Manually monitoring your visibility across five AI engines every month takes hours of repetitive work and produces data that is hard to compare over time. There are tools that do it automatically, but they all promise the same things and prices vary enormously. I tested Peec, Otterly and Profound for three months on the same client — and the results were not what I expected. Knowing which one works for your specific case saves you months of wrong subscriptions and gives you immediate continuity of data on which to make real decisions.
Peec, Otterly and Profound: three tools that promise to measure your AI visibility. I tested all three for 3 months on the same client — a corporate events agency in Mestre — and here is what I found.
The client is an interesting case: 12 years on the market, a client list including chemical and pharmaceutical multinationals, a well-maintained website, an active blog. When I started asking ChatGPT and Perplexity “best corporate events agencies in Veneto”, their name never came up. Not a single citation, not a single link, not a single attribution. The ideal starting point to understand whether these three tools really do what they claim to do.
What an AI visibility tracking tool does (and what it does not do)
An AI visibility tracking tool automates the monitoring of your brand mentions across AI answer engines: ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews. What you would do by hand — open each engine, run 30 queries, count how many times you appear, save the screenshots — the tool does in batch, every day, and returns it to you in a dashboard.
It is not a tool that makes you appear in AI answers. It is a tool that tells you whether you appear, where you appear, how often compared to competitors. The difference matters: without a strategy of authority, citations, content structure and presence in the knowledge graph, the tool will return the same zero to you every day. I will come back to this shortly.
What I am sharing here is a deduction from field testing. Translated: take the numbers as indicative, not as absolute truth.
Why monitoring sits downstream of all the previous work
If you have followed the previous nodes in this series, you know that visibility in AI answers stems from a precise set of factors: how the model tokenizes you, how much you are recognized as an entity, how much authority you hold for the model, how well structured your content is.
Monitoring comes afterward. You measure what you have built upstream. If you have built nothing — no work on brand name tokenization, no author entity recognition, no entry into the Google Knowledge Graph — the tool will not work miracles. It will only tell you, with great precision, that you appear nowhere.
That said, once the upstream work has been done (or is underway), these tools give you something almost impossible to obtain by hand: continuity of data and systematic comparison with competitors.
Tracking “best X consultant in Bologna” where X is you is pointless: nobody searches that way.
The test: 3 months, 1 client, 50 queries, 4 engines
Test setup on a corporate events agency in Mestre, January-March 2026:
- 50 queries relevant to the sector: “corporate events agency Veneto”, “pharmaceutical convention organization Italy”, “corporate team building Northeast”, and variants
- 4 engines monitored: ChatGPT, Perplexity, Gemini, Google AI Overviews
- 6 direct competitors tracked in parallel
- All three tools active on the same set of queries
Stated limitation: one single client, one single sector, one single period. It is an indicative test, not a study. A rigorous analysis would require professional tools on a sample of at least 30-50 brands across different sectors. What follows is what I saw, on this case, with these tools.
Track the queries your target client runs before they know you exist.
Peec AI: the most European, the most orderly
Peec AI was born in Germany and is the tool I found most straightforward for those starting from scratch. Setup in 20 minutes, a clean dashboard, a focus on brand mentions and share of voice against the competitors you specify yourself.
On the Mestre client, Peec intercepted 4 mentions in 3 months (out of 50 queries x 90 days x 4 engines = 18,000 total checks). All 4 on Perplexity, none on ChatGPT or Gemini. Over the same period, the client’s market leader appeared 47 times. A ratio of 1 to 12.
Peec’s value is precisely this number: the ratio. Without the tool you would have run 5-10 queries by hand, might never have stumbled upon a mention, and would have concluded “nothing works”. With 18,000 automatic checks you know that 4 mentions exist, on Perplexity, and that your gap with the leader is 12x.
Otterly.AI: the most granular on citations
Otterly.AI is Austrian, founded in 2024, and takes a different approach: it does not just tell you whether you appear, it tells you which URL of your site gets cited and in what context within the answer. For those working on content, this is the most useful dimension.
On the Mestre client, Otterly confirmed Peec’s 4 mentions and added 2 that Peec had missed: two citations of a blog article on “how to organize an in-person kick-off meeting” that appeared on Perplexity when the query contained “kick-off”. A pattern I would never have found by hand.
Otterly’s limitation is the price for those starting out: the plan useful for an SME client sits above €200 per month, against roughly €100 for Peec on the entry plan. For an agency with 5-10 clients the bill climbs quickly.
Profound: the most suited to already-cited brands
Profound is the premium tool of the group, the one you see cited in the case studies of large American brands. Its strength is “AI share of voice”: it tells you not only how many times you appear, but what percentage of the answer space you occupy compared to those cited alongside you.
On the Mestre client, Profound essentially confirmed the numbers from Peec and Otterly, adding a metric I liked: the “co-citation graph”. When the client was cited in an answer, Profound showed me which other 5-7 brands were cited in the same answer. It is the most useful starting point for building a backlinks as a citation proxy strategy: you know exactly who the AI wants to make you compete with.
Profound’s problem is that on a client with 4-6 mentions in 3 months it is oversized. The tool is at its best above 50-100 monthly mentions. Below that, you pay for features you do not use.
The mistakes I am seeing most often
Three patterns I have seen with clients who bought one of these tools without thinking it through:
- Buying the tool before the upstream work. If you have no authority, no entity in the knowledge graph, no structured content, the tool will tell you “zero mentions” every month. You have paid €100-500 a month to discover that you do not appear. First do the work, then measure.
- Tracking queries driven by ego. Tracking “best X consultant in Bologna” where X is you is pointless: nobody searches that way. Track the queries your target client runs before they know you exist.
- Comparing yourself with the wrong competitors. The tools ask you for the names of the competitors to track. If you put in the 3 big national players and you are an SME from Mestre, the comparison is uneven by definition. Put in competitors of your size and your territory.
- Looking at the dashboard once a month. The value of these tools is the weekly pattern: what changes when you publish an article, when you earn a mention in a publication, when you update the about page. Without a reading rhythm, it is a fixed cost with no return.
What I would do if I had to choose today
Operational summary after the 3 months on the Mestre client:
- You are starting out, budget €100/month: Peec. Fast setup, clear numbers, enough to understand whether you appear or not.
- You have an active blog and want to understand which content the AI cites: Otterly. The URL granularity is what you need.
- You already have 50+ mentions a month and want to optimize the strategy: Profound. The co-citation graph makes the difference.
- You want to do it by hand for 3 months before paying: fair enough. 20 queries x 4 engines x once a week = 320 checks a month. It is doable, it only costs time. After 3 months you will know whether it is worth automating.
These are entry-level checks nonetheless: a real analysis of your visibility in AI answers requires professional tools combined with a strategic reading of the data, not just dashboards.
Measuring is only worth it if you build first
I return to the initial point. Visibility in AI answers is the result of a precise path — a recognized entity, domain and author authority, content structured for extraction. These tools measure the output of that path. They are not a shortcut, they are a dashboard.
For the Mestre client, after 3 months of tools and 6 months of work on the upstream factors, monthly mentions went from 4 to 19. The ratio with the leader dropped from 1:12 to 1:4. The tool did not do this work: it only made it visible.
In the next nodes of the series I will explain how to set up an internal dashboard for AI visibility without paid tools, how to build a set of test queries that makes sense for your sector, and how to read the AI share of voice data without being fooled by noise spikes.
