Search traffic is no longer a simple Google SERP game. In March 2025, Google AI Overviews appeared on 13.14 % of U.S. results pages, while ChatGPT, CoPilot, Claude, Perplexity, and friends attracted more than 600 million unique visitors in a single month.

The trouble for search visibility within the era of AI is this—when LLMs summarize a topic, they pick only a handful of brand mentions or product recommendations. If your company isn’t in the short‑listed answer, you are invisible.

How are brands to keep up?

Large Language Model Optimization (LLMO) has become essential for maintaining brand visibility. 60% of consumers are now using AI chatbots for product research, and traditional web clicks have decreased by 34% due to user reliance on AI-generated responses.

Traditional SEO suites weren’t designed to measure—or shape—how models present your brand. LLMO tools give marketers a purpose‑built radar and action layer for the AI answer economy.

This comprehensive analysis examines the top 9 LLMO software platforms that marketers, SEO professionals, and content specialists need to understand in today’s AI-driven search environment.

First, Let’s Define the Category: What the Hell Is LLMO?

LLMO is an emerging discipline of marketing that focuses on optimizing for brand visibility and performance on AI surfaces and large language models like ChatGPT, Gemini, and Claude.

Every product profiled below checked at least three of the five boxes as of the evaluation date, June  2025.

1. Goodie AI

The AEO Pioneer That Set the LLMO Bar

Goodie launched as an Answer Engine Optimization platform and has evolved into a full LLMO control tower. Its core modules include AI Visibility Monitoring, an Optimization Hub that prescribes prompt‑level fixes, a Topic Explorer fed by multi‑LLM data, and a Traffic & Attribution suite linking citations to conversions.

The dashboard covers ChatGPT, Gemini, Claude, Perplexity, DeepSeek, and Google AI Overviews out of the box, and its 15‑factor “AEO Periodic Table” gives teams a science‑backed checklist for boosting mention share.

The highlights:

• It is the most comprehensive platform in the space, covering the most models and features needed to effectively execute an LLMO strategy.
• Deep competitive benchmarking for brands (think SimilarWeb for AI answers).
• Action layer includes multistep outreach agents that can pitch journalists, update Wikipedia, or push structured brand‑verified data feeds to model providers.
• Clean UI built for SEO, PR and growth teams—not just data scientists.

For pro marketers, Goodie AI provides the most comprehensive dashboards for AI search performance metrics, competitor benchmarking, and content gap analysis. The platform’s integrated approach combines monitoring with actionable optimization recommendations, making it ideal for CMOs and brand managers who need real-time insights into AI search performance while maintaining control over brand narrative across AI platforms.

Best for: Mid‑market to enterprise brands that need one pane of glass for monitoring + action.

Pricing: Subscription-based with lower tiers starting at $950. Custom pricing available upon request.

2. Ahrefs

An SEO Powerhouse Now Tuned for LLMO

Ahrefs rolled out some early LLMO features into existing tools like Site Explorer and Content Explorer. You can filter keywords that trigger AI Overviews, see which of your pages are cited in ChatGPT or Perplexity, and run backlink campaigns targeted at the sources that LLMs quote most.

The highlights:

• Giant 10B keyword dataset now flags SERP features labelled AI.
• “LLMO Opportunities” report surfaces unlinked brand mentions inside popular Q&A sites, Reddit threads, and news stories to seed future model training.

Limitations: No prompt‑level sentiment scoring and no optimization workflow—you’ll still need manual outreach. Ahrefs is still primarily focused on conventional SEO.

Best for: Organizations who are already leveraging Ahrefs in their marketing toolkit and are looking to incorporate elements of LLMO.

Pricing: Seamless for existing Ahrefs users—no extra contract. Pricing for Ahrefs starts at $108 per month for an annual contract.

3. Evertune AI

Research‑Grade Brand Perception Analytics

Evertune AI treats LLMs as survey respondents. Its engine autogenerates thousands of category‑specific prompts, captures the answers, then distills the data into insights about share of voice, sentiment polarity, and reasoning chains.

The highlights:

• Prompt panels allow users to choose personas (ex. “CFO evaluating ERP software”) to see how positioning shifts.
• Bias flagging that alerts when models use outdated pricing or misinformation.
• Export‑ready charts for board decks.

Best for: Fortune 1000 communications teams and investor‑relations groups that need brand‑perception telemetry.

Pricing: Pricing for Evertune AI is custom and not readily available on their website.

4. Semrush

An AI Toolkit Added to a Familiar Swiss Army Knife

Semrush’s new AI Visibility widgets live inside Position Tracking and the Enterprise AIO module. You can monitor how often AI Overviews cite your domain, discover the common citation sources in your niche (Quora and Reddit dominate today) and calculate the 4.4× higher value of AI visitors vs. traditional organic sessions.

The highlights:

• Combines traditional SEO with emerging LLMO capabilities.
• Integration with ContentShake and backlink outreach means fixes sit one click away.
• Provides extensive educational resources and certification programs, with documented case studies across multiple industries.

Note: Visibility is Google‑centric; coverage of ChatGPT or Claude is thinner than pure‑play LLMO suites.

Limitations: Semrush entered the game a bit late, so the product is still largely traditional SEO focused and may fall short in terms of feature parity compared to other players. Additionally, the comprehensive feature set creates complexity that may overwhelm smaller businesses, and many advanced features require additional subscriptions beyond base plans

Best for: If your org already relies on Semrush, enabling the AI tab is friction‑free. Also ideal for teams needing comprehensive marketing toolkits.

Pricing: Semrush pricing is largely custom and varies depending on the specific toolkits your team needs.

5. LLMO Metrics

Laser‑Focused on Generative‑Engine KPIs

LLMO Metrics positions its solution as helping companies “be the brand AI recommends.” It tracks answer accuracy, brand‑mention frequency, and source bias across ChatGPT, Gemini, CoPilot, and more. Its “Prompt Search Volume” panel gives a sense of demand—how many people are asking a query inside chat products.

The highlights:

• One‑click “Fix” cards that link a problematic answer to suggested PR targets or on‑page schema tweaks.
• Role‑based dashboards for PR vs. SEO vs. product.
• Free tier to monitor 25 prompts—useful for startups.

Best for: Multi-discipline teams, particularly for SaaS and technology products.

Pricing: LLMO Metrics offers a freemium model, with paid plans starting at 72 Euros per month when billed annually.

6. Peec AI

SMB‑Friendly Analytics With Slick UX

Founded in 2025 in Berlin with $2.01M pre-seed funding from Angel Invest, Antler, Combination VC, and other tier-1 investors, Peec AI represents cutting-edge innovation in the LLMO space.

Despite being extremely new with only three employees at the time of writing this article, Peec AI has gained recognition as one of the early GEO/LLMO tools to differentiate between brand visibility and source citations.

Peec brings a polished, almost Mixpanel‑like interface to AI answer tracking. Line/bar views show week‑over‑week visibility shifts, and underperforming prompts are flagged in red.

