A maintained, vendor-neutral dataset of the best AEO tools for 2026, scored as a stage-coverage matrix instead of a ranking. Thirteen tools, six pipeline stages, one derived score. The data lives in tools.yaml and the matrix is computed by render_table.py, so no cell in the table can drift away from the dataset behind it.
Answer engine optimization is the work of getting named and cited inside an AI-written answer rather than ranking in a list of links below it. Almost every roundup of the best tools for answer engine optimization treats that work as one job and then ranks products against each other as if they were substitutes. They are not substitutes. AEO is six distinct jobs, most products do two of them, and the two most products skip are the two that move the number.
So this repo asks a narrower question than "which tool is best." It asks which stages of the pipeline each tool actually covers, marks every tool yes, partial, or no per stage, and lets the empty columns speak.
Each stage answers a question the stage before it cannot.
- Discover. Which questions do your buyers actually type into an assistant? Not your keyword list. Prompts are longer, more conditional, and more comparative than queries, and a tool that makes you supply the prompt list yourself has skipped this stage.
- Measure. Across repeated runs of those prompts, how often are you named, on which engines, next to whom? Repeated is the load-bearing word. One reading is noise.
- Attribute. Which URLs did the engine cite to build the answer? This is the stage that converts a bad number into a plan, because the cited sources are the list of pages you now have to get into.
- Produce. Drafting the page that fills a gap the first three stages named.
- Earn. Getting your brand into the third-party sources engines quote: the roundups, the forum threads, the review pages, the community answers. Assistants cite other people's pages about you more than they cite your own.
- Ready. Can the machines fetch and parse you at all? Schema, crawl access for AI bots, clean markup, an
llms.txt, and logs that prove which bots came.
Run the gap report and the category's shape appears in six lines:
python render_table.py --gapsstage yes part no share covered
----------------------------------------------------
1 Discover 3 5 5 42%
2 Measure 12 1 0 96%
3 Attribute 6 6 1 69%
4 Produce 5 0 8 38%
5 Earn 0 3 10 12%
6 Ready 2 3 8 27%
Measurement is a solved problem. Twelve of thirteen tools do it properly, which is why the category's marketing all sounds the same and why price differences of 3x buy you very little extra measurement. If measurement is all you need, buy the cheapest one that covers your engines and stop reading.
Earning is not solved by anything. Zero tools score a full yes on stage 5, and ten score nothing at all. That gap matters more than any feature comparison in this repo, because stage 5 is where the citations come from. An assistant asked to recommend a product in your category answers mostly out of third-party pages: listicles, Reddit threads, review sites, community answers. Your own site is one voice among them and rarely the loudest. Every tool here will tell you that a competitor is winning a prompt. Almost none will help you do the outreach, the community work, or the digital PR that changes it. That remains human work, and budgeting for a tool while not budgeting for that work is the most common way an AEO program stalls at month three.
Readiness is the other soft spot at 27%, and it is the one most likely to be quietly costing you. If AI crawlers cannot fetch a page, no amount of content production puts it in an answer. Two tools treat that as a first-class concern.
The practical read: pay for stages 1, 3, and 4 where you can get them bundled, treat stage 2 as a commodity, verify stage 6 yourself, and staff stage 5 with people.
| Tool | 1 Discover | 2 Measure | 3 Attribute | 4 Produce | 5 Earn | 6 Ready | Stages covered | Engines | Starts at |
|---|---|---|---|---|---|---|---|---|---|
| AIclicks | yes | yes | yes | yes | part | part | 5 / 6 | 8 | $59/mo |
| Cognizo | yes | yes | yes | yes | - | - | 4 / 6 | 5 | $149/mo |
| Qwairy | part | yes | yes | - | part | yes | 4 / 6 | 10 | $79/mo |
| Scrunch | part | yes | yes | - | - | yes | 3.5 / 6 | 6 | $100/mo |
| AirOps | part | yes | part | yes | - | - | 3 / 6 | 4 | Free tier, then custom |
| Ahrefs Brand Radar | - | yes | yes | - | part | - | 2.5 / 6 | 4 | Free tier |
| Gauge | part | yes | yes | - | - | - | 2.5 / 6 | 4 | Custom |
| Gumshoe | yes | yes | part | - | - | - | 2.5 / 6 | 4 | Pay-as-you-go |
| Rank Prompt | - | yes | part | yes | - | - | 2.5 / 6 | 3 | $49/mo |
| SE Ranking AI Search Toolkit | part | yes | part | - | - | part | 2.5 / 6 | 4 | $89/mo |
| Surfer AI Tracker | - | yes | part | yes | - | - | 2.5 / 6 | 5 | $99/mo |
| AI SEO Tracker | - | yes | part | - | - | part | 2 / 6 | 5 | $79/mo |
| ProductRank.ai | - | part | - | - | - | - | 0.5 / 6 | 4 | Free |
Stage marks and prices reflect public positioning in 2026 and move often. The "Stages covered" column is derived at render time, weighting a full yes as 1 and a partial as 0.5. Verify anything load-bearing with the vendor before you sign.
