The AI visibility landscape: Pick44 vs Profound, Scrunch, AthenaHQ, and the SEO incumbents
Your buyers increasingly ask an AI assistant before they ask a salesperson. When someone types “best API monitoring tool” into ChatGPT, Gemini, Claude, or Perplexity, the model returns a short, confident list, and the names it omits never get a second look. Measuring and improving how you show up in those answers is a new category: Generative Engine Optimization (GEO), sometimes called AI visibility or Answer Engine Optimization (AEO).
The category is young and crowded. Here is an honest map of who does what, followed by where Pick44 fits and why we built it the way we did.
The landscape, in three tiers
Most of the market sorts into three groups.
| Tier | Who | Focus |
|---|---|---|
| Enterprise GEO leaders | Profound, Scrunch AI, AthenaHQ, BrightEdge | Broad AI-engine coverage, crawler analytics, real-world prompt datasets, enterprise workflows. |
| Mid-market GEO platforms | Peec AI, Otterly.ai, Ranketta, Promptwatch | Visibility tracking with strong UX at a lower price; often self-serve. |
| SEO incumbents moving in | Semrush, Ahrefs, Conductor | Established SEO suites bolting AI-search and brand-visibility features onto existing platforms. |
Newer entrants show up in comparisons too, including Analyze AI, AIclicks, RankPrompt, PromptMonitor, and Bluefish. Profound is widely treated as the enterprise reference point; its differentiators are broad engine coverage, AI crawler analytics, and a “Prompt Volumes” dataset of real AI queries.
What most tools have in common
Nearly everyone in the category tracks the same core thing: are you mentioned, and how, across AI engines. Where they differ is coverage, price, and whether they connect that visibility data to the work of actually fixing it. The gap we kept running into ourselves was a different one: trust in the number. A visibility score is easy to inflate — pick the best model, count a brand mention generously, show a big percentage with no baseline — and very hard to defend when a customer checks it by hand.
Where Pick44 is different
Pick44 is built around one idea: a measurement you would not be embarrassed to defend line by line.
| Dimension | How most AI-visibility tools do it | How Pick44 does it |
|---|---|---|
| The headline number | A single visibility score, often best-case. | Median rank across models, with a confidence band and token usage shown. Never the best case. |
| Bias control | Queries frequently name your brand. | The model never sees your brand in neutral queries, so you have to earn the mention. |
| Progress | “+200%” with no anchor. | Progress vs a fixed first-run baseline that can go negative, always shown with the absolute score. |
| Failed model calls | Often filled in or hidden. | Labeled “no live answer” and excluded, never faked or counted as zero. |
| Fixes | Mostly monitoring; you write the fixes. | Fix Sprint drafts truthful, publish-ready pages, FAQs, and schema for each losing query. |
| Methodology | Usually a black box. | Published in-app; every score expands to the exact prompts and raw answers so you can re-run them. |
| Getting started | Sales calls and enterprise contracts. | A free report, self-serve, no card. |
We also cover multiple independent engines by default and label every report as live-web grounded or training-recall, so you always know which reality you are reading. The goal is not to be the biggest platform. It is to be the one whose numbers survive a hand-check.
This comparison reflects public positioning as of July 2026, and this space moves fast. If we have a competitor wrong, tell us and we will correct it, same as we do on our public leaderboard. Rank cannot be purchased here, and neither can a favorable comparison.
See it on your own domain
The honest version of this is not an argument, it is a report. Run one for your company.
- Median rank across every major AI model
- A confidence band on every score
- The exact prompts and raw answers, always