Profound vs Otterly vs AEOTrack: Choosing an AI Visibility Tracker

If you are searching "Profound vs Otterly", you are choosing an AI visibility tracker — and the useful comparison is not a feature grid, it is which tool can tell you why an AI answer picked someone else. This page lays out how the category differs, the five questions that separate the tools, and a test you can run on Profound, Otterly AI and AEOTrack in a single afternoon.

Three tools, one category

All three products do the same three things at their core: send prompts to AI answer engines, store the answers, and count how often a brand shows up. Broadly, Profound positions itself toward enterprise answer-engine analytics, Otterly AI toward AI search monitoring for marketing teams, and AEOTrack toward diagnosis you can verify — every number traces back to a stored answer you can read. Where tools genuinely differ is what happens after the counting, and that is what the rest of this page is about.

Question 1: does it separate being named from being cited?

An engine can use your page without crediting you. Being named means the answer text says your brand; being cited means one of your URLs was retrieved as a source. They fail for opposite reasons and need opposite fixes. Across 446 answers we tracked for a single domain, 68 cited the domain as a source but only 38 named the brand — and on one engine, 22 answers cited the site while none named it. A tool that reports one blended "visibility" number cannot show you that split.

Question 2: does it show what the engine read instead of you?

In the same dataset, engines consulted an average of 7.7 other sources alongside the tracked domain. Whoever those sources recommend is who the answer recommends. A tracker that lists the exact domains behind each answer — how often each was retrieved, and whether each names you or a rival — turns "we are not showing up" into a list of specific pages to earn a mention on. AEOTrack stores that source list for every answer; when you evaluate any tool, open one stored answer and check whether you can see it.

Question 3: where does its crawler data come from?

Most AI crawlers fetch raw HTML and never execute JavaScript, so a browser beacon literally cannot see them. Ask each vendor whether crawler analytics come from server logs or from a script tag. AEOTrack ingests logs directly from Vercel and Cloudflare log drains, with a beacon as the acknowledged smaller half of the picture.

Question 4: is the scoring honest about uncertainty?

AI answers are non-deterministic; the same prompt asked twice can name different brands. Any score computed from sampled answers therefore carries sampling error. AEOTrack publishes a confidence interval and sample count with every score, versions its scoring method so history is never silently rewritten, and reports a separate non-branded score — because prompts containing your own brand name inflate every visibility number, including ours.

Question 5: what does it cost to find out you chose wrong?

Enterprise contracts are annual; monitoring tools are monthly. AEOTrack's plans start at $29/month, every plan begins with a 14-day free trial, no card required — and there is a free instant audit that runs on any domain without an account. Whatever you choose, prefer a trial you can cancel over a demo you have to book.

The afternoon test — run it on all three

• Choose five prompts a real buyer would type, at least three without your brand name in them.
• Run each prompt in the tool and in the engine directly. The stored answer should match what the engine actually says.
• Take one answer where you appear and ask: were we named, were we cited, and which other domains were read?
• Ask whether crawler figures come from server logs or JavaScript.
• Leave the site unchanged for a week and see whether the score moves more than its stated interval.

Where AEOTrack fits

AEOTrack suits teams who want the diagnosis rather than the headline: five engines (ChatGPT, Gemini, Claude, Perplexity, Grok) queried live, naming separated from citation, the full source list behind each answer, log-based crawler analytics, and an improvement plan that ties each recommendation back to the check that produced it. It is not the right tool if you want a single number for a slide — the design assumes you intend to act on the detail underneath.

For the head-to-head pages, see AEOTrack vs Profound and AEOTrack vs Otterly AI; for the wider market, the buyer's guide to AEO tracking tools.