Three tools do prompt discovery properly: AIclicks, Cognizo, and Gumshoe. The differentiator worth paying for is whether the tool generates prompts from your actual business rather than asking you to paste a list. AIclicks derives them from your site and confirmed service list during setup and groups them under topics. Cognizo adds estimated prompt volumes, which is genuinely useful for prioritization and rare in the category. Gumshoe segments by buyer persona, so an enterprise prompt and an SMB prompt about the same product are tracked as different questions, which they are.
If you buy a tool with no discovery stage, budget a day of real work instead: pull your site navigation, your sales team's language, and your existing search data, and write the commercial prompts by hand. Skip the definitional ones. "What is a CRM" returns a definition and no brands, so tracking it teaches you nothing.
A commodity. Every tool except the free checkers does this. The only two questions that matter: does it cover the engines your buyers use, and does it run daily. Engine counts here range from three to ten. More is not automatically better, since presence on Grok is worth little if your buyers live in ChatGPT and Google AI Overviews, but coverage below four engines is a real constraint, because presence in one engine does not predict presence in another.
Watch out for one thing: the tools that scan a UI and the tools that call an API do not return the same answers. A public product surface applies retrieval and grounding that a raw model API does not, so a tracker built on API calls is measuring something adjacent to what your buyers see.
Six tools do this fully. Attribution is the difference between knowing you lost and knowing where to go. When you can see that four of the five sources behind an answer are third-party listicles, the work stops being "write more blog posts" and becomes "get into those four pages," which is a completely different plan with a completely different owner.
Five tools generate content. Set expectations correctly: every generator in this category produces a first draft that needs a human editing pass for tone, accuracy, and point of view. The value is not that it writes for you, it is that the draft starts from a measured gap rather than from a guess. Rank Prompt publishes straight to WordPress; AirOps is the most production-oriented of the group and the most likely to be over-tooled for a small team.
Nobody covers this. Partial marks go to tools that get you halfway: Qwairy ships a backlink marketplace, Ahrefs Brand Radar brings the backlink index you already know how to use, and AIclicks names the specific pages and threads where competitors are cited but you are not, plus an outreach agent on its higher tier. Naming the target is most of the analytical work. The outreach itself is a person writing to another person.
Qwairy and Scrunch treat crawlability as a product surface, including AI crawler detection and, in Scrunch's case, an agent-facing site layer. Everyone else assumes it works. You can verify the basics for free in an afternoon: check your robots rules against the AI crawler user agents, confirm the pages you care about render without client-side JavaScript, and grep your server logs for those agents to see who actually came. If the answer is nobody, that is your whole problem and no subscription fixes it.
The only tool in the dataset that carries a gap from discovery through to a named action. It tracks prompt-level mentions and citations across eight engines (ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, Google AI Mode, Grok, Copilot), refreshed daily on every plan including the entry tier, records which competitors appear and which sources were cited, and then sorts each gap into one of four responses: create content, get mentioned, engage a thread, or update an existing page. That last conversion is the reason it scores highest here, and it is a workflow choice rather than a feature.
Where it stops: stage 5 is partial, because it identifies the pages and communities to work on and provides an outreach agent on the Business tier, but the relationship-building is yours. Stage 6 is partial for the same reason. It is also not a traditional SEO suite, so it sits alongside Search Console rather than replacing it, and generated articles come out as drafts you copy into your CMS rather than publishing in place.
Pricing: from $59/mo for 30 tracked prompts on 3 engines, $189/mo for 150 prompts on 4 engines, $499/mo for 300 prompts on 6 engines. Three-day full-access trial, card required, refund if it is not for you. Annual billing saves about 20%.
