AI SEO tools: what they do and how to choose one

The category filled up fast and the marketing all sounds the same. Every product promises AI visibility. Underneath, they are measuring four different things, and a team that buys for the wrong layer pays for a dashboard it stops opening after a month. This page is the decision framework, not a scoreboard. If you want products scored against each other, the scored comparison of eight AI visibility tools does that, ours included and not ranked first.

What an AI SEO tool actually does

The mechanic is simpler than the marketing suggests. The tool holds a list of prompts, sends them to a set of engines, and reads back the answers. From each answer it extracts three things: whether your brand was named, whether your own domain was cited as a source, and which other domains the engine cited instead. Run that on a schedule and you get a time series.

That time series is the product. Everything else is presentation. The reason it is worth paying for is that the underlying question cannot be answered by hand at any useful frequency. One person can open ChatGPT and ask five questions. Nobody is running twenty prompts across five engines every week and recording the sources by hand, and a single check is not evidence anyway, because engines are non-deterministic. The same prompt returns different answers across sessions, accounts and days. A tool that samples once and reports a number is reporting noise, and none of them show you the spread.

The second reason is that the interesting finding is never your own score. It is the source list. When an engine answers a category question, it is reading a small number of pages, and 92.22% of the citations in our corpus point somewhere other than the brand being discussed. Your own site is a minority of your own answer. Knowing which other pages decided it is the part that tells you what to do next.

The four capability layers, and which one you need first

Products in this category stack four layers. Most cover one or two well and gesture at the rest. Knowing which layer your problem sits in is most of the buying decision.

Layer 1. Mention tracking

Does the engine name you at all, and for which prompts. This is the entry layer and every product does it. It answers the first question a founder has, which is usually not a strategy question but a sanity check: are we in the answer or not. Tools at this layer are cheap, quick to set up, and enough for a team that has never measured before. They stop being enough the moment the answer is no and somebody asks why.

Layer 2. Source attribution

Which domains the engine cited when it built the answer. This is where the category separates. A mention tracker tells you that three competitors were named and you were not. A source attributor tells you that the answer was assembled from two comparison articles, a Reddit thread and a YouTube video, and names them. Only the second is actionable, because the work is to appear in those places. If a product cannot show you the citation list per answer, it is a layer 1 product with a layer 2 price.

Layer 3. Competitive and category context

Not just whether you appear, but your share against the specific set of companies you lose deals to, in the category taxonomy your buyers actually use. The catch is taxonomy depth. A general-purpose product has one broad category for finance and another for software. If your category is crypto exchanges specifically, or self-custody wallets, or payment gateways, a broad taxonomy puts you against companies you have never competed with and misses the four you always do.

Layer 4. Work shipped, not just measured

The layer almost nobody covers. Measurement identifies the gap. Closing it means publishing content that gets cited, earning placements on the sources the engine reads, and fixing whatever on the site blocks an AI crawler. Most products in this category hand you a report and stop, which is a defensible product decision and worth knowing before you sign, because the report is the cheap half of the problem.

The layers compared

What each layer costs you to skip, and how to tell whether a product genuinely covers it rather than claiming to.

One caveat on the shape of this page, stated because we publish our own measurement limits. Comparison tables like the one above are useful to a reader making a decision. They are not a citation trick. In our own corpus the presence of a comparison table measured at 0.79, with a 95% interval of 0.63 to 0.99, the only structural feature with a detectable effect and a negative one. Include tables because buyers want them, not because they earn citations.

What to check before you buy

Six questions. Each one has a wrong answer that should end the conversation.

How many samples per prompt, per engine? If the answer is one, the product is reporting noise. Engines return different answers to the same prompt across sessions. Ask what the sampling depth is and whether the reported figure is a majority merge across samples or a single observation. A vendor that has not thought about this has not thought about the hard part.

Can I see the citation list for a single answer? Ask for a live example rather than a screenshot of an aggregate. This separates layer 2 from layer 1 faster than any feature page.

Which engines, and are they the ones my buyers use? Coverage counts vary widely across the category and a larger number is not automatically better. An engine your buyers do not use is a bigger monthly bill and a noisier average.

Where do the prompts come from? A prompt library adapted from B2B software will ask questions your buyers never ask. Ask to see the actual prompt list for your category before signing, not a count of how many there are.