The highlights:

• Industry‑ranking leader‑boards (great for investor updates).
• Slack alerts when your share of voice dips below threshold.
• White‑label PDF exports for agencies.

Best for: Agencies looking to integrate LLMO into their service offering and small, specialized teams in need of real-time updates.

Pricing: With plan prices between $79 and $299 per month, it’s the most affordable option on our list.

7. Bluefish AI

Brand‑Safety Meets AI Marketing Agents

Backed by Crane and Bloomberg Beta, Bluefish AI offers an AI Marketing Suite that marries brand‑safety monitoring with data‑syndication pipelines. It even allows brands to push brand‑verified data feeds directly into model providers’ ingestion queues. A separate chatbot platform helps enterprises run on‑site assistants that echo the approved messaging.

The highlights:

• “AI Funnel Consumer Insights” shows how different audience segments see you across LLMs.
• Custom AI agents can run agent campaigns—think interactive advertorials inside chat interfaces.

The platform’s comprehensive approach combines monitoring, optimization, and engagement capabilities. Industry expertise from team members with proven MarTech experience and backing from top-tier AI-focused investors provide strong credibility. However, limited public case studies and pricing opacity may challenge evaluation processes.

Best for: Enterprises, likely with specialized AI and search teams, who are looking to rapidly scale up their LLMO efforts.

Pricing: High‑touch, starting at around $4,000 per month after demo.

8. daydream

Full‑Spectrum GEO/LLMO Services + In-House Tech

daydream operates as a full-service SEO and GEO agency rather than a traditional software platform. The company was founded in 2023 in San Francisco with $3.8M seed funding from First Round Capital, Basis Set Ventures, WndrCo, and Hustle Fund. Led by recognized SEO experts, the agency specializes in programmatic SEO and AI-driven content optimization.

Daydream began as a programmatic SEO agency; v2.0 adds Generative Engine Optimization (GEO) services, open‑source frameworks for LLMs.txt, and a proprietary visibility‑score model.

The highlights:

• Thought‑leadership depth—its public library dissects how Gemini, Claude, and Perplexity crawl and cite content.
• It combines editorial SEO, technical SEO, and LLM‑specific data structuring under one retainer.

Best for: Teams who would rather outsource their LLMO and GEO efforts than license software for an internal team.

Pricing: Pricing is custom after a 30-minute discovery call with their team.

9. Soar.sh

Community‑Driven LLM SEO for Reddit & Quora

Soar is technically an agency, but its LLM SEO capabilities earn it a place because it solves a unique slice of optimization: getting your brand into the training data by dominating Reddit and Quora threads.

The highlights:

• They create and manage Reddit threads that often rank in organic Google and feed LLMs.
• They also offer Subreddit acquisition and Discord community programs that seed long‑tail, high‑trust UGC—which content language models love.

Best for: For companies who are already leveraging one of the analytics-heavy platforms listed above, Soar.sh works well as a complement.

Pricing: Pricing is custom through a proposal request Typeform on their website.

Side‑by‑Side Snapshot (Scores Out of 5)

How to Pick the Right Large Language Model Optimization (LLMO) Software

1. If you need an end‑to‑end control center: Goodie AI or Bluefish AI dominate, with Goodie AI skewing analytics‑first and Bluefish AI skewing campaign‑heavy.
2. If you already pay for a leading SEO suite: Enable the LLMO modules inside Ahrefs or Semrush before adding another subscription.
3. If your budget is tight, but you need instant visibility charts: Peec’s $79 starter is unbeatable.
4. If you care about why models like or dislike you: Evertune’s sentiment deep‑dives are gold.
5. If your goal is simply to flood the data layer: Pair Soar’s Reddit operations with any tracker.

Final Thoughts on the Growing LLMO Software Space

LLMO software is evolving at an incredible speed, especially now with VC money pouring in, but the fundamentals stay constant: monitor how models mention you, understand why they do it, and feed them cleaner, higher‑authority signals than your competitors.

Pick a platform (or combination of tools and services) that closes all three loops and start optimizing now—while your competitors are still arguing about keyword density.

The post The Marketer’s Guide to the Best LLMO Software [2025] appeared first on NoGood™: Growth Marketing Agency.

This guide breaks down what GEO actually means, how to measure performance, and which GEO tools are leading the pack. Whether you’re a marketer, founder, or SEO pro, we did the heavy lifting so you don’t have to – here’s everything you need to navigate this next chapter of search.

What is Generative Search Optimization (GEO)?

GEO involves optimizing your content to appear prominently in responses generated by AI search engines. Unlike traditional SEO, which targets rankings in search engine results pages, GEO ensures your content is cited or mentioned within AI-generated responses.

GEO focuses on:

• Improving clarity, consistency, and accuracy of content.
• Ensuring your brand is consistently referenced by AI models.
• Crafting content optimized for AI’s contextual understanding.
• Building a website optimized for AI crawlers and agents.

Goodie AI recently published a study, the AEO periodic table on AI search visibility ranking factors, that offers insights into the key variables that impact GEO.

How to Measure GEO Performance

Here are key GEO metrics to monitor:

• AI Visibility Score: Frequency of your brand or content appearing in AI-generated responses.
• Source Citations: How often AI platforms reference your content.
• Competitive Benchmarking: Comparing your visibility against competitors.
• Sentiment Analysis: Ensuring AI accurately and positively portrays your brand.

Tracking these metrics provides insights into your GEO strategy’s effectiveness and helps identify areas for improvement.

Overview of the Market for GEO Tools

Features

Many of the tools we reviewed offer visibility monitoring features, but some are more detailed and extensive than others. The main methodology behind measuring this centers on analyzing multiple prompts and providing insight about a brand’s visibility or lack thereof in the responses to those prompts.

Optimization Capabilities

Most of the current players lack strong optimization capabilities. While they provide helpful insights, those insights are not always coupled with actionable optimization opportunities. Some, however, are more advanced in that regard than others.

Pricing

The space is split between enterprise tools and small business light tools. The light tools are not as accurate and tend to only focus on the visibility aspects of GEO. We found that the enterprise tools like Goodie, Scrunch, and Profound have more capabilities and offer more value, but are also more expensive.

GEO Market Dynamics

There are many new emerging players, so we’ll continue to update this list as the market changes.

New York City houses many of the current players and is rapidly becoming the GEO capital of the world.

How to Choose the Right GEO Tool

When selecting a GEO tool, consider:

• Platform Coverage: Support for major AI platforms (ChatGPT, Perplexity, Google Gemini).
• Real-Time Monitoring: Quick updates and alerts on brand mentions.
• Actionable Insights: Clear guidance on content optimization.
• Integration: Compatibility with existing SEO and analytics tools.
• Ease of Use: Intuitive dashboards and straightforward data interpretation.
• Pricing and Scalability: Transparent pricing and flexibility to scale with your needs.