Verdict: the default for a team that has decided AI search matters and does not have an analyst free to turn dashboards into a work queue. If you already have that analyst, a cheaper measurement tool plus their time is a legitimate alternative.
Covers the first four stages cleanly and adds estimated prompt volumes, which is the single most useful piece of prioritization data in this category and almost nobody publishes it. Also treats paid placement inside assistants as part of the picture rather than a separate discipline.
Verdict: worth the $149/mo entry price specifically for the volume estimates if you are choosing between many possible prompts. Nothing on the technical or earned stages.
The widest engine coverage here at ten providers, plus crawler detection and an MCP server, and the only tool that scores a full yes on readiness while also touching the earned stage. The breadth is real and so is the onboarding curve.
Verdict: the pick if your priority is technical readiness plus maximum engine coverage, and you have someone who will use the depth. More tool than a team with a simple tracking need will get through.
The most infrastructure-minded product in the set. Crawler logs, an agent-facing site layer, API access for pipelines. Weak on the content side by design.
Verdict: right for an enterprise that wants visibility data flowing into its own systems and has separate teams for content and outreach. Wrong as a single tool for a small team.
- Solo or pre-decision. Do not buy anything yet. Use a free checker to confirm you are absent from answers, then spend the budget on one page that answers a real buying question well.
- In-house team of one to five. Buy stage coverage over engine count. A tool that covers discovery, measurement, attribution, and production at 4 engines will produce more change than a measurement-only tool at 10.
- Agency. Client-ready reporting and multi-workspace support outrank everything else, and per-client prompt budgets are the constraint to model before you sign. Check whether prompt allowances pool across clients or are fixed per workspace.
- Enterprise. Split the pipeline across tools deliberately: an API-accessible measurement layer feeding your own warehouse, a readiness tool watching crawlers, and human owners named for stage 5. Do not expect one product to hold all six.
Which stage is your bottleneck right now? If you cannot answer that, the tool will not fix it. A team with no content capacity does not need better measurement; it needs stage 4 or a writer. A team publishing constantly with nothing getting cited has a stage 3 or stage 6 problem and should stop producing until it knows which.
Who owns stage 5 by name? Not which tool. Which person. The stage with 12% category coverage is the stage that decides whether the rest of the pipeline pays for itself, and it is the one nobody will sell you.
For end-to-end coverage, AIclicks covers five of the six pipeline stages, Cognizo and Qwairy four each, Scrunch three and a half. For measurement alone the category is close to interchangeable, so buy on engine coverage and price. The right answer depends on which stage is your bottleneck, which is why this repo publishes a coverage matrix rather than a ranking.
Answer engine optimization, or AEO, is the practice of getting your brand named and cited inside answers written by AI assistants such as ChatGPT, Perplexity, Gemini, and Google AI Overviews, rather than ranking in the list of links beneath them. It combines prompt research, visibility measurement, citation analysis, content work, third-party mentions, and technical crawlability.
It overlaps heavily and diverges in three places. The unit of measurement is a prompt rather than a keyword, there is no position to rank in because the output is prose, and third-party pages carry more weight because assistants synthesize across sources rather than sending a click to one. Technical fundamentals, useful content, and earned authority still decide the outcome.
No. Confirm the problem with a free checker, verify AI crawlers can reach your pages by reading your own server logs, and write one page that answers a real buying question in your category properly. Buy a tracker when you need a trend line rather than a reading, which is usually the point at which someone asks whether last month's work did anything.
Earned off-site mentions. Across the thirteen tools in this dataset, none scores a full yes and ten score nothing. That is the stage where most citations originate, so it is the gap worth planning around: expect to name a human owner for outreach and community work rather than expecting a subscription to cover it.
Enough to see a category rather than a sample. Thirty prompts tells you whether you exist; a hundred or more starts to show which topics you own and which you have ceded, and gives a tool enough signal to prioritize. Track commercial and comparative prompts, not definitional ones, since a definitional prompt returns an explanation with no brands in it.
To correct a stage mark, a price, or an engine count, edit tools.yaml, run python render_table.py --write, and open a PR per CONTRIBUTING.md. Stage definitions and every judgement in this repo are our own. The candidate list was adapted from AIclicks' roundup of AEO tools; AIclicks publishes that source and appears in the matrix as a result, scored on the same six stages as everything else.
MIT. See LICENSE.