The GEO/AEO Tool Landscape: The New Stack for Winning in AI Search
Most GEO tools tell you whether your brand shows up in AI-generated answers. Very few tell you why and almost none help you do anything about it. Here's how the current market actually breaks down.
As buyers increasingly ask ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews, Copilot, and other AI answer engines for recommendations, comparison lists, summaries, and buying advice, brands are no longer competing only for search ranking and links. They are competing to be mentioned, cited, trusted, and recommended inside the answer itself.
That shift has created a new category of tools, often described as GEO, Generative Engine Optimization, or AEO, Answer Engine Optimization. The category has attracted over $200M in disclosed funding since 2024 and produced G2 category leaders, Gartner Cool Vendors, and at least one unicorn. It has also produced a lot of tools that look similar on a feature matrix and perform very differently in practice.
This post evaluates eight of the most widely discussed platforms, Profound, Goodie AI, Scrunch AI, AthenaHQ, Bluefish AI, Peec AI, Otterly.AI, and the Semrush AI Visibility Toolkit, across 16 criteria covering pricing, enterprise readiness, data quality, and the depth of the insight-to-action loop. The goal is not to rank them definitively but to answer the question most buyers actually have: which one is right for my situation?
What GEO/AEO platforms actually do
A GEO or AEO platform tracks how your brand appears in AI-generated answers across major large language model interfaces such as ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude, Microsoft Copilot, and others. At the monitoring layer, these tools show you whether your brand is mentioned, how often, in what context, against which competitors, and which source URLs the AI is pulling from when it answers questions in your category.
The more differentiated platforms go further: they identify content and citation gaps (what you'd need to publish or earn to start showing up), surface inaccurate AI representations of your brand, connect visibility data to published content workflows, and in some cases publish AI-optimized content automatically. The gap between a monitoring-only tool and a full insight-to-action platform is the defining fault line in this market right now.
Why this channel is becoming a priority
AI search volume is displacing traditional search at scale
B2B buyers are using AI to shortlist vendors before engaging sales
Most brands have no idea whether they're visible in these answers
Gartner projects traditional search engine volume will drop 25% by the end of 2026 as users shift to AI-powered answer interfaces. ChatGPT alone surpassed 700M weekly active users by mid-2026. For B2B brands, this matters because the shortlisting conversation "what are the best tools for X" now happens inside an AI interface, not a Google search results page. If your brand isn't in those answers, you're invisible in the earliest and often most influential stage of the buying process. Most marketing teams have SEO programs, paid search programs, and content programs. Very few have any program at all for AI search visibility.
The three categories of GEO/AEO tools
Monitoring-only platforms tell you where you stand. They track brand mentions, share of voice, sentiment, and source citations across AI engines. Peec AI and Otterly.AI are the clearest examples. These tools are valuable for baselining, competitive benchmarking, and building the internal business case for a GEO program. They are not useful for teams that already know they have a visibility problem and need to fix it.
Insight-to-action platforms connect visibility data to optimization workflows. Goodie AI, Profound (via Agents), and AthenaHQ all have some version of this: they don't just show you that competitors appear in 62% of category prompts while you appear in 8%. They tell you what content to create and in some cases create it for you. This is where the real ROI is, but it's also where pricing and complexity increase substantially.
Infrastructure and brand-safety platforms operate at a different level — not tracking citation frequency but managing how AI models represent your brand across all surfaces. Scrunch AI's hallucination detection catches when LLMs fabricate claims about your brand (wrong pricing, incorrect features, invented partnerships). Bluefish AI's metadata governance targets brand consistency across AI engine interpretations. These are niche but critical use cases for brands in regulated industries or with complex, easily-misrepresented products.
Key questions to ask and criteria to consider when evaluating a tool
LLM engine coverage: How many AI engines tracked across real plan tiers (not just enterprise).
Actionability: Does the tool close the gap from "you're not showing up" to "here's what to do about it"?
Competitor SOV tracking: Share of voice benchmarking vs. named competitors.
Source / citation intelligence: Can you see which URLs are being cited by AI and why?
Content optimization / generation: Built-in tools to create or optimize content for AI citation.
Brand accuracy / hallucination detection: Does it catch when AI says something false about your brand?
Which platform wins, and for whom
Top Pick: Profound
Profound is the category benchmark on data quality and enterprise compliance. Its Conversation Explorer, built on 400M+ real user prompts, and up to 11-engine coverage are genuinely unmatched. The pricing structure is more enterprise friendly vs. startups. the Starter plan ($99/mo) covers ChatGPT only at 50 prompts and reviewers consistently describe it as a paid demo, not a working tier. Real multi-engine functionality starts at Growth ($399/mo for 3 platforms), and full engine coverage with Claude, Gemini, Grok, and the rest requires an enterprise contract that typically lands at $2,000–$5,000+/month.
Runner-up: Goodie AI
Strongest all-in-one platform. It combines brand visibility tracking, sentiment analysis, competitive share of voice, and a content writer — all in a single workspace, without requiring separate tools for monitoring and optimization. At around $495/month, it's priced at the upper end of mid-market but justifies it for teams who would otherwise stitch together two or three tools to accomplish the same workflow.
For smaller teams earlier in the process
Otterly.AI at $29/month is the right first step not because it's cheap, but because it provides real monitoring data (prompt coverage, brand mentions, competitor presence, site citations) across ChatGPT, Perplexity, and AI Overviews without setup overhead. Sixty to ninety days of Otterly data is often enough to build the internal business case for a larger GEO investment. Peec AI is the natural next step if source-level citation intelligence or international tracking matters. It supports 115+ languages with regional competitor benchmarking on every plan tier.
| GEO criterion | Profound | Goodie AI | Scrunch AI | AthenaHQ | Bluefish AI | Peec AI | Otterly.AI | Semrush AIT |
|---|---|---|---|---|---|---|---|---|
| G1 · LLM engine coveragenumber of AI engines tracked across real plan tiers | 5 | 5 | 4 | 4 | 4 | 3 | 3 | 2 |
| G2 · Monitoring → action loopinsight to optimization workflow depth | 4 | 5 | 3 | 4 | 3 | 2 | 2 | 3 |
| G3 · Competitor SOV trackingshare of voice vs. named competitors | 5 | 4 | 5 | 4 | 4 | 5 | 4 | 3 |
| G4 · Source / citation intelligencewhich URLs AI cites and why | 5 | 4 | 4 | 3 | 3 | 5 | 3 | 2 |
| G5 · Content optimization / generationbuilt-in AI writing and optimization tools | 4 | 5 | 2 | 4 | 2 | 2 | 2 | 3 |
| G6 · Brand accuracy / hallucination detectioncatches AI misinformation about your brand | 3 | 3 | 5 | 3 | 4 | 3 | 2 | 2 |
| GEO weighted total / 33 max · G1 & G2 at 1.5× | 30.5 | 31 | 26.5 | 26 | 23.5 | 22.5 | 18.5 | 17.5 |
G1 (LLM engine coverage) and G2 (monitoring → action loop) carry 1.5× weight, reflecting their outsized importance in determining whether a GEO platform delivers real value vs. a monitoring dashboard.
GEO/AEO Platform Evaluation — GTM Analysis Series · May 2026 · Scores based on public product documentation, G2 reviews, and analyst research. Verify directly before purchase. By Wonji, created using Claude.
What the market is still missing
The most fundamental limitation of every GEO/AEO platform on this list is one almost none of them disclose prominently: none of them have first-party data access.
Google Analytics and Google Search Console work the way they do because Google owns the platform. When Search Console tells you that a page received 12,000 impressions for a specific query at an average position of 4.2, that number is exact, sourced directly from Google's own index and served to you via an official API. There is no equivalent in AI search. OpenAI, Anthropic, Google, and Perplexity do not publish APIs that tell you how often your brand was mentioned, in what context, or with what frequency across their user bases. That data doesn't exist for third parties to access.
What GEO/AEO platforms do instead is simulate queries. They construct a set of prompts either synthetically generated or sourced from third-party data, submit them to AI interfaces (some via UI scraping, some via API), observe the outputs, and report on what they find.
Coverage percentage is not impression share. When a platform tells you your brand appears in 34% of tracked prompts, that number reflects only the prompts that platform chose to track — not the full universe of queries users are actually submitting.
Prompt databases are proxies, not pipelines. Profound's 400M+ prompt dataset and Semrush's 100M prompt database are often cited as competitive differentiators, and prompt volume does matter for sample representativeness. But these are third-party behavioral datasets, not live feeds from the AI platforms themselves.
Sentiment and accuracy scoring is model-dependent. When a platform reports that your brand is referenced "positively" in 78% of AI answers, that sentiment classification was itself produced by an LLM — a different one than the one that generated the original answer.
Current GEO/AEO tools cannot tell you:
how many real users asked a specific prompt
how many times your brand was actually shown in AI-generated answers
your true impression share across all relevant AI answers
your true click-through rate from AI answers
whether a specific answer led to a visit, signup, demo request, or purchase
how often a competitor was shown to real users versus just in sampled prompts
Instead, they estimate visibility by running representative prompts and measuring whether your brand appears in the generated answers.
In addition, attribution is still a challenge as it’s difficult to prove how many real users saw those answers or how many conversions those answers influenced. GEO measurement today looks less like paid search attribution and more like brand, PR, and dark-funnel measurement.
However, directional signal on competitive visibility, citation source patterns, and brand representation gaps is genuinely valuable. The best tools will not just show whether a brand appears in AI answers. They will help connect AI visibility to actionable insights and downstream signals like branded demand, website engagement, pipeline quality, and revenue influence.