Top 9 Generative Search Optimization Tools in 2025

1. Goodie AI

Description: Goodie AI is a pioneering AI native platform specializing in Generative Engine Optimization. It helps brands enhance their visibility across AI answer engines like ChatGPT, Gemini, and Perplexity.

Pricing: They offer multiple tiers for startups and enterprises; the exact pricing structure is available upon request.

Founded: 2023

Headquarters: New York City, USA

GEO Features

• AI Visibility Monitoring: Tracks brand visibility and mentions across various LLMs and AI search platforms.
• AI Optimization Hub: Identifies opportunities and provides actionable recommendations to improve AI search visibility.
• AI Content Writer: Identifies gaps and assists in creating content optimized for AI search engines.
• AI Search Analytics & Attribution: Measures and attributes your AI search performance back to the metrics that matter for your business, whether that be user growth or revenue growth.

Pros

• Comprehensive suite of tools tailored for AI search optimization.
• User-friendly interface with real-time analytics.
• Measurement for most major AI platforms, including DeepSeek and Claude.
• Access to an Optimization Hub that’s highly precise and provides actionable GEO insights for each tracked source.

Cons

• As a newer platform, it may lack some integrations with existing marketing tools.
• The tool doesn’t have a report export feature (yet).

2. Semrush

SEMrush recently launched its GEO tool, and it’s currently only available to enterprises. They came late to this GEO tool race and primarily focus on the visibility aspect of things. That being said, they’re the leading traditional SEO tool with massive data capabilities and a large customer base.

• Key Features: Visibility analysis, robust keyword research, competitive analysis, and AI result monitoring on Google.
• Pros: Extensive suite, reliable, user-friendly.
• Cons: Limited to Google-centric AI features, indirect GEO insights.
• Pricing: Starts at $120/month.
• Founded: 2008
• Headquarters: Boston, USA

3. Scrunch AI

Scrunch AI delivers a comprehensive enterprise-focused approach to monitoring and optimizing brand visibility within AI search ecosystems. In a world where users want answers instead of links, Scrunch AI offers a platform to help businesses give users what they’re looking for while boosting your brand’s visibility.

• Key Features: Real-time visibility monitoring, search journey mapping of AI-driven customer interactions, proactive hallucination and misinformation detection.
• Pros: Extensive insights, ideal for large enterprises.
• Cons: Not suitable for small businesses, no self-service option available.
• Pricing: Custom pricing offered upon request.
• Founded: 2022
• Headquarters: Salt Lake City, USA

4. Peec AI

Peec AI simplifies AI analytics with a straightforward approach, ideal for small-to-medium businesses and marketing agencies. Peec AI’s versatility makes it applicable across multiple industries such as healthcare, finance, and marketing. Peec’s dashboard is also user-friendly, making it a great choice if you aren’t an expert in AI platforms.

• Key Features: AI visibility metrics, source citation tracking, competitive benchmarking.
• Pros: Easy to use, clear analytics, affordable.
• Cons: Basic feature set, lacks advanced options like in-depth sentiment tracking or optimization recommendations.
• Pricing: From €120/month.
• Founded: 2023
• Headquarters: Berlin, Germany

5. Otterly AI

Otterly AI is an intuitive and accessible AI search tool designed for monitoring and optimizing your generative engine presence. It’s particularly suitable for smaller businesses and startups that are primarily focused on the monitoring side of the equation.

• Key Features: AI visibility tracking, sentiment analysis, and monitoring link citations.
• Pros: User-friendly interface, affordable, good for beginners.
• Cons: Limited recommendation capabilities, basic analytics.
• Pricing: Starts at $49/month.
• Founded: 2023
• Headquarters: Vienna, Austria

6. Profound

Profound provides a wide range of in-depth GEO analytics specifically tailored to large, global enterprises. One of the early movers in the space, they raised a seed round for Khosla Ventures and South Park Commons, among other notable VCs.

• Key Features: AI visibility tracking, detailed conversation and sentiment analysis, proactive crawler optimization.
• Pros: Highly detailed and robust analytics, multilingual support.
• Cons: Only suitable for enterprises, lacks SEO and content features, and is premium-priced.
• Pricing: Custom pricing upon request. For more information about pricing structure, reach out to Profound’s sales team.
• Founded: 2024
• Headquarters: New York City, USA

7. Hubspot’s AI Search Grader

HubSpot’s AI Search Grader is designed to evaluate content’s search engine performance, providing insights and generative suggestions to help content rank better. It combines AI-driven analysis with SEO expertise, giving users a powerful tool to improve their content strategy.

Instead of just focusing on traditional SEO metrics, it also evaluates the semantic relevance of your content. This includes how well your content aligns with what AI-powered search engines understand in terms of context and user intent.

• Key Features: Provides suggestions to optimize content for better rankings within AI search, compares your content to competitors, and evaluates for conversational AI.
• Pros: Excellent for improving content visibility within AI search.
• Cons: Doesn’t account for all of the AI search algorithms, lacks deeper customization abilities.
• Pricing: Part of HubSpot’s Marketing Hub. Starts at $23 a month and ranges up to $3,200 depending on your subscription tier.
• Founded: This tool was launched in 2023.
• Headquarters: Cambridge, MA.

8. Athena HQ

Athena HQ is an AI search visibility platform designed to assess and enhance the search engine performance of your content. Leveraging advanced AI algorithms, Athena HQ offers in-depth analysis of your website’s visibility, providing actionable insights and tailored suggestions to help your content rank higher in search results. Athena HQ’s tool also evaluates semantic relevance, which determines how well-aligned your content is within the given context.

• Key Features: AI-driven content analysis, semantic relevance evaluation, SEO recommendations, insights into user intent, content performance tracking.
• Pros: Can easily be integrated with other SEO tools and an easy-to-understand reporting dashboard.
• Cons: Limited customization capabilities for niche industries, doesn’t offer a complete GEO/SEO solution.
• Pricing: $400 for the lite version and $900 for their growth version. Also offers an enterprise version for custom pricing options.
• Founded: 2025
• Headquarters: San Francisco, CA

9. Ahrefs

Ahrefs hasn’t really launched anything that is GEO-specific yet, but they provide powerful SEO tools crucial for building and maintaining site authority, indirectly supporting GEO effectiveness. They’re a key player that we cannot ignore in our research, but they aren’t a true GEO tool yet, so we’ve ranked them at the bottom of the list for now.

• Key Features: Comprehensive backlink analysis, site and content auditing.
• Pros: Excellent for improving overall site credibility.
• Cons: Does not directly track generative AI mentions or visibility.
• Pricing: Starts at $99/month.
• Founded: 2010
• Headquarters: Singapore

Generative Search Optimization Tools: FAQs

Final Thoughts

As AI search continues to grow, leveraging these emerging GEO tools to monitor and optimize your brand visibility will be critical. Choose tools aligned with your business size and goals, focusing on clear metrics and actionable insights. Starting with GEO today ensures your content remains visible, authoritative, and relevant as our world continues to be revolutionized by AI.

