Jesse Morquecho
All demos
01AEO / GEO auditLive + cached

Does AI recommend your brand?

When a buyer asks ChatGPT, Gemini, or Claude for the best tool in your category, this scores whether you show up — and which competitor shows up instead.

3 engines
ChatGPT · Gemini · Claude
~13 Qs
across 4 buyer-intent tiers
Live + cached
instant, no login
The output

A visibility score, and the gap that matters

Every mention is a frequency (“named in 2 of 3 runs”), not a yes/no. The reveal is the tier breakdown: strong early, invisible where the purchase is decided. Switch between competitors — they’re scored from one shared run, so share-of-voice is directly comparable.

Audit:
77
/ 100
AI Visibility Score
Amplitude
Product analytics platforms
3 runs × 3 engines · 14 questions
Where you show up, by intent

Weighted so comparison (×2.5) and decision (×3) count for more than problem-aware (×1).

Problem-aware×181%
Solution-aware×1.589%
Vendor comparison×2.567%
Decision×378%
Competitor share of voice

Of every brand mention across all runs, who gets named.

Mixpanel
28%
Amplitude
27%
PostHog
18%
Pendo
12%
Google Analytics
11%
Statsig
2%
The readout
  • ▸Mixpanel owns 28% of the AI conversation in this category; you hold 27%.
  • ▸When you are mentioned, only 32% of the time is it a linked citation — the engines say your name but rarely send a click.
  • ▸Zero mentions on: “Mixpanel vs PostHog for a startup — which should we choose?”
Question-by-question detail (14) ›
QuestionTierChatGPTGeminiClaude
We're flying blind on how users actually move through our product. How do teams figure out what's working?Problem-aware2/33/31/3
Our data team can't keep up with every 'how many users did X' question. What do product teams use to answer these themselves?Problem-aware3/33/33/3
We ship features but have no idea if anyone adopts them. How do teams track feature adoption?Problem-aware3/32/32/3
What's the best product analytics tool?Solution-aware3/33/33/3
What should a growing SaaS look for in a product analytics platform?Solution-aware0/33/32/3
What are the best tools for tracking user funnels and behavior in a web app?Solution-aware3/33/33/3
What do product and growth teams use to analyze retention and conversion?Solution-aware3/33/33/3
Amplitude vs Mixpanel — which is better for product analytics?Vendor comparison3/33/33/3
What are the best alternatives to Google Analytics for product analytics?Vendor comparison3/33/32/3
Mixpanel vs PostHog for a startup — which should we choose?Vendor comparison0/30/30/3
Amplitude vs Pendo — which is better for product analytics and adoption?Vendor comparison3/33/31/3
Best product analytics tool for an early-stage startup on a budgetDecision3/33/33/3
Best product analytics platform for a mid-market B2B SaaSDecision3/33/33/3
Which product analytics tool is best for a team that wants open-source and self-hosting?Decision0/33/30/3
The differentiator

The questions a buyer actually asks

The value isn’t the prompt — it’s knowing buyer intent ladders from “I have a problem” to “X vs Y for my use case,” and weighting the questions where the shortlist forms.

Problem-awareweight ×1

The buyer feels a pain but doesn't know a category of tool exists yet.

  • We're flying blind on how users actually move through our product. How do teams figure out what's working?
  • Our data team can't keep up with every 'how many users did X' question. What do product teams use to answer these themselves?
  • We ship features but have no idea if anyone adopts them. How do teams track feature adoption?
Solution-awareweight ×1.5

They know they need a tool and are asking what kind and which are best.

  • What's the best product analytics tool?
  • What should a growing SaaS look for in a product analytics platform?
  • What are the best tools for tracking user funnels and behavior in a web app?
  • What do product and growth teams use to analyze retention and conversion?
Vendor comparisonweight ×2.5

Named head-to-heads and 'alternatives to X'. Where the shortlist forms.

  • Amplitude vs Mixpanel — which is better for product analytics?
  • What are the best alternatives to Google Analytics for product analytics?
  • Mixpanel vs PostHog for a startup — which should we choose?
  • Amplitude vs Pendo — which is better for product analytics and adoption?
Decisionweight ×3

'Best X for [my exact situation]'. The last question before a purchase.

  • Best product analytics tool for an early-stage startup on a budget
  • Best product analytics platform for a mid-market B2B SaaS
  • Which product analytics tool is best for a team that wants open-source and self-hosting?
Instant + real

Preloaded, or run it live

Preloaded brands are precomputed on a schedule, so a visitor sees a real result instantly. Or trigger a fresh run — rate-limited and cost-capped, keys server-side.

AmplitudeMixpanelPostHogPendoShowit· I ran SEO/AEO for themAsanaMonday.comClickUpNotionAirtableSmartsheet
Run a live check

Rate-limited and cost-capped. One run queries ~12 questions × 2 runs × 3 engines (~$2–3 in API spend). Keys stay server-side.

Why it's smart

The score defends itself in one sentence

“The percent of buyer questions where AI engines name you — across 3 runs each on three engines, weighted so the comparison and decision questions that drive purchases count about three times as much as generic problem questions.” A brand can look fine early and be invisible exactly where the purchase is decided — surfacing that is the whole point.
ChatGPT (OpenAI)

Responses API with the web_search tool — returns cited sources.

Gemini (Google)

Grounding with Google Search — returns grounding metadata and citations.

Claude (Anthropic)

web_search tool — returns citations.

~$0.03
Per engine call
~$4–6
Cached full audit
~$2–3
Capped live run
Server-side
Keys never client
What I'd do with this

A low score isn’t a vanity metric — it’s a lost channel. If the engines introduce my category but recommend a competitor on every comparison question, I’m disqualified from the shortlist before a human sees my site.

I’d work the tier gap first — the specific comparison and “best X for [use case]” questions where I’m absent. Those map to content the engines cite: head-to-head pages, use-case pages, and the third-party listicles and Reddit threads the models pull from. Re-run monthly, watch the comparison tier move — and treat it as a leading indicator, never as revenue.

Limits & honesty

These engines are non-deterministic and personalized. Here's exactly how much to trust a result.

Non-determinism — that's why it's a frequency
The same question doesn't return the same answer twice, so every question is run N times per engine and reported as 'named in 2 of 3 runs.' A single yes/no would be dishonest.
Varies by user, location, and session
AI answers are personalized. This measures a neutral, logged-out baseline — a reasonable reference point, not what every buyer sees.
Citation is not traffic, and not revenue
Being named means you're in the consideration set — not a click, a signup, or a dollar. It's a leading indicator of visibility, never a performance metric.
Coverage is only the engines we query
OpenAI, Gemini, Claude. Google's AI Overviews and consumer ChatGPT have no official API and are not included — the tool never implies coverage it doesn't have.
The question set drives everything
A weak or off-target question set produces a misleading score — which is exactly why the generated questions are shown to you.
A snapshot, not a monitor
Engines change models and grounding constantly, so today's score isn't a promise about next month — which is why it's built to be re-run on a schedule.