention rate is dragging down the overall score, while page performance represents the most urgent technical liability.
| Dimension | Score | Status |
|---|---|---|
| AI Search Visibility | 40 / 100 | ⚠️ Needs Improvement |
| GEO Technical Audit | 73 / 100 | 🔶 Moderate |
| Website Performance (PageSpeed) | 67 / 100 | 🔶 Moderate |
The biggest bottleneck is the AI visibility score of just 40. The brand received vague mentions on some platforms during direct brand queries, but was completely absent from every industry-scenario query across all platforms. When a procurement professional asks an AI, "Which connector distributors in Taiwan would you recommend?", this company simply does not appear in the answer.
AI Search Visibility Testing
We submitted queries to three major AI platforms — Claude, ChatGPT, and Gemini — running both direct brand queries and industry-scenario queries on each. A total of 12 tests were completed. Here are the full results.
Claude
For the direct brand query, Claude returned a vague mention — the AI recognized the company name but could not provide specifics about product lines, service scope, or differentiators. The response was surface-level at best. For the industry-scenario query (e.g., "Recommend electronic component distributors in Taiwan"), the company was completely absent. This suggests that Claude's training data contains insufficient structured information about the company, leaving the AI unable to build a complete brand knowledge profile.
ChatGPT
ChatGPT performed relatively better on the direct brand query, returning a positive mention — correctly identifying the company as an electronic components distributor with a favorable tone. However, the industry-scenario query again produced no mention. This result indicates that the brand has some baseline presence in AI training data, but has not established a strong enough "industry expert" positioning. When answering procurement decision queries, ChatGPT does not proactively cite this company.
Gemini
Gemini's results closely mirrored ChatGPT's: a positive mention for the brand query, and complete absence in the industry-scenario query. Gemini's recommendations for electronic component distributors tend to favor companies with rich structured product data, detailed technical documentation, and specification comparison content — precisely the content gap this company currently faces.
Across all 12 queries, 6 resulted in a mention and 6 did not, yielding a 50% overall mention rate. The critical issue: every single mention came from direct brand queries. The industry-scenario mention rate was 0%. This means that when a potential buyer doesn't yet know the company's name and is actively searching for a supplier, AI offers zero assistance in getting this company discovered.
Competitive Landscape Analysis
During industry-scenario testing, the AI platforms did provide answers recommending connector and electronic component distributors — they just didn't include this company. As many as 8 competitors were recommended by AI platforms in industry queries, while the company received zero recommendations in those same contexts.
The distributors that AI tends to recommend share common characteristics: their websites offer searchable product part number databases, they publish technical selection guides or application notes, their technical content is cited in industry forums and trade media, and they have clear Schema structured data markup in place. These elements give AI sufficient "evidence" to confidently include them in a recommendation list. This gap represents the most direct and actionable competitive disadvantage identified in this audit.
GEO Technical Audit
The technical foundation is generally solid, with most key items passing — but one notable gap cannot be overlooked.
| Technical Item | Status |
|---|---|
| Schema JSON-LD Structured Data | ✓ Configured |
| XML Sitemap | ✓ Configured |
| Meta Description | ✓ Configured |
| OG Tags (Social Preview) | ✓ Configured |
| Canonical URL | ✗ Not Configured |
| HTTP/2 Protocol | ✓ Enabled |
| Title Tag | ✓ Configured |
| H1 Tag | ✓ Configured |
The technical pass rate is 7 out of 8 items (87.5%), indicating a solid foundation. However, the missing Canonical URL configuration is a hidden risk: when product pages exist in multiple URL variants — such as filter pages with query parameters or paginated URLs — AI crawlers and search engines may split crawl authority across duplicates, potentially indexing low-quality duplicate pages. For a distributor website with a large number of SKUs, this problem compounds as the product catalog grows. It is also worth noting that while Schema markup is in place, it has not been extended to cover e-commerce-specific types such as Product and Offer — a critical gap for AI recognition of product-level information.
Website Performance
Performance is where the starkest contrast in this audit emerges: the SEO sub-score is a perfect 100, while the Performance sub-score sits at just 33 — both from the same PageSpeed Insights report, yet worlds apart.