The post 9 Best Generative Search Optimization Tools (GEO Tools) for 2025 appeared first on NoGood™: Growth Marketing Agency.

How Is AI Search Visibility Measured?

We’ve published an AEO study that dives deep into factors impacting AI search visibility. Some of the top critical determining factors that we found affect how AI platforms decide who shows up in AI search results are:

• Content Quality & Depth: AI search engines look for high-quality, informative, and comprehensive content that satisfies the user’s need for an answer. Content must be clear, detailed, and well-structured.
• Credibility & Trustworthiness: AI search engines prioritize sources with authority, valuing third-party validation such as recognized credentials (certifications, awards) and references from authoritative sources like government websites, academia, and high-authority publishers.
• Content Relevance: AI search engines aim to match content with user intent. If your content answers the specific questions or addresses the queries users are typing, it will rank better.
• Citations & Mentions: AI models prioritize sources cited by reputable publishers like Wikipedia, academic journals, and trusted news outlets. Given LLMs’ reliance on real-time retrieval from reliable sources, securing relevant citations from trusted industry sources is key for brands and publishers aiming to boost AI visibility.
• Topical Authority & Expertise: AI models favor content from subject-matter experts with a niche focus, often prioritizing well-known industry leaders. Publishing deep insights in a specific field enhances visibility across AI models.

And those are just the top five factors out of the fifteen that impact visibility in AI search engines. For the entire list of ranking factors and a deeper dive into each, check out our study.

Things to Consider When Selecting an AEO Tool

AI Functionality

AI functionality refers to how well a tool leverages artificial intelligence to understand and enhance content for AI search engines. Look for tools that go beyond generating content. The right tool should be able to analyze search intent, identify trends, and tailor your strategies to meet the needs of AI search algorithms. Additionally, the tool should provide real-time insights to inform ongoing adjustments to keep up with evolving search behaviors and algorithm changes.

Content Optimization for AI Search Engines

Content optimization for AI search engines like ChatGPT or Google Gemini focuses on refining content to meet the unique demands of these platforms. AI search engines prioritize direct, relevant answers to user queries, so optimization in this context isn’t just about crafting content; it’s about ensuring the content aligns with AI algorithms that prioritize clarity, specificity, and relevance.

Tools that excel in this area help you structure content to enhance visibility and search ranking, ensuring your content is properly optimized for AI search engines’ expectations.

Ease of Use

User experience and how easy it is to implement optimizations are important. A tool that’s intuitive, with a simple interface and quick setup, allows for faster adoption and better results.

Comprehensiveness of the Toolset

Tools that offer a full suite of features for both content creation, optimization, and AI search engine integration will rank higher. This includes keyword research, content analysis, topic discovery, SEO audit features, and performance tracking.

Adaptability to AI Search Trends

We are at the mercy of AI search engines, and they’re evolving every day. These tools need to be proficient in adapting to those changes with frequent updates. Tools that stay on top of trends and continuously update their features to match the latest in AI search will provide long-term value.

The Best AEO Tools in The AI Search Space in 2025:

1. Goodie AI

Goodie AI is a cutting-edge, AI-native platform that specializes in optimizing your website for generative engines. But that’s just putting it mildly. We leverage advanced algorithms to help brands amplify their visibility and improve their interactions across a range of AI answer engines, including ChatGPT, Gemini, and Perplexity.

Our powerful optimization tools ensure that brands can effectively reach their target audience, improve user engagement, and stay ahead as consumers do their research through AI.

Pricing: Different pricing tiers are available for both startups and large-scale enterprises. More details on the pricing structure are available upon request.

Founded: 2023

Headquarters: New York City, USA

Key Features

• AI Visibility Monitoring: Tracks your brand’s visibility and mentions within top LLMs and AI search programs. Goodie then scores your visibility within different answer engines.
• Sentiment Analysis: See how AI search engines perceive your content to ensure it resonates with both your audience and AI search algorithms.
• AI Search Analytics and Attribution: Goodie AI tracks and evaluates your AI search performance, linking it directly to the key metrics that drive your business, whether that’s user growth, revenue increase, or other crucial business outcomes.
• AI Content Writing: You can plug in your ‘Author Stamp’ to effortlessly generate content that aligns with your company’s branding and your tone of voice. Unlike other content tools that regurgitate the same information over and over, Goodie AI helps you quickly create content optimized for AI search engines without sacrificing quality.
• AI Optimization Hub: Offers recommendations to improve your website’s AI search visibility, along with an AI Visibility Impact (AVI) report that highlights which pages are driving the most visibility and which ones need further optimization.

Pros

• By optimizing your website for AI search visibility, Goodie AI helps you invest in a future-proof strategy, which could yield higher returns as AI search continues to grow in importance.
• The tool’s ability to analyze AI visibility can help inform your broader marketing strategy, providing insights into how your content is being perceived and indexed by modern search engines.
• Instead of spending hours on manual audits or guesswork, your marketing team can use Goodie AI to streamline the process of AI search optimization, freeing up time to focus on creative or strategic tasks.
• The insights provided by the tool give you evidence-based guidance, which can reduce uncertainty and help you feel confident in your optimization decisions.
• With clear reports and recommendations, Goodie AI can help different departments (like SEO, content, and growth) align on optimization goals, leading to better collaboration across your team.

Cons

• As a newer platform, Goodie AI is continually enhancing its capabilities. Some advanced features may still be under development.
• The platform does not yet provide a feature to export reports.
• Some of Goodie AI’s advanced functionalities may require a learning period. Users new to AI search optimization might need to invest time in understanding and utilizing these features effectively.

2. Evertune

Founders Ed, Poul, and Brian launched Evertune in 2024 as a tool to give businesses the insights they need to navigate the landscape of answer engine optimization. The founders were inspired by a key issue they experienced in their own individual work: inputting the same questions into various LLMs would generate very different responses depending on the platform. Evertune was created to provide what they call “the AI brand index.”

Pricing: Pricing information not publicly listed.

Founded: 2024

Headquarters: New York City, New York

Key Features

• Sentiment & Perception Analysis: Analyzes tone (positive/negative/neutral) in AI responses mentioning your brand and reveals how your brand is perceived compared to your competitors.
• Competitive Benchmarking: Shows where your brand ranks in AI-generated recommendation lists and queries, and identifies which brands are getting more favorable exposure.
• Actionable Insights & Recommendations: Provides specific guidance on improving content, SEO, and public perception in AI models.
• Brand Monitoring: Understands how frequently and in what context your brand appears.

Pros

• Helps businesses prioritize where to focus SEO/content creation efforts.
• Evertune can help identify inaccuracies or gaps in how AI models describe your brand, allowing you to correct AI-driven misinformation.
• By analyzing how competitors are portrayed, you gain insights into their content strategies or perceived brand strengths.

