Quick summary: this guide rounds up 11 free AEO/GEO tools, organized by the stage of work they solve: discovering what people actually ask, simulating how AI breaks a question into sub-queries (query fan-out), finding content ideas by comparing your sitemap with a competitor’s, checking whether your brand is already cited by ChatGPT and Perplexity, structuring data for machines, controlling which AI bots crawl your site, and measuring all of that through the official channels. None of them is a chatbot or general-purpose LLM — they’re specific utilities, most requiring no credit card or login.
The “AEO/GEO tools” market inflated fast in 2026, and a good chunk of what shows up when you search “free SEO tool for AI” is a new site with no track record, competing for the same wave of traffic. That doesn’t invalidate the tool, but it changes how you should evaluate it: before relying on one in your routine, check whether it solves a specific, real problem, not just whether it promises to “optimize for AI” in vague terms. That’s the filter behind the selection below.
How do you find out what people are actually asking about a topic?
Before structuring any content for generative AI, the starting point is the same as it’s always been: know the exact question someone asks, not the keyword an SEO database assumes represents the intent.
WhatTheyAsk is the tool I maintain for this. It expands a keyword into hundreds of real Google Suggest suggestions, grouped by intent, at no API cost because the calls run directly from the user’s browser. It works as a first filter to find the questions that later become literal H2 headings in an article — the same question-and-direct-answer format that helps both featured snippets and AI citation, as described in the AEO/GEO guide.
Answer Socrates covers a complementary angle: “People Also Ask” extraction with question clustering by subtopic. The free plan allows 5 keyword-generation searches and 5 recursive searches per day, with no clustering or LLM credits unlocked — enough to run one topic a day for free, but not enough to sweep an entire niche at once. It’s the most-used free alternative today to paid tools like AlsoAsked, which caps you at 3 free daily searches and charges for CSV export.
How do you simulate how AI breaks a question into multiple sub-queries (query fan-out)?
1 - Query fan-out is the process ChatGPT, Gemini, and other AI search engines use to answer complex questions: instead of running a single search, the system decomposes the question into multiple sub-queries, searches each one separately, and assembles the final answer from the combined results. I’ve already covered this mechanism in the context of SEO and PPC converging on the same content input, because subquery coverage has become a metric neither discipline can ignore on its own.
2 - LLMrefs’s Query Fan-Out Generator simulates that process: you type in the prompt a user would put into an AI search engine, pick a reference model (Google AI Mode, ChatGPT Search, among others), and the tool returns the list of likely sub-queries, each with a direct button to check the current ranking on Google and Bing for that specific slice. It’s free, no card and no login — the company’s business model is paid continuous tracking, so the generator works as bait, but that doesn’t change the usefulness of what it gives away for free.
In practice, the value isn’t the exact number of sub-queries — no third-party tool replicates Google’s or OpenAI’s actual algorithm — it’s the exercise of finding gaps: if three out of eight generated sub-queries have no page of yours ranking well, that already points to a content gap before any citation report arrives.
How do you find content ideas that generative AI has already validated, by looking at a competitor?
An indirect, low-cost way to know where to invest in content is to see what your direct competitor has already bet on publishing, by comparing the two sitemaps.
3 - Lacuna de Conteúdo (Content Gap) is the tool I’m building to automate exactly that cross-check: paste in the sitemap URL of your site and your competitor’s, and it interprets the topic behind each slug and splits the result into three groups — topics exclusive to the competitor, topics both of you cover, and topics where you already have more depth. It differs from keyword-gap tools like Ahrefs or SEMrush because it starts from content structure that’s already been published, not from a ranking database — the article that details the logic behind it explains why this catches editorial bets three months before they show up in any keyword-gap report. It’s still in testing with real sites before opening up to more users.
How do you know if your brand is already cited by ChatGPT, Gemini, or Perplexity?
Unlike Google ranking, there’s no official dashboard equivalent to Search Console for citations in generative AI answers — at least not a complete one yet. The most direct free path today is to run real buyer prompts against the models and see who shows up cited.
4 - AIclicks’s AI Citation Tracker does this without asking for sign-up, email, or a credit card: you enter the domain, the tool identifies the product or service category, generates typical prompts from someone deciding on a purchase, runs each one through ChatGPT with web search enabled, and returns the list of cited sources, flagging whether your domain is among them. The natural limitation of the free plan is that it delivers a one-time check, not continuous monitoring — to track this automatically every week, most of these tools push you toward the paid plan.
5 - It’s worth running this test alongside the manual check described in the AEO/GEO guide: open ChatGPT, Gemini, and Perplexity directly and ask about your niche without naming your brand, comparing what the automated tool found against what shows up in practice.