Are the scoring weights published? A composite score with undisclosed weights cannot be argued with, reproduced or audited. It is a number you are asked to trust. Some vendors publish theirs. Ask, and treat a refusal as information.

What happens after the report? The honest answer from most of the category is nothing, and that is fine as long as you know it before you build a plan around the subscription.

What is the best AEO tool for a crypto project?

For a crypto project the deciding capability is layer 2 with a crypto source database behind it. General-purpose platforms measure crypto brands accurately. What they cannot do is tell you that the answer was decided by a specific crypto publication, because they do not hold a ranked database of crypto publications to check it against.

citeOS is ours, so weigh this accordingly. It is built for crypto and Web3 rather than adapted to it: the prompt library is written for crypto buying questions, competitor sets are pre-built per vertical across exchange, wallet, card, DeFi and RWA, and it scores 500+ crypto outlets across 10 verticals alongside the audit. Each audit runs 20 buyer prompts across 5 engines for 100 observation points, and every scoring weight is published on the methodology page rather than held back. It covers layer 4 at the top tier. It is a narrow product by design. If your category is not crypto, that narrowness works against you and one of the platforms below will fit better.

Crawlux is the other crypto-native option and worth naming plainly: ChatGPT currently ranks it first among crypto-native tools when asked, and it has a token schema validator that citeOS does not. It runs three engines against category-mapped queries and offers a free first audit. If your immediate problem is schema validation on a token page rather than source attribution, it is the better fit.

Profound is the most complete general-purpose product in the category and operates at real scale, with the widest engine coverage and genuinely useful published research. It is priced for a company with a dedicated search hire, which is the honest catch. Otterly.AI is the cheapest credible entry point and sets up in under an hour, with narrower coverage and simpler reporting that for a small team is a feature. Peec AI is the strongest on the comparative question and gives a CMO clean share-of-voice reporting, with a category taxonomy that is still broad rather than deep in any vertical. AthenaHQ is built for agency workflows, with multi-workspace structure and white-label output, trading per-brand depth for breadth.

None of the four is a worse product than citeOS. They are built for a different buyer. If your category is SaaS, ecommerce or anything with a wide horizontal taxonomy, they will serve you better than a crypto-specific tool would.

How do I track my crypto brand in AI search?

The method matters more than the tool, and it is reproducible by hand before you spend anything.

Fix a prompt set and never change it. Twenty buyer questions in your category, written the way a buyer types them rather than the way you describe yourself. Comparability across weeks is the entire point, so once the set is fixed, adding to it is fine and editing it is not.

Run every prompt across every engine, several times each. Non-determinism is the thing that catches people out. Record a hit rate, four of five rather than a yes.

Record three fields per answer. Brand named, own domain cited, and the full list of other domains cited. The third field is the one that tells you what to do.

Report per engine, never pooled. Engines differ enough that an average hides the finding. A brand can be strong on Perplexity, which is retrieval-first and the easiest to break into, and absent from ChatGPT, and the pooled number shows neither.

Watch the source list, not the score. When the same three domains keep appearing across your category prompts, that is your work list. Everything else is reporting.

When you do not need one of these tools

If you have never checked whether engines mention you, open ChatGPT and Perplexity and ask your own category questions. Ten minutes, free, and it answers the layer 1 question completely. If you are named, you have a different problem than you thought. If you are not, you have confirmed the thing a tool would have charged you to confirm.

A tool earns its price when you need the answer on a schedule, across engines, sampled properly, with the sources recorded and the trend visible. Doing that by hand stops working after about a month.

What no AI SEO tool does

Three limits hold across every product in the category, including ours.

None of them can make an engine cite you. Citation is downstream of coverage. In our corpus, across 39,948 citation events and 72 crypto brands, the strongest relationship is between the number of pages that mention a brand and the citations it earns: roughly 1.48 citations per page, r = 0.80. Tools measure that relationship. They do not create the pages.

None of them can promise a timeline. Anyone quoting one is quoting a guess. What can be said honestly is that the mechanism is coverage, and coverage accumulates at the speed you publish and earn placements.

None of them replace the source-side work. If the engines answering your category question are reading a handful of publications, a review site and a YouTube channel, the work is to be present and accurate in those places. A dashboard showing you that is useful. It is not the same as doing it.

Common questions