Cons

• Evertune only monitors AI-generated outputs, not traditional web, social, or ad channels.
• Being newly launched, adoption proof may still be developing. Currently, Evertune doesn’t provide much information about its capabilities, nor do they name companies leveraging the tool in their strategies.

3. Scout by Yext

While Yext’s origins were grounded in providing search and social media tools, they’re stepping into the AI search visibility pond with Scout. While the tool is still pending launch, the prospect of a centralized solution for tracking brand performance, analyzing competition, and executing improvements is promising.

Pricing: Pending launch, pricing information not yet available.

Founded: Scout was launched in 2025.

Headquarters: New York City, New York

Key Features

• Channel Integration: Many AI search visibility tools don’t offer full-scale digital marketing capabilities. Scout’s integration with Yext offers a wider point of view across your whole digital marketing strategy.
• Hyper-Local Insights: The tool offers detailed analysis at various geographic levels, enabling brands to tailor strategies for specific markets.
• Competitive Analysis: Provides insights into competitors’ strategies and performance, aiding in strategic planning.​

Pros

• A unified platform that connects with the rest of Yext to provide full visibility across all marketing channels it supports.
• Delivers prioritized AI optimization recommendations that users can immediately integrate within the tool.
• Suitable for multi-location brands, offering insights at national, regional, and local levels.

Cons

• Hasn’t opened to the public. Proof of value for various niches is to be determined.
• While Scout provides visibility into LLMs like ChatGPT and Gemini, Scout can’t fully explain why a result ranks the way it does.
• Full functionality is tied to the overall Yext ecosystem. If you have other tools in your marketing stack, implementation may be difficult.
• While not exclusive to retail or brick-and-mortar, Scout is more suitable for brands with many physical locations (e.g., restaurants, banks, service chains).

4. Semrush

While Semrush’s AI Toolkit arrived later in the game than some competitors, they’ve come out with a solution designed to help brands monitor, analyze, and enhance their presence across AI search platforms such as ChatGPT, Perplexity, Gemini, and Claude. The dashboard provides real-time insights into brand mentions, sentiment, and competitive positioning within generative searches.

Pricing: Semrush’s AI Toolkit is available to all Semrush users and costs $99/month per domain. Each new domain requires a separate subscription.

Founded: 2008

Headquarters: Boston, Massachusetts

Key Features

• Brand Monitoring: Offers extensive coverage of AI search platforms, enabling brands to track mentions, assess sentiment, and identify visibility trends across multiple channels.
• Analysis: The platform allows for detailed analysis at various levels, from overall brand performance to specific products or topics, facilitating precise tracking of sentiment shifts and source credibility.
• Customizable Reporting: This solution includes customizable reporting features, enabling teams to create and share targeted AI search performance reports tailored to their specific needs.

Pros

• While the AI toolkit provides an overview of brand sentiment and visibility, it doesn’t provide more in-depth analysis into why your brand performs the way it does in AI searches.
• The platform delivers up-to-date information on brand mentions and sentiment, enabling timely and informed decision-making.

Cons

• Semrush’s platform is built for one user at a time. If you want your entire team to be able to access the tool, you’ll need to enroll and pay for additional seats for other users.
• You’ll need a Semrush subscription to add on their AI Toolkit feature for an additional $99 per month.
• While the tool is capable of generating content recommendations based on common related queries, it doesn’t offer additional assistance throughout the writing process.

5. Scrunch AI

As consumer behavior shifts toward seeking direct answers rather than clicking through links, Scrunch AI argues that your most important website visitors are now AI bots scanning your content. While Scrunch AI offers a comprehensive monitoring dashboard, it lacks the additional features provided by other tools on this list.

Pricing: Pricing information is available upon request.

Founded: 2023

Headquarters: Salt Lake City, Utah

Key Features

• AI Search Audit: The tool completes a comprehensive audit of your website to understand how AI crawlers interpret your content. It then identifies key areas of improvement where crawlers might be misinterpreting.
• AI Search Visibility Monitoring: The platform refreshes every three days, so you can stay informed on how you’re showing up in AI search results.
• Knowledge Hub: Their refined report identifies discrepancies between your website’s content, third-party information, and AI-generated outputs like Google’s AI Overviews to identify information gaps and inaccuracies.
• Journey Mapping: Scrunch presents visualizations of how AI agents and crawlers perceive your business so you can optimize how you target each stage of the customer journey from an AI perspective.

Pros

• Identifies information gaps, misinformation, and outdated information, enabling users to enhance their content to be more relevant and accurate for AI evaluation.
• Track your brand’s performance against competitors in AI responses.
• Easily find which changes will be the most impactful on your website’s AI search visibility.

Cons

• Scrunch AI’s services are tailored for larger organizations, which may limit accessibility for smaller businesses seeking similar solutions.
• Some advanced features may require users to invest time in understanding and utilizing them effectively, especially for those new to AI search optimization.
• There are constraints in tailoring the tool’s insights and recommendations to fit specific business needs/industries.

6. Otterly.AI

Otterly.AI was built around the question, “Is your business ready for AI?” Their tool aims to get you there. They offer brand, content, and competitor monitoring within LLMs. Otterly claims their tool is the Semrush for AEO.

Pricing: Lite version goes for $29 per month, the standard model $189 per month, and the pro tier $989 per month.

Founded: 2023

Headquarters: Mountain View, California

Key Features

• AI Search Monitoring: Automatically tracks brand mentions and content visibility across major AI search platforms, offering real-time insights into how your brand is perceived in AI-generated responses.
• Keyword Research: Assists in identifying conversational keywords and prompts that audiences are searching for on AI platforms, aiding in the development of targeted AEO and content marketing strategies.
• Link Tracking: Provides automated tracking of all link citations on AI search platforms, helping you understand your website’s positioning and discover opportunities for improvement.
• Sentiment Analysis: Gain insights into how frequently your brand appears and the sentiment associated with it across AI search platforms, enabling you to assess and enhance your brand’s reputation.

Pros

• Offers detailed analytics on brand presence, sentiment, and link tracking, providing a holistic view of your brand’s performance across AI search platforms.
• Designed with ease of use in mind, allows users to navigate features effortlessly.

Cons

• Otterly.AI was built for ChatGPT, Google’s AI Overviews, and Perplexity, which may limit its optimization capabilities for other AI platforms.
• While the tool excels at improving visibility in AI searches, it doesn’t extend its expertise to traditional and local SEO.
• Lacks border content capabilities like content creation and writing assistance. Can only optimize metadata elements.

How to Effectively Use an AEO Tool

1. Focus on Search Intent and Conversational Queries

Whichever tool you go with, ensure you prioritize answering these queries correctly, concisely, and in a conversational format. When performing keyword research using AEO tools, focus on long-tail keywords and conversational queries that your audience is searching for. Most AI search engines and voice assistants use content that directly answers these questions.

