I Failed My Own AI Visibility Audit — Here's the Fix

EurekaNav is an AI visibility audit tool for SaaS founders. I built it to help founders find out how ChatGPT, Perplexity, Gemini, DeepSeek, Claude, and Mistral describe their products.

Last week I ran my own tool on my own site. The result: six out of six AI engines had no idea what EurekaNav actually is.

This is that story — what every engine said, why it happened, and the three fixes I shipped the same day that moved my own rule compliance from 58% to 75%.

What six AI engines said about EurekaNav

I asked each engine the same question: "What is EurekaNav?" Here is exactly what I got back:

• ChatGPT → "maritime navigation software"
• Gemini → "maritime navigation"
• Mistral → "maritime navigation"
• Perplexity → "no such product exists"
• Claude → "no information available"
• DeepSeek → "AI research navigator"

Zero out of six called it what it actually is. Three hallucinated a nautical product (because "Eureka" plus "Nav" apparently reads as boat software to a language model). One said I did not exist. Two refused to answer.

I sell a $79 product that audits this exact failure mode on SaaS websites. And my own site failed the audit.

Why this happened

I ran the same 10 GEO compliance rules my tool runs on customer sites, pointed at eurekanav.com. The diagnosis was brutally simple. Three rules were failing outright.

Rule #03 — Schema.org Structured Data: FAIL

There was no JSON-LD on the homepage. No SoftwareApplication. No Organization. No FAQPage. AI engines crawl structured data first because it is the most reliable "what is this product" signal. My homepage gave them nothing.

Rule #08 — FAQ Section: FAIL

There was a FAQ section in the DOM, but it was not wrapped in FAQPage schema. AI engines look at FAQ sections to figure out what questions a product answers. Without the schema, the content was invisible to them as structured Q&A.

Rule #02 — Answer-First Structure: PARTIAL

The hero copy said "See how 6 AI engines describe your SaaS" — that is a call to action, not a definition. Anywhere an AI engine looked for "what is EurekaNav," it found marketing copy, not a factual statement.

Three failures, one consequence: when the engines could not find a clear answer, they guessed. The guess was almost never in my favor.

The three fixes I shipped

Fix 1 — Structured data (JSON-LD)

Injected three schemas into the homepage:

• SoftwareApplication — name "EurekaNav", description "AI visibility audit tool for SaaS founders. Scores how ChatGPT, Perplexity, Gemini, DeepSeek, Claude, and Mistral describe and recommend your product..."
• Organization — contact, logo, sameAs links to Twitter/GitHub
• FAQPage — the site's FAQs wrapped in the schema AI engines read

This single change hit two of the three failing rules. Because structured data is machine-readable and unambiguous, engines that crawl the site next will have a canonical product description to reference.

Fix 2 — Answer-first paragraph under the hero

Added a compact definition block between the hero and the rest of the homepage:

EurekaNav is an AI visibility audit tool for SaaS founders. We score how ChatGPT, Perplexity, Gemini, DeepSeek, Claude, and Mistral describe and recommend your product, then ship a prioritized 10-rule fix list. One URL in, PDF report out in 5 minutes. $79 flat, 30-day refund.

It is 55 words. The first 40 to 60 words of a page are what AI engines extract when they need to answer "what is this product." Research on top-cited pages shows 72% of them have their definition in that window. Mine did not — now it does.

Fix 3 — FAQ with the anchor question

Added a new FAQ at the top of the list:

Q: Which 6 AI engines do you check?
A: ChatGPT (OpenAI), Perplexity, Gemini (Google), DeepSeek, Claude (Anthropic), and Mistral. Every audit queries all six with high-intent buying prompts your ICP actually types, then scores each engine's answer for coverage, accuracy, and competitive framing.

This question exists to give AI engines a structured place to find the specific list of engines EurekaNav covers. When someone asks ChatGPT "what does EurekaNav do," the engine can now lift this answer directly — with the six engine names, which is the single most distinguishing feature of the product.

Before / after

I ran the rule evaluator again immediately after deploy. Three rules flipped:

• Rule #03 Schema.org — FAIL → PASS
• Rule #08 FAQ Section — FAIL → PASS
• Rule #02 Answer-First — PARTIAL (improved, but first paragraph still >60 words; next fix)

Overall rule compliance: 58% → 75% in a single deploy.

The AI engines themselves will not re-crawl for days or weeks, so the 6-engine re-audit will take time to reflect. But the rule-level diagnosis is binary — either the schema exists or it does not, either the FAQ has the right markup or it does not. Those flipped immediately.

The lesson for SaaS founders

If you built your site in the last few years, it was optimized for Google. Maybe for social cards. Almost certainly not for how ChatGPT and Perplexity synthesize answers about your product.

The failure mode has three layers, in order:

1. No schema — AI engines cannot find a machine-readable "what is this product" answer, so they extract whatever prose is on the page. If the prose is a call-to-action ("See how..."), they hallucinate the rest.
2. No answer-first prose — Even when engines extract prose, they look for the first 40 to 60 words. If those words are not a factual definition, the engine's own language model fills the gap with its best guess.
3. No FAQPage schema — FAQ content that is not structured is invisible as Q&A. Engines lift from FAQs a lot; unstructured FAQs lose that lift.

Fix all three and you give AI engines a complete, structured, unambiguous story about your product. Fix none and every engine makes its own story up.

How to check your own site

If this story hit close to home — or you just want to know whether your SaaS has the same three problems — the same tool I ran on my own site is what I sell.

$79 gets you the 6-engine audit, the 10-rule compliance scorecard, and a PDF with prioritized fixes, delivered in 5 minutes. 30-day refund if it does not help you ship a fix.

If the fix list looks like more than you can ship this month, the $499 AI Fix Pack implements the top 3 fixes in 5 working days — before/after re-audit included, full refund if the score does not move 10+ points.

Either way: the audit is the same one that exposed this failure on my own site.

Related reading

• Gartner's 25% search drop prediction — reality check
• AI search engine market share 2026
• ChatGPT vs Google search volume 2026
• HubSpot AEO vs manual audits — 3 patterns
• Linear — AI visibility teardown

Sources & references

Each external claim in this post links to a primary source. Where we cite our own observations, we disclose sample size (currently n=4 published audit teardowns plus broader audit work). For methodology details and our 6-engine scoring approach, see eurekanav.com/methodology.

• Gartner press release, 2024-02-19 — 25% search drop prediction — https://www.gartner.com/en/newsroom/press-releases/2024-02-19-gartner-predicts-search-engine-volume-will-drop-25-percent-by-2026-with-ai-chatbots-and-other-virtual-agents
• Sam Altman (OpenAI CEO) — 1B+ daily ChatGPT messages, 2024-10-31 — https://x.com/sama/status/1851803931188064256
• Google — AI Overviews launch, 2024-05-14 — https://blog.google/products/search/generative-ai-google-search-may-2024/
• Google 2024 Year in Search recap — https://blog.google/products/search/google-trends-2024-year-in-search/
• Asia Frost (HubSpot Senior Director, Global Growth) — HubSpot Grow 2026 talk — https://blog.hubspot.com/marketing

If you spot a claim in this post that you cannot trace to a source above or to our methodology, email don@eurekanav.com — we will provide one or correct the claim within 24 hours.