How do you structure data so generative AI can read and cite your page?
Machine-readable technical structure is still the factor that most separates who gets cited from who just gets crawled. Two official, free tools cover validation:
6 - Google’s Rich Results Test validates whether a page’s schema.org is correctly implemented for the types Google recognizes, including Article and FAQPage. The Schema Markup Validator, maintained by schema.org itself, validates against the full vocabulary spec, including types Google doesn’t use for rich results but other AI engines might read. Running both is more thorough than trusting just one, because they cover different scopes — the guide to programmatic FAQs with JSON-LD details the question-and-answer markup that these two tools validate.
7 - For llms.txt, the summary file aimed at language models, LLMsFile’s free generator assembles the file from your site’s most important pages, without requiring any prior knowledge of the standard’s syntax. Adoption of llms.txt by AI crawlers is still in an early stage, so the real payoff varies — but the cost of generating one is low enough that it’s not worth skipping.
How do you control which AI bots crawl your content?
8 - Blocking “AI bots” as a generic category usually backfires, because each company runs a crawler with different behavior — one trains a model, another does real-time search to cite a source. The experiment that showed most of the crawler traffic in a server log is fake reinforces why deciding this by looking at raw logs, without a reference list, usually means blocking the wrong bot.
9 - Known Agents (formerly Dark Visitors) maintains an up-to-date list of hundreds of known AI bots and automatically generates a robots.txt that updates itself when a new bot appears — free for most sites, no credit card. For those who’d rather not depend on any third-party service, even a free one, there’s a fully open alternative: the community-maintained ai.robots.txt list on GitHub, which you simply copy and paste, updated through public contributions with no account of any kind involved. It’s the same bot-category reasoning Cloudflare adopted, described in the post on how to block AI training without disappearing from Google.
How do you measure AI citation through official, free channels?
No third-party tool replaces first-party data from the two search engines that still power most generative AI answers.
10 - In June 2026, Google Search Console launched a dedicated generative AI performance report inside the Performance section, showing impressions in AI Overviews, AI Mode, and generative Discover features, broken down by page, country, and device. It doesn’t yet show clicks, CTR, or query-level data, and it’s rolling out gradually property by property, but it’s the most direct, free data that exists today on AI visibility, straight from Google.
11 - Bonus tool: Bing Webmaster Tools remains relevant because Bing’s index feeds Microsoft’s Copilot, and it’s free with the same depth of technical reporting Search Console offers for Google — worth checking even if Bing’s traffic volume looks small in isolation, as already noted in the guide to free SEO monitoring tools.
Summary table: which tool for which stage?
| Stage | Tool | What it solves | Free limit |
|---|---|---|---|
| Discover questions | WhatTheyAsk | Expands a keyword into real Google Suggest questions | No fixed limit, runs in-browser |
| Discover questions | Answer Socrates | PAA extraction with subtopic clustering | 5 searches/day, no clustering |
| Simulate query fan-out | LLMrefs Query Fan-Out | Sub-queries an AI would generate from a prompt | No card, no published limit |
| Find topics via sitemap | Lacuna de Conteúdo | Compares your sitemap vs. a competitor’s | Closed beta |
| Check AI citation | AIclicks AI Citation Tracker | Runs buyer prompts and lists who gets cited | One-time check, no continuous monitoring |
| Validate schema | Rich Results Test / Schema Validator | Validates JSON-LD for rich results and the full standard | No limit |
| Generate llms.txt | LLMsFile | Assembles the summary file from your site’s pages | No published limit |
| Control AI bots | Known Agents / ai.robots.txt | Generates and maintains a robots.txt against AI bots | Free for most sites / open list, no limit |
| Measure on Google | Search Console | Impressions in AI Overviews, AI Mode, and Discover | Gradual rollout, no click data yet |
| Measure on Bing | Bing Webmaster Tools | Technical and indexing data that feeds Copilot | No limit |
Where should you start, in practice?
The order that yields the most signal in the least time is usually: first validate the schema on your most important pages (Rich Results Test and Schema Validator), because ambiguous structure gets in the way of everything else. Next, generate or review your robots.txt against AI bots using Known Agents or the open GitHub list, making sure the right crawler can get in. Only after that does it make sense to run the AI Citation Tracker to get a baseline of who’s already citing you today, and use WhatTheyAsk, Answer Socrates, and LLMrefs’s Query Fan-Out Generator to find the question gaps that still have no answer at all in your content.
None of these tools trade well-written content for a technical shortcut. They cut down the manual work of finding where to invest and confirming whether that investment has already shown up in an AI answer — which only matters if the writing behind it stays dense, direct, and backed by real topical authority, not by isolated markup.