2. Create Structured Data and Snippet-Friendly Content

Many AI search engines rely on structured data to pull rich snippets. Using AEO tools or Google’s Structured Data Markup Helper, you can reformat your content to make it easier for AI to interpret and present in results. Additionally, they can identify the content that’s currently ranking so you can make further improvements.

3. Analyze Your Competitor’s Content

Most of these tools allow you to track how your content compares to the competitors in your industry. Analyze the top-ranking content for keywords and optimize your content based on your findings. This can include the format, structure, tone, and topics AI search engines favor.

4. Continuously Monitor and Optimize

AI search engines change all the time, so continuous monitoring of your content’s performance is crucial. Use these tools to track visibility changes, adjust keywords, and re-optimize your content to adapt to changing AI algorithms and user search behaviors. A good tool offers real-time feedback so you can quickly adjust and improve.

5. Improve Content Quality

While AEO tools are experts in technical optimization, their writing skills are still being workshopped. Always review and revise AI-written content with high-level expertise. AI search engines reward content that’s comprehensive, valuable to users, and well-researched. Focus your efforts on creating authoritative, user-friendly content that thoroughly answers user queries.

Empower Your AI Search Visibility

If your business hasn’t implemented an AEO strategy yet, now is the time to get started. . The right AEO tool will help you meet the evolving demands of AI search algorithms so you can enhance your brand’s viability and authority.

See how Goodie can revolutionize your current AEO strategy or help you catch up so you don’t continue falling behind.

AI Search Visibility: FAQs

The post Top AEO Tools to Boost Your AI Search Visibility appeared first on NoGood™: Growth Marketing Agency.

While creating content for AEO is becoming more manageable, the bigger challenge lies in answering one crucial question: How do you measure the performance of your AEO efforts? Unlike traditional SEO, where rankings and traffic data are straightforward and consistent, AEO operates in a probabilistic space—the same question might yield different answers across different AI platforms, or even from the same model. These answers are often non-clickable, making performance tracking less direct and far more nuanced.

In this blog, we will break down exactly how to track and evaluate your AEO initiatives—what to measure, why it matters, and how to do it well.

AEO vs SEO Goals

With AEO, the goal isn’t necessarily to drive instant traffic or direct conversions. Instead, it’s about brand discovery and presence within AI-generated responses.

Why is this important? Because users often don’t go beyond the initial AI-generated answer. There’s no need to click a link, scroll through pages, or open multiple tabs. The AI delivers the entire response in one go. And yet, this interaction can significantly influence user behavior.

Even without a click, an AI-generated brand mention:

• Builds brand awareness
• Establishes credibility through AI’s “endorsed” answer
• Influences future searches, conversations, and buying decisions

Example: If your brand is listed in ChatGPT as a top tool in your industry, that mention may not lead to immediate traffic, but it increases the chances of users later searching for your brand, visiting your site, or converting through another channel.

Why don’t traditional SEO metrics apply to AEO? In SEO, rankings, impressions, and click-through rates provide a clear map of performance. These are based on deterministic systems—if your site ranks #1 today, chances are it still ranks #1 tomorrow, unless something significantly changes.

AEO doesn’t work like that. LLMs rely on probabilistic models, meaning:

• Responses vary based on the model’s training and external web sources
• Results aren’t fixed or consistent
• Answers may not include clickable links at all

That’s why AEO performance must be evaluated differently. You’re not tracking just traffic or rankings—you’re measuring presence, prominence, sentiment, and how those influence user behavior over time.

What AEO Performance Metrics Should You Track?

Here are 3 types of metrics that can help you evaluate AEO performance meaningfully:

1. Visibility Metrics (Primary metric)

Visibility is the foundation of AEO. These metrics help you assess how often and how prominently your brand is being included in AI-generated answers.

• Brand visibility score in ChatGPT, Gemini, Perplexity, etc: This reflects how frequently your brand is mentioned in AI responses relevant to your category. For example, if you ask Gemini, “What’s the best task management tool for teams?” and your brand is listed in the top three, that’s a win, even if it doesn’t lead to a click.
• Position or priority in LLM answers: Are you mentioned first, last, or somewhere in the middle? Higher placement often reflects stronger brand authority. Being listed in a response should be considered a win.
• Sentiment in brand mentions: LLMs learn from online content, including reviews (e.g., G2, Capterra), discussions (e.g., Reddit), and articles. Positive sentiment in these sources makes favorable mentions more likely in AI answers.

2. Engagement Metrics

Much of the brand discovery and evaluation happens within the AI interface itself, before a user ever visits your website. As a result, while your overall website traffic may decrease, traffic quality is likely to improve. Users who do click through have already done a fair amount of research through AI and are coming in more informed, curious, and ready to engage. This often translates to higher-quality sessions on your site.

To measure this effectively, focus on the following engagement metrics:

• Bounce rate: Are visitors sticking around or leaving right away?
• Average time on site: Are they exploring your content in depth?
• Pages per session: Are they engaging across multiple pages?

Engagement metrics tend to improve when your brand’s visibility and positive sentiment for the brand increase. For example, if Nike sees a rise in brand visibility for “best running shoes” across AI searches, they should also expect improved engagement on their running shoe product pages—across all traffic channels, including organic, direct, and paid.

3. Conversion Metrics

Although AEO primarily plays a role at the top of the funnel, it’s important to track its impact on downstream conversions. You may have noticed that many AI platforms—like ChatGPT—are now starting to appear in your website analytics as referral sources, often tagged with UTM parameters such as utm_source=chatgpt.com. As traffic from these LLM-powered platforms grows, you may begin to see a lift in bottom-of-funnel metrics, including:

• Actions initiated (form submissions, demo requests, or product views)
• Actions completed (lead forms submitted, purchases made, or demos scheduled)
• Live chat engagements
• Product support inquiries
• Direct revenue generated

The Challenge of Measuring AEO: Rag Is a Strong Influencer of Performance

Retrieval-Augmented Generation (RAG) is a technique used by most leading LLMs to enhance the quality and relevance of their responses. RAG-enabled AI models pull in real-time information from the web to supplement their answers.

Because these models reference external sources dynamically, any changes in the availability, credibility, or ranking of your brand across these sources can directly influence the outcome of an AI-generated response, similar to how Google’s algorithm updates shift the weight given to certain websites over others.

RAG allows LLMs to:

• Actively retrieve live data from the internet at the time of the query
• Combine it with their existing training knowledge
• Generate fresh, contextually rich responses using high-authority sources

Example: Ask Perplexity, “What are the top CRM tools for nonprofits?” and it might reference current blog posts, software comparison sites, and industry articles to compile its answer. If your brand is mentioned frequently in those sources, it increases your chances of being included.

Manual vs. Automated AEO Performance Tracking

When it comes to tracking your brand’s presence in AI search, there are two primary approaches: manual audits and automated tools. Unlike the Google search ecosystem, where we have tools like Keyword Planner, SEMRush, or Ahrefs to help us with search performance over time, similar reporting metrics are still a black box for the AI search ecosystem.

