# AI visibility tools compared What Profound, Peec, Otterly, Scrunch, and the rest of the AI visibility tracking category actually measure, and the questions to ask before you buy one. Source: https://martenfield.com/guides/ai-visibility-tools-compared/ Published: 2026-07-31 --- Most of this category launched inside eighteen months, raised heavily, and now sells the same headline: find out whether AI mentions your brand. The differences that matter are underneath that sentence. This is a functional comparison. We have deliberately not published a pricing table, because published prices in this category change faster than a guide can be maintained, and a stale price is worse than no price. Check current pricing on each vendor's own page before you shortlist. ## The four things these tools actually do Every product in the category is some combination of four jobs. Almost none of them do all four well, and the marketing rarely tells you which one you are buying. **Prompt running.** Sending a set of questions to one or more engines on a schedule. The differentiators are how many engines, how many runs per prompt, and whether you control the prompt set or accept a generated one. **Answer parsing.** Turning a generated answer into structured records: which brands were named, in what order, with what sentiment, and which URLs were cited. This is where the quality difference lives, and it is the hardest part to evaluate from a demo. **Change tracking.** Keeping the history so you can show that something moved. Sounds trivial. It is the reason most spreadsheet approaches collapse in month three. **Recommendation.** Telling you what to do about it. Treat this part with suspicion across every vendor in the category, including the ones with the largest funding rounds. Nobody has a validated causal model linking a specific site change to a specific citation outcome, because the engines do not publish ranking behaviour and change it without notice. ## The questions that separate them Ask these of any vendor, including us. The answers are more diagnostic than any feature list. ### How many runs per prompt, and do you report the variance? Generated answers are not deterministic. Ask the same model the same question three times and you will frequently get three different vendor lists. A tool reporting one run per prompt is reporting a coin flip and calling it a metric. If a vendor cannot tell you their run count, they are either running once or they have not thought about it. Both are disqualifying if you intend to contract on the number. ### API or consumer interface? Most tools query models through APIs, because it is cheaper, faster, and scriptable. Your buyers use the consumer apps, which have different retrieval behaviour, different system prompts, personalisation, and often a different model version. The gap is real and it is not always small. There is no fully clean answer here, since scraping consumer interfaces at volume is fragile and against most terms of service. What matters is that the vendor tells you which one they are measuring, rather than letting you assume it is the one your buyer uses. ### Do you store raw responses, or only the parsed result? This is the question that reveals whether a vendor expects to be doing this in two years. Parsers are wrong sometimes. Brand names collide with common words, competitors get missed, a citation gets attributed to the wrong sentence. If the vendor stored only the parsed output, a parser fix cannot be applied backwards, and your history is permanently built on the old mistake. If they stored the raw answer, they re-parse and your history corrects itself. ### Which engines, and does the list include Google AI Overviews? Most tools cover ChatGPT, Perplexity, and Gemini. Coverage of Google AI Overviews is the common gap, because it needs a search results provider rather than a model API, and vendor claims about the fidelity of that data conflict with each other. Ask specifically how AI Overviews data is obtained. If the answer is vague, the coverage is probably thin. ### Can I export the underlying rows? You want one row per run, per query, per engine, per brand, with the mention type, the cited URL, the position, and the timestamp. If a tool only exports its own charts, you cannot audit it, and you cannot take your history with you when you leave. ## Where the named tools sit Broad strokes, and check each vendor's current documentation before relying on this, because the category is moving quickly. **Profound** is the enterprise end. Heavily funded, broad engine coverage, built for large brands with a team to run it. The depth is real and so is the price floor. **Peec** grew fast in the mid-market on a cleaner, more focused product. Strongest when you have a defined brand and competitor set and want the tracking to be straightforward rather than exhaustive. **Otterly** is the low-cost entry point. Genuinely useful for answering "am I mentioned at all", and honest about being a monitoring tool rather than a strategy platform. **Scrunch** sits toward the enterprise end with an emphasis on brand presence across answers rather than pure citation counting. The category also has a long tail of tools that are a scheduled prompt runner plus a dashboard. There is nothing wrong with that, and it is worth knowing that is what you are paying for. ## What none of them can tell you No tool in this category can tell you what a citation is worth to you. Attribution from generated answers is unreliable. Referral data is incomplete, many answers produce no click at all, and the buyer who reads your name in an answer and searches for you directly the following week arrives as organic brand search. Any vendor quoting you pipeline attributed to AI answers is modelling, not measuring. That is not an argument against measuring citation share. It is an argument for knowing what the number is: a leading indicator of whether you are in the consideration set when a buyer asks a model, which is a real thing to care about and a different thing from revenue. ## If you are choosing today Write your own prompt set first, before you look at any product. Twenty to fifty questions your buyers actually ask, in their words, including the ones where you would expect a competitor to win. That list is the asset. The tool is interchangeable, and most of them will happily generate a prompt set for you that measures the questions you are already winning. Then run it manually once, across the engines you care about, three runs each, logged out. It takes an afternoon. You will learn more from reading twenty raw answers than from any dashboard, and you will know what you are asking a vendor to automate. ## Questions ### Do I need an AI visibility tool at all? If you only want to know whether you are mentioned, you can answer that yourself in an afternoon with a spreadsheet and a query list. A tool earns its cost when you need the same measurement repeated on a schedule, across several engines, with the history kept so you can prove something changed. ### What is the difference between mention tracking and citation tracking? A mention is the model naming you in the text of an answer. A citation is the model linking a source it drew from. They move independently. You can be mentioned constantly without ever being cited, which tells you the model knows of you from training data but is not currently retrieving you. ### Why do two tools give different numbers for the same brand? Because they are asking different questions, at different times, from different locations, with different prompt sets, and often through different interfaces. Generated answers vary between runs. Any tool reporting a single figure without stating run count, date, and prompt set is reporting an anecdote.