Why do AI models recommend competitors instead of your brand?
Most AI visibility tools are just dashboards. VectorGap is a diagnostic engine.
Don't just know if you're mentioned—know why you're not THE answer.
AI Visibility Diagnostics is VectorGap's core analysis engine that answers a fundamental question: why do AI systems like ChatGPT, Claude, Gemini, and Perplexity recommend your competitors instead of your brand? Unlike simple visibility scores that tell you "you're at 67%" without explanation, our diagnostic approach identifies the specific factors holding you back across five weighted dimensions: Technical accessibility (20%), Entity Health (25%), Content Structure (20%), Source Presence (20%), and Consistency (15%). Each dimension is analyzed with specific checks—from llms.txt implementation to Wikidata presence to Reddit mention sentiment—and every failed check comes with actionable fixes including code examples. The result is a clear roadmap showing exactly what to improve and in what order to maximize your chances of being the primary AI recommendation, not just another option in a list.
What problems does AI Visibility Diagnostics solve?
AI doesn't rank—it recommends.
Being "mentioned" isn't enough
When a user asks "best project management tool", they want THE answer—not a list of options. If you're option #4, you might as well be invisible.
Visibility scores are vanity metrics
Knowing your "AI visibility score is 67%" tells you nothing. What exactly is holding you back? Where do you invest to improve?
Without diagnostics, you're flying blind
Is it your technical setup? Your entity presence? Content structure? Without knowing the root cause, every "fix" is a guess.
What are the 5 dimensions of AI visibility?
We diagnose the root cause, not just the symptom.
Technical
Weight: 20%Can AI systems actually read your content?
Entity Health
Weight: 25%Does AI know who you are?
Content Structure
Weight: 20%Is your content optimized for AI understanding?
Source Presence
Weight: 20%Are you mentioned in places AI trusts?
Consistency
Weight: 15%Is your information consistent across sources?
How does VectorGap turn diagnostics into action?
Every diagnostic comes with specific, actionable fixes.
Diagnose
5-dimension analysis identifies exactly what's holding you back. Each check shows "What we found" and "Why it matters."
Recommend
Actionable fixes with code examples for every failed check. Priority-ranked by impact on AI perception.
Execute
One-click "Create Mission" turns any issue into an actionable task. Track progress until resolved.
Example: Technical Dimension Fix
# Issue: Missing llms.txt file for AI crawler guidance
# Create /llms.txt in your web root User-agent: * Sitemap: https://yourbrand.com/sitemap.xml # Preferred content for AI Prefer: /about, /products, /faq Avoid: /admin, /checkout # Contact for AI systems AI-Contact: ai@yourbrand.com
How do you compare your AI visibility against competitors?
Run the same 5-dimension diagnostic on your competitors. See exactly why they get recommended and you don't.
Insight
Your Entity Health score (45%) is significantly lower than Competitor A (88%). This is likely why AI recommends them as "the leading solution" while only mentioning you as an alternative. Fix: Improve Wikidata presence and Wikipedia citations.
What will you learn from an AI visibility diagnostic?
Exactly why AI recommends competitors over you
Which of the 5 dimensions is your weakest link
Specific code examples and fixes for every technical issue
Priority-ranked action items by impact on AI perception
Entity health status: Wikidata, Wikipedia, Knowledge Panel presence
Source presence: Where you're mentioned (Reddit, YouTube, News) and sentiment
Frequently Asked Questions about AI Visibility Diagnostics
AI Visibility Diagnostics is a 5-dimension analysis engine that identifies exactly why AI models recommend competitors over your brand. It examines Technical factors (crawlability, schema markup), Entity Health (Wikipedia, Wikidata presence), Content Structure (FAQ markup, definition patterns), Source Presence (Reddit, YouTube, news mentions), and Consistency (cross-platform accuracy). Each dimension is weighted and scored, with specific actionable recommendations provided for every failed check.
AI recommendation algorithms favor brands with strong entity presence (Wikipedia, Wikidata entries), consistent information across sources, high-quality third-party mentions (Reddit, YouTube, news), properly structured content with schema markup, and technical accessibility for AI crawlers. When AI encounters a query like "best project management tool," it synthesizes information from these sources to determine which brand to recommend first. Brands weak in any dimension often appear as alternatives rather than primary recommendations.
Traditional AI visibility tools show you a score (e.g., "67%") without explaining what's wrong. VectorGap's Diagnostics tells you exactly which dimension is your weakest link and provides specific fixes. For example, instead of just showing a low score, we identify "Your Entity Health score is 45% because you lack a Wikidata entry and Wikipedia citations" with a step-by-step guide to improve. This diagnostic approach transforms vague metrics into actionable improvements.
Yes, VectorGap runs the same 5-dimension diagnostic on your competitors, allowing apples-to-apples comparison. You can see exactly why Competitor A scores 88% on Entity Health while you score 45%, and understand that this gap is why they get recommended as "the leading solution" while you appear as an alternative. Each comparison includes insights explaining the impact of score differences on AI recommendations.
Why is VectorGap the only platform with built-in WHY diagnostics?
Other tools tell you your visibility score. VectorGap tells you exactly what's wrong and how to fix it. Stop guessing. Start diagnosing.
Ready to discover why AI isn't recommending your brand?
Stop asking "Am I mentioned?" Start asking "Why am I not THE answer?"