1. Manual Tracking: Hands-On Learning

Manual tracking typically involves running periodic queries across AI platforms like ChatGPT or Gemini to observe how and where your brand appears. This includes:

• Asking the same set of queries each week to check for consistency
• Noting any changes in phrasing, ranking, or brand mentions
• Reviewing the sentiment and context in which your brand is mentioned

Example: Maintain a simple spreadsheet tracking whether your brand shows up for queries like “best CRM for small businesses” or “top productivity tools for remote teams” every week across key AI search platforms.

While manual audits can be time-consuming, they offer significant value in today’s rapidly evolving AI landscape. These hands-on checks provide deeper insight into how different models interpret and represent your brand—nuances that automated tools may still miss. Though not easily scalable, manual analysis remains a powerful complement to automation and should be part of your routine, even if you already use other tracking tools.

2. Automated Tracking: Scalable & Consistent Monitoring

As the space matures, tools like Goodie AI have emerged to help brands monitor their AI search performance at scale. These platforms offer features such as:

• Brand visibility scores across ChatGPT, Gemini, Perplexity, and others
• Sentiment and positioning analysis
• Competitor benchmarking
• Topic-level reporting tied to your business objectives
• Optimization suggestions to improve inclusion and prominence

Because AEO is about visibility rather than traffic, these tools focus on discoverability metrics—measuring how often, and how favorably, you appear in AI-generated answers.

Goodie in Action: AEO Power Tool for Modern Brands

Goodie AI is a leading platform built specifically for AEO tracking and optimization. It offers a full suite of features to support both measurement and improvement:

• Real-time tracking of brand mentions across AI search engines
• Sentiment analysis across LLMs like ChatGPT, Gemini, Claude, Perplexity, and Deepseek
• Dual optimization strategies for both AI and traditional SEO
• AI-driven content recommendations to close visibility gaps
• Traffic and conversion attribution from AI interactions

💡 Why it matters: In a probabilistic AI environment where exposure = influence, tools like Goodie AI helps you track what matters—and take action to stay ahead.

As AI-driven search continues to evolve, the brands that adapt early—and measure smartly—will lead the way. Traditional SEO metrics no longer tell the whole story. AEO demands new tools, new KPIs, and a new mindset.

Your brand doesn’t need to “rank”—it needs to be recognized, recommended, and remembered. Tracking visibility, sentiment, and brand presence across LLMs is no longer a “nice to have”—it’s a competitive necessity.

The post How to Measure Performance of Answer Engine Optimization (AEO) appeared first on NoGood™: Growth Marketing Agency.

This is your blueprint for staying visible, clickable, and trusted across Google’s evolving AI ecosystem.

Ranking Factors for AI Mode & Google Search Experience

How AI Mode Works

AI Mode is powered by a customized version of Gemini 2.5, Google’s most intelligent available model. This model is capable of thinking and reasoning through an in-depth thought process before responding, resulting in enhanced performance and improved accuracy.

• AI Overviews + AI Mode use query fan-out: They break a single question into many sub-queries to find the most relevant, high-quality information across the web.
• Deep Search can generate expert-level, cited reports, favoring multi-source, semantically rich pages.
• Real-time multimodal capabilities: AI Mode integrates live visual search through Project Astra, allowing users to have conversations about what they see using their camera.

Content Strategy for AI Overviews & Deep Search

Google’s shift to AI Overviews and Deep Search fundamentally changes how your content is discovered and cited. Instead of returning a list of links, Google is now using Gemini to interpret intent, break queries into subtasks, scan hundreds of sources, and synthesize a cohesive answer.

This changes your job from “get ranked” to “get cited.”

What Google AI is Optimizing For

1. Relevance and specificity
2. Originality and depth of insight
3. Authoritativeness, especially from niche experts
4. Semantic alignment with related subqueries

How to Optimize Your Content

Write for real human intent: Don’t just target keywords. Reverse-engineer full user prompts and sub-questions using forums like Reddit, Quora, and People Also Ask to understand how people phrase real questions.

Structure for skimming AND synthesis: Use subheadings, bullet points, summaries, and data boxes to make it easy for Google to extract meaningful text chunks.

Go deeper than summary-level advice: Offer frameworks, decision matrices, pricing breakdowns, firsthand experiences, expert quotes, or user-generated insights. Additionally, including expert quotes and firsthand experiences improves your brand authority and expertise.

Answer the follow-up question: If a user asks, “Is creatine good for runners?”, include the science behind it, usage tips, side effects, alternatives, and expert opinion. This way, you can cover both the explicit and the implicit user intents.

Create data-backed, expert-level content: AI Overviews are more likely to link to content with citations, named authors, data sources, and external references.

Technical AEO for AI Mode Retrieval

Technical accessibility is the price of admission. Googlebot and Gemini need to find, access, and interpret your content without friction.

Top 7 Must-Haves

1. Pages return 200 status codes (avoid soft 404s, 3xx redirects, or errors)
2. No robots.txt blocking Googlebot or Google-Extended
3. Meta tags like noindex, nosnippet, or data-nosnippet are used intentionally
4. Consistent canonical tagging to prevent duplicate content conflicts
5. Mobile-first design that loads fast and works across screen sizes
6. Proper use of headers (<h1> to <h3>) to reflect content hierarchy
7. Descriptive titles and meta descriptions that align with user intent and long-tail queries

Bonus Tips for AI performance:

• Semantically structured content with NLP-friendly phrasing
• Longer-tail topic pages that cover a full topic cluster rather than just a single keyword
• Embedded answers to FAQs, comparisons, and pros/cons to help LLMs extract structured information

Structured Data & Multimodal Optimization

AI Mode and Overviews rely heavily on structured data to surface and summarize rich content. As Google adds capabilities like visual live chat, virtual try-ons, and financial data graphs, structured data becomes essential.

Structured Data Best Practices

Structured data implementation provides search engines with the context they need to properly understand and showcase your content. Implement schema.org types that align with your specific business needs, including:

• Product markup for e-commerce sites,
• Service schema for professional offerings,
• Article markup for blog content,
• FAQPage schema for support sections,
• Event markup for upcoming activities,
• LocalBusiness for location-based services
• Recipe schema for food-related content etc.

Quality control is essential once you begin implementing structured data across your site. Validate every markup addition using Google’s Rich Results Test to ensure proper implementation, and maintain strict consistency between your schema content and what visitors actually see on the page. Mismatches can result in search engine trust penalties.

Enhance your credibility signals by including author, datePublished, and publisher information wherever applicable. These elements help establish authority and can positively impact how your content performs in search results.

Tips for Multimodal Search Optimization

• Use original images with descriptive, natural-language alt text
• Compress and size images for fast loading
• Add short-form video explainers or walkthroughs
• Upload video transcripts for better semantic extraction
• Keep Google Merchant Center product data and Business Profile details up-to-date and optimized

This matters more now that AI Mode includes Search Live, which lets users point their camera at an object or visual and ask live follow-up questions.

