2026-09-17, GEO · Analytics · Fundamentals

Best AEO tools in 2026, organized by the job you need done

The best AEO tool depends on which job you are trying to get done, because the category now splits into distinct jobs that few products cover equally well:

1. Tracking answers: what ChatGPT, Perplexity, Gemini, Copilot and Google’s AI surfaces say about you and your competitors.
2. Deciding what to change: which sources, pages and topics to work on to be cited more.
3. Controlling AI access to your site: which AI crawlers and agents fetch your pages, what they are allowed to do, and what they receive.
4. First-party data: what an answer engine itself reports about citations of your site.

This guide organizes verified tools by those jobs, with a build-on-an-API option for teams that need raw data. Every price and feature below comes from the vendor’s own page, checked on 2026-09-17 and linked. If you want a plan-by-plan comparison of AI visibility platforms instead, read best AI visibility tools; if you are new to the term, what is AEO.

Answer engine optimization (AEO) is the practice of making a brand’s content findable, understandable and citable by AI systems that answer questions directly, so that it appears accurately in those answers.

The tools at a glance

Job 1: tracking what answers say

The unit that matters is answers per month (prompts × engines × runs), and Peec’s pricing page defines its monthly AI answers that way.

Peec AI

Starter is €85 a month with 50 prompts, 3 chosen models, daily tracking and 1 project; Pro €205 with 150 prompts and 2 projects; Advanced €425 with 350 prompts, 5 projects, multi-country and a Looker Studio integration. Models to choose from on those plans are ChatGPT, AI Mode, AI Overviews, Microsoft Copilot, Perplexity and Gemini. Enterprise adds API access, SSO and up to 13 tracked models. The Measure section lists visibility, position, sentiment and share of voice tracked daily against competitors, an ads library of prompts with sponsored placements, and AI shopping analytics with SKU-level tracking.

Otterly.AI

Lite is $29 a month for 15 prompts across ChatGPT, Google AI Overviews, Perplexity and Microsoft Copilot, tracked daily, with ChatGPT ads tracking. Standard is $189 for 100 prompts and Premium $489 for 400; Enterprise starts at $1,000. Google AI Mode, Gemini and Claude are paid add-ons. API and MCP access come with Standard (2,000 requests a month each) and Premium (5,000).

Scrunch

Starter is $300 month to month or $250 a month billed annually, with 350 custom prompts, 3 personas and 3 users; Growth is $500 or $417 annually with 700 prompts and 5 personas. Coverage listed: ChatGPT, Claude, Gemini, Perplexity, Google AI Mode and AI Overviews, and Meta. Enterprise adds an Enterprise Data API.

Profound

The free Trial tracks 10 unique prompts once, on ChatGPT only, without data history, exports or API. Enterprise is custom-priced with daily tracking across up to 9 answer engines (ChatGPT, Perplexity, Google AI Mode, Gemini, Microsoft Copilot, DeepSeek, Claude, Google AI Overviews, Exa Search), ChatGPT Shopping, CSV and JSON exports and API access.

AthenaHQ

Essential is free with $25 of credit (300 credits) across ChatGPT, Perplexity, Google AI Overviews, Gemini and Copilot. Starter is $295 a month for 3,600 credits across 11 models, adding AI Mode, Claude, Grok, DeepSeek, Meta AI and Mistral; API access and extra credits are paid add-ons.

Semrush AI Visibility toolkit

$99 a month per domain billed annually, with 25 custom prompts tracked daily and mentions from ChatGPT, Google AI, Gemini and Perplexity. The knowledge base lists Visibility Overview, Competitor Research, Prompt Research, Brand Performance, Prompt Tracking and an AI Search Site Audit.

Ahrefs Brand Radar

Custom prompts start at $50 a month per model, or $699 a month for all models, refreshed daily; the AI Visibility Index is $199 a month. The page lists AI Overviews, AI Mode, ChatGPT, Gemini, Perplexity and Copilot, with Claude on higher tiers.

When comparing trackers, check whether the engines your buyers use are included or add-ons, how many runs sit behind each number (see sampling vs census), and whether you can export the underlying answers, not only scores.

Job 2: deciding what to change

Tracking tells you that you are missing. This job tells you why and what to do. The inputs are the same everywhere: which sources get cited for your prompts, which of them mention competitors but not you, and what the engine searched for before answering.

Using these outputs:

1. Split earned from owned. A gap on a review site or forum is an outreach problem; a gap on your own pages is a content problem. Peec groups recommendations this way; do the same with any tool.
2. Work from fan-out queries. The searches an engine runs before answering are the closest thing to keywords in AEO. See ChatGPT query fan-out and how AI engines choose citations.
3. Fix accuracy before visibility. Wrong claims about pricing or features cost more than a missing mention. Features like Peec’s fact-checking, or a manual review of answer text, catch them.
4. Re-measure the same prompts. A recommendation is only validated by the tracked prompts moving over several runs, not one.

