10 Best AI Visibility Trackers (2026 Guide)

An AI visibility tracker monitors whether AI engines name your brand when users ask relevant questions, and how you stack up against competitors. Before comparing specific tools, it helps to know what separates a genuinely useful tracker from a dashboard that just looks busy.

The essentials to check:

• Engine coverage. Does it track the engines your buyers actually use - ChatGPT, Perplexity, Gemini, Google AI Overviews, and Claude? A tracker that only watches one engine gives you a partial picture.
• Share of model measurement. Can it calculate your share of model - the percentage of relevant answers that name you - and trend it over time?
• Competitor benchmarking. Can you see who is being named instead of you, and by how much?
• Citation-level detail. Does it show the sources the engine cited, so you know why an answer named a competitor?
• Measure versus improve. This is the big one. Most trackers only tell you where you stand. A few also help you close the gap with off-page work.

Outrigger founder Joel House, author of AI for Revenue, is blunt about his own category: "Most AI visibility tools are thermometers - they tell you the temperature and stop there. That's useful for a week. Then you're staring at a number you don't know how to change. The tools worth paying for either move the number themselves or plug directly into the work that does. Measurement without a path to improvement is just anxiety with a dashboard."

With that lens, here is the honest roundup. Descriptions of competing tools reflect their public positioning, and pricing shifts constantly in this category, so this focuses on capability and fit rather than quoting numbers that go stale.

1. Outrigger. Built inside a working agency to solve a real client problem, Outrigger tracks visibility across five AI engines (ChatGPT, Perplexity, Gemini, Google AI Overviews, and Claude) and pairs measurement with the off-page work that improves it - citation seeding in high-authority threads, entity and directory consistency, review building, and digital PR. It is the option for teams who want to both see their share of model and do something about it without stitching five tools together. Its research pedigree - the Outrigger Visibility Index study of 1,004 businesses - informs how it scores and prioritizes. Best for agencies and businesses that want tracking plus execution. See the full feature set.

2. Profound. A polished, enterprise-leaning analytics platform focused on deep AI-answer measurement and conversation-level insight. Strong dashboards and data depth; priced and positioned for larger teams. Best for enterprise analytics buyers. Compare directly on the Outrigger vs Profound page.

3. Otterly. A lean, fast share-of-model monitor that makes it easy to track prompts and see when you appear across engines. Light and approachable rather than deep. Best for small teams that want quick visibility monitoring without complexity. See the Outrigger vs Otterly comparison.

4. Peec AI. An analytics-forward tracker with a focus on clean reporting and competitor benchmarking across AI engines. Popular with marketing teams that want a straightforward monitoring layer. Best for data-focused in-house teams.

5. Athena HQ. A focused AI-visibility monitoring tool built for speed and clarity, aimed at teams that want to watch their brand across the major engines without heavy setup. Best for lean teams prioritizing simplicity.

6. Daydream. An AI-search visibility platform with a content-and-analytics bent, geared toward teams thinking about AI search as a content channel. Best for content-led organizations. See how it stacks up on the Outrigger vs Daydream page.

7. Semrush AI toolkit. If you already live in Semrush, its AI visibility features let you fold AI tracking into a suite you know. The advantage is consolidation, not depth - it is an add-on to a broad SEO platform rather than a purpose-built AI tracker. Best for existing Semrush customers who want everything in one login.

8. Goodie. A newer entrant in the AI-visibility monitoring space, worth a look for teams evaluating the field. Best for early adopters comparing options. See the Outrigger vs Goodie comparison.

9. Purpose-built monitor scripts. Some technical teams roll their own by querying engine APIs on a schedule and logging mentions. This gives full control and no license cost, but you build and maintain everything - prompt sets, parsing, benchmarking, and dashboards. Best for engineering-heavy teams with time to invest.

10. Manual spot-checking. The zero-cost baseline: periodically ask the engines your key questions and record the results in a spreadsheet. It doesn't scale and it's easy to let slip, but it beats flying blind and is a reasonable way to prove the problem before buying a tool.

For a deeper feature-by-feature look across the category, the AI visibility tools compared guide goes further than this roundup.

Here is the honest at-a-glance view. Engine coverage reflects the tools' primary focus; "best for" reflects who each fits most naturally.

The single most useful column is the last one. Nearly every tool here can tell you your share of model. The real differentiator is what happens next. Most stop at the number. Outrigger is built around the premise that measurement is step one of a longer job - which is why it bundles the citation, entity, review, and PR work that actually moves visibility. If all you need is a thermometer, several of these are fine. If you need the number to go up, weight your choice accordingly.

The right pick comes down to three honest questions about your situation.

1. Do you only need to measure, or do you need to improve? If you have an in-house team ready to act on the data, a pure tracker like Otterly, Peec AI, or Athena HQ may be enough. If you want the measurement and the execution in one place, Outrigger is built for that. This is the decision that matters most.
2. How many engines do your buyers use? If your audience lives in ChatGPT and Perplexity, broad coverage matters less. If they span AI Overviews, Gemini, and Claude too, prioritize a tracker that covers all five so you aren't blind on the engines that count.
3. What's your team and stack? Existing Semrush users may value consolidation. Enterprises with dedicated analysts may want Profound's depth. Lean teams may want the simplest possible monitor. Engineering-heavy teams might build their own.

Pick the tool that matches the job you actually have. If the job is "tell the client where we stand each month," a clean monitor is fine. If the job is "get this brand cited by AI," you need the tracking wired to the work - otherwise you spend six months watching a flat line and wondering why measurement didn't fix anything.

Whatever you choose, start by establishing your baseline. The urgency is real: the Outrigger Visibility Index found 65.9% of businesses are effectively invisible in AI search, so most brands discover they are starting from near zero. A free AI visibility audit shows exactly how the major AI engines describe your brand today, where competitors out-cite you, and which gaps to close first - a useful benchmark no matter which tracker you end up adopting.

Trials look impressive in the first ten minutes, so it pays to know what to scrutinize before you commit budget. A few warning signs separate a tool that will genuinely help from one that just charts numbers.

• Single-engine coverage dressed up as complete. Some tools track only ChatGPT and imply that is your whole AI visibility. As covered above, your visibility varies by engine - a tracker blind to Perplexity, AI Overviews, Gemini, or Claude can hide the exact place a competitor is beating you.
• A blended score with no breakdown. One tidy "AI visibility score" is easy to show a client, but if you can't decompose it by engine, by question, and by competitor, you can't act on it. Insist on being able to drill down.
• No citation-level detail. If the tool tells you a competitor was named but not which sources the engine cited, you're missing the why. The cited sources are the roadmap for what to fix.
• Measurement with no path to improvement. This is the biggest one. A tracker that leaves you staring at a flat line for months, with no connection to the entity, mention, review, and content work that moves it, is an expense that never pays back. Ask directly: once I see the gap, what does your tool help me do about it?
• Opaque methodology. If a vendor can't explain how they build their prompt sets or detect mentions, you can't trust the trend they report.

The question worth asking every vendor is simple: after three months of paying you, what is different besides the fact that I now know the number? If the honest answer is "nothing, that's your job," you are buying a thermometer - and you should price it like one. For a broader feature comparison of the category, the AI visibility tools compared guide breaks down the field in more detail.