In practice, the two terms describe the same discipline from slightly different angles. Generative Engine Optimization emphasizes the AI system that generates the answer, while Answer Engine Optimization emphasizes the format, a direct answer instead of a ranked list. Most vendors and buyers use them interchangeably in 2026, and the tools in this guide typically market themselves under both labels depending on the page.

Entry pricing across the ten platforms in this guide runs from $20 a month at the low end to $295 a month at the high end, with most landing between $39 and $250. The real cost usually depends less on the headline number and more on how many AI engines, prompts and seats a team needs, since several vendors charge those as add-ons rather than including them in the base price.

Yes, at a basic level. Manually asking ChatGPT, Perplexity and Gemini the questions a buyer would ask, and noting whether a brand gets mentioned, is a real starting point and costs nothing. Several vendors, including InsightWonder and Ahrefs, also offer a free one-time AI visibility snapshot tool that doesn't require an account. The limitation is that manual checks don't scale past a handful of questions or track change over time the way a paid platform does.

ChatGPT and Google's AI Overviews carry the largest share of everyday AI-assisted search traffic, so most teams start there. Perplexity matters disproportionately for research-heavy and technical audiences, and Gemini's reach follows wherever Google's AI Mode is active. Claude and Microsoft Copilot tend to matter most in B2B software categories where those assistants are embedded directly into work tools.

Not automatically, and this is one of the more honest gaps in the category right now. A February 2026 Reddit thread on GEO tooling summed it up well: these tools are useful for directional insight, not absolute truth, because API-based tracking doesn't always match what a real user sees in the ChatGPT or Perplexity interface. Platforms with revenue attribution built in, like AthenaHQ's Shopify and GA4 integration, are trying to close that gap, but for most tools a rising mention rate should be read as a leading indicator rather than a guaranteed outcome.

Weekly monitoring has become the common default across this category, and it's usually enough to catch meaningful shifts without drowning a team in noise from normal model-to-model variance. Brands in fast-moving categories, or running active PR and content campaigns, often move to daily tracking during the campaign window and back down afterward.

No. Every platform in this guide still depends on a human writing, approving or at least reviewing the content that closes a visibility gap, and several, including InsightWonder, deliberately keep publishing draft-first with a required confirmation step rather than auto-publishing. GEO tools are better understood as a measurement and prioritization layer that tells a content team where to focus, not a replacement for the team itself.

llms.txt is a proposed convention, similar in spirit to robots.txt, that gives AI crawlers a structured summary of a site's key pages and facts. It's not a universal standard yet and not every AI engine reads it, but several platforms in this guide, including AthenaHQ and InsightWonder's free validator tool, treat it as one small, low-effort signal worth setting up alongside more established technical basics like structured data and crawlable HTML.