Job 3: controlling AI crawler and agent access

An answer engine cannot cite what its crawler cannot fetch or parse. This job covers three questions: which AI bots reach the site, whether rules allow them, and what they get back. For background, see AI crawlers and llms.txt.

Selection notes:

• Ask where the data comes from. Profound says its Agent Analytics uses server logs instead of JavaScript trackers, and Peec lists CDN and log-upload integrations. A tool that only sees what a page script reports may miss bot visits.
• Separate retrieval bots from training bots. Blocking a training crawler and blocking the bot an assistant uses to fetch a page at answer time have different consequences. Decide per bot.
• Be deliberate about serving bots different content. A version served only to AI retrieval bots should carry the same facts as the human page; drift between the two is hard to audit.

Job 4: first-party data from an answer engine

Microsoft’s Bing Webmaster Tools has an AI Performance report. Bing’s June 2026 announcement describes visibility into “how their content appears in AI-generated answers across Microsoft Copilot, Bing, and select partner AI experiences”, including grounding queries, and adds four preview features: Intents, Topics, Citation Share (your share of citations for a grounding query) and Compare (overlaying time periods). The announcement describes them as rolling out in preview.

First-party data is the only view of citations that does not come from sampling prompts, but it covers one company’s surfaces. Pair it with tracking for ChatGPT, Perplexity, Gemini and Google. For Copilot answer collection, see monitoring Copilot with an API.

Build on an answer API

Every job above rests on the same raw material: the answer text, its sources and citations, and the searches behind it, for a prompt, engine and market. An answer API gives you that directly, so you can run AEO programs larger than a plan’s allowance, keep the data in your own warehouse, or build AEO features into your own product.

This API returns structured JSON per engine: POST /v1/monitor/chatgpt, /gemini, /copilot, /grok, /perplexity, /aimode, /google (with the AI Overview) and /google/news. Field schemas are on each engine page, for example Perplexity.

Credits per async task (credits): ChatGPT 5 (+2 once for query fan-out, shopping, ads or raw response), Perplexity 4, Gemini 4, Copilot 5, Grok 5, AI Mode 4, Google Search 3 (+2 for the AI Overview). Synchronous calls add 2. Plans are on /pricing.

How the four jobs map to fields:

A daily tracking task, queued in a batch of up to 500:

{
  "taskType": "CHATGPT",
  "payload": {
    "prompt": "best accounting software for freelancers",
    "country": "US",
    "include": { "searchQueries": true }
  },
  "idempotencyKey": "chatgpt-acct-012-US-2026-09-17",
  "webhook": { "url": "https://example.com/hooks/answers" }
}

Submitted with POST https://api.answerline.dev/v1/async/task/batch and Authorization: Bearer $ANSWERLINE_API_KEY; that task costs 7 credits. The rest of the pipeline is in programmatic AI visibility tracking and AI rank tracking.

What the API does not do: it has no crawler analytics and no recommendations engine. Jobs 2 and 3 are yours to build or buy.

Selection criteria

1. Name the job first. A crawler problem is not solved by a visibility dashboard, and a citation gap is not solved by bot analytics.
2. Count answers, not prompts. Prompts × engines × runs × markets. Compare that number to each plan.
3. Check engine coverage at your tier. Several tools sell AI Mode, Gemini or Claude as add-ons or higher tiers.
4. Ask how answers are collected. Consumer surfaces and model APIs can answer differently; see provider APIs vs consumer answers.
5. Demand the evidence behind recommendations. A recommended action should link to the answers and sources that produced it.
6. Check the data exit. Exports, API tier and request allowances decide whether you can combine AEO data with traffic and revenue.
7. Check where crawler data comes from. Server logs or CDN integrations, and which CDNs.
8. Match the buyer. In-house teams usually buy a platform; agencies and product teams building their own reporting need an API. See white-label AI visibility.

Example stacks

Pitfalls

• Buying for a score. Visibility scores are defined differently by each vendor; trends inside one tool are comparable, numbers across tools are not. See GEO vs SEO metrics.
• Too few prompts per topic. A single prompt swings from run to run; see AI answer volatility.
• Blocking the wrong bot. A bot that fetches pages at answer time is not the same as a training crawler; check which one a rule blocks before shipping it.
• Treating recommendations as done work. Validate each change against the same tracked prompts over time.

To start tracking answers from your own prompt set, see the quickstart or the AI visibility tracking use case.

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