A performance score of 33 signals genuinely slow page load times. Common causes include uncompressed large images, render-blocking JavaScript files, and the absence of lazy loading. The direct impact on AI visibility is significant: generative AI crawlers operate under time constraints when fetching content, and pages that load too slowly risk being skipped or only partially indexed. The composite PageSpeed score of 67/100 is average, but the 33-point performance sub-score is the most urgent technical issue to resolve — and also carries the highest remediation upside. Industry experience shows that image optimization and caching improvements alone can typically push performance scores above 60 within a short timeframe.
Expert Recommendations
The following three priorities represent the highest-leverage improvements identified in this audit. Each is tied directly to a specific diagnostic finding.
Priority 1: Fix the Performance Bottleneck — It's the First Gate for AI Crawlers
A performance score of 33/100 is the most urgent technical issue on the table. When AI crawlers and search engine bots decide how deeply to index a site, page response speed is a primary signal. This means that even if the website's content quality is excellent, slow load times may cause AI systems to reduce crawl frequency. The root causes need to be diagnosed across three areas: image asset management, front-end resource loading order, and server-side caching strategy.
Priority 2: Structured Data Depth Is Insufficient for AI Recognition
Schema markup exists, but for a distributor managing a diverse SKU catalog, AI needs machine-readable product specifications, part numbers, represented brands, and application context. The current markup depth tells AI "this is a company" — but not "this company distributes these specific products for these specific applications." The latter is exactly what AI references when answering procurement queries. Closing this gap is essential for improving AI visibility in product-level searches.
Priority 3: Content Strategy Lacks Decision-Support Assets
When AI answers procurement comparison questions, it relies heavily on the existence of concrete comparison content, selection guides, and application case studies online. The company's current content architecture is primarily a product catalog. It lacks the material needed to answer decision-driven queries like "Which connector type should I use for this application?" or "How do distributor service models differ?" The absence of this content is the direct cause of the 0% mention rate in all industry-scenario queries.
AI Search Trends in the Electronic Components Distribution Industry
Electronic component procurement is undergoing a quiet but profound shift in search behavior. Traditionally, engineers and procurement managers sourcing connectors would use Google to search part numbers, browse distributor catalog sites, or rely on sales relationships and trade show contacts to build a vendor shortlist. Since 2024, however, a growing number of electronics buyers have started turning directly to ChatGPT or Gemini with queries like: "Which Taiwan-authorized distributors carry RJ45 connectors?", "Recommended suppliers for IP67-rated industrial M12 connectors", or "Which connector distributor has the fastest sample fulfillment process for board-to-board connectors?"
What distinguishes these queries is that they are not searching for a part number — they are searching for a supply chain decision. The buyer describes an application scenario and set of requirements in natural language, then expects AI to return a trusted shortlist of suppliers. This process entirely bypasses traditional keyword search, which means SEO rankings are increasingly being supplemented — and in some cases partially replaced — by AI recommendation eligibility.
For connector distributors specifically, the AI search opportunity window is especially significant for three reasons. First, electronic component selection is highly technical: buyers need specification comparisons, compatibility confirmation, and alternative part suggestions — exactly the type of information AI is best at synthesizing, and exactly the kind of content that enables well-prepared distributors to be cited. Second, supply chain uncertainty is pushing procurement teams to actively identify backup suppliers, making AI a rapid-access entry point for scanning market options. Third, connector distributors differentiate through specific brand authorizations, inventory depth, and technical support capabilities — all of which, if presented in a structured format on the company's website, can be directly referenced by AI when answering queries like "Which distributors have stock and offer technical support?"
In the Taiwan connector distribution market, several players have already begun deliberately investing in GEO (Generative Engine Optimization). Companies that establish AI-friendly content first will build an AI visibility advantage over the next 12 to 18 months that will be difficult for late movers to close. This window will not stay open indefinitely.
Want to Know How Your Company Performs in AI Search?
Every electronic components distributor has a different AI visibility profile. The gap between brand query results and industry-scenario query results often reveals the most critical content and technical deficiencies.
You can start understanding your current position in two ways:
- Use our free AI Visibility Check tool to get a quick read on your website's foundational technical status.
- Or schedule a free results consultation to have a strategist walk through the findings in the context of your specific industry and competitive environment.
For more industry case studies, visit the Joseph Intelligence Case Study Index to explore AI visibility audit results across different company sizes and sectors.
Disclaimer
This article is based on anonymized data from an actual AI visibility audit. All information that could identify the company has been removed. AI platform responses are non-deterministic; results may vary across different query sessions and time periods. Technical audit results and performance scores represent a snapshot at a specific point in time.