AI-Generated Content Guidelines & Trust Signals

Using AI to write content is not banned. But using AI to flood Google with thin, low-effort pages is grounds for a penalty under their scaled content abuse policy.

Follow These 4 Key Principles

• Don’t mass-publish similar pages that differ only by city/product/category name
• Add real human context and editing to every AI-assisted piece
• Disclose automation where relevant, such as:
  • Author notes
  • Image metadata (using IPTC DigitalSourceTypeTrainedAlgorithmicMedia)
  • Page footers with transparency disclaimers
• Use generative AI to structure, not replace, human creativity. Think: first draft assistant, not auto-publisher.

Metadata and Markup Hygiene

Every page requires custom <title> and <meta description> tags that accurately represent its content and purpose. Your structured data should be descriptive and consistent across all implementations while staying compliant with search engine policies to avoid penalties.

Enhance your markup with additional context signals where relevant, including isAccessibleForFree, inLanguage, and contentLocation tags. These additional data points help search engines better understand and categorize your content for more precise targeting.

How to Measure Performance Beyond CTR

Clicks are a fading metric in the AI search era. Google says AI Overviews drive “higher-quality clicks,” but volume is down and continuing to decrease

Key Metrics

• Conversion rate (form fills, purchases, subscriptions)
• Session depth (pages per visit)
• Time on site and bounce rate
• Return visits and retention
• Lead quality, not just quantity

Tools

• GA4 + Looker Studio dashboards for engagement segmentation
• Heatmaps (Hotjar, Microsoft Clarity) to analyze content friction
• First-click vs. last-click attribution modeling to evaluate AI-driven journeys
• Server-side tagging to capture more reliable data, especially with AI agents acting on behalf of users
• AEO platforms like Goodie AI to monitor visibility, measure performance, and take actions across AI search platforms

Next-Level Tactics for Brand Visibility

AI Overviews and AI Mode pull from a wider range of sources than classic Search. Your brand’s presence across the entire web determines your visibility.

Become an Entity, Not Just a Website

Ensure you build consistent profiles across Wikipedia, Crunchbase, LinkedIn, GitHub, YouTube, and press articles. Using a well-rounded strategy mitigates the chances of incorrect information being sourced and improves visibility through presence. Connect identities through sameAs schema and keep your About Page structured, semantic, and detailed.

Use Semantic Saturation

Build strong associations between your brand and high-intent phrases by consistently positioning your brand name close to the terms that matter most to your business. Create comprehensive, glossary-style content that defines and explores the terminology you want to own within your industry space.

Structure your content using hub-and-spoke topic clusters that demonstrate your domain expertise to AI systems. This approach helps large language models understand the breadth and depth of your authority across interconnected topics, making you more likely to be referenced as a credible source.

Get Mentioned in Credible Sources

Expand your digital footprint through strategic outreach to high-authority blogs, industry publications, podcasts, and newsletters within your sector. Position yourself as a thought leader by contributing to guest articles or participating in expert roundups that showcase your expertise to new audiences.

Build citation momentum across platforms where professionals gather and share insights, including Quora discussions, relevant Reddit communities, LinkedIn thought leadership posts, and Substack publications. These mentions create the reference signals that AI systems use to assess credibility and authority.

Optimize for How People Ask, Not How They Search

Instead of optimizing for traditional keyword phrases, design landing pages that directly answer the conversational questions users actually ask AI systems.

Rather than targeting “CRM for small SaaS,” create content that responds to natural language queries like “What’s the best CRM for small SaaS startups in 2025?” Apply this same approach across different content types, such as creating pages that answer “How to get verified on Instagram without being famous” instead of targeting “Instagram verification,” or “Compare budget electric SUVs with over 300-mile range” rather than “electric SUV comparison.”

These natural language patterns reflect how users communicate with AI assistants and search for information in an increasingly conversational search environment.

Future-Proofing: Agentic Search & AI Actions

Google AI Mode is not just answering questions. It’s now doing things on behalf of users:

• Booking reservations
• Purchasing tickets
• Comparing SKUs
• Surfacing local availability and pricing

If You Want Your Business to Be Selected by AI Agents

Make sure to keep your Google Business Profile up-to-date. Ensure you use availability, price, and action structured data. Additional best practices to improve selectability are to integrate with booking and ticketing partners such as:

• Resy
• OpenTable
• Vagaro
• Ticketmaster
• StubHub

Lastly, to automate your process and simplify updates, use a public API or structured feeds so Google can access real-time inventory

Anticipate Changes in User and AI Agent Behavior

Prepare for the shift toward conversational commerce, where users make specific requests like “find me a black turtleneck under $50, size M, in stock near me” rather than browsing product categories.

Similarly, LLM-driven agents will handle complex, multi-step tasks such as “book the closest available dentist for Thursday 4 pm, under $150,” requiring businesses to structure their data and booking systems for AI accessibility.

We’re Already in the Future of Search

LLMs are shaping what users see, trust, and act on. To stay visible and drive organic growth, your content must be designed for humans AND for generative engines.

This means shifting from keyword targeting to intent mapping, prioritizing organic conversions over vanity traffic metrics, and creating structured, original, expert-level content that demonstrates clear authority. Treat technical SEO as your foundational layer while embracing structured data and multimedia formats that AI systems can easily parse and reference. Focus on deeper engagement metrics that reflect genuine user value, and prepare for a world where AI agents increasingly determine which brands get discovered and which get overlooked.

The rules of search are being rewritten, but the opportunity is enormous. The brands that adapt fastest, those who earn visibility through value, clarity, and semantic strength, will dominate the AI-first era of discovery.

The post AI Mode Optimization Guide: Strategies for Google AI Search Experiences appeared first on NoGood™: Growth Marketing Agency.

OpenAI’s rollout of ChatGPT Shopping marks a platform shift in eCommerce. Instead of browsing on Google or Amazon, users are now turning to LLMs like ChatGPT to get personalized, intelligent product recommendations. This guide is for consumer brands, DTC operators, and digital commerce teams ready to optimize for this new AI discovery channel. 

We’ll walk through how ChatGPT Shopping works, what factors drive product visibility, and how to optimize your product data and presence for AI commerce.

Why ChatGPT Shopping Matters for Brands

The launch of ChatGPT Shopping signals a larger shift: AI answer engines are becoming the new front doors for product discovery.

When a user types something like “best dress shoes under $200” or “eco-friendly cookware set for induction stoves,” ChatGPT doesn’t send them to a list of links. It generates direct product recommendations – complete with prices, descriptions, review highlights, and merchant links – right inside the conversation.

No ads. No sponsored listings. No pay-to-play – at least for now.

Product recommendations are based on:

• User intent
• Structured metadata
• Contextual relevance

For brands, this creates an entirely new surface for visibility and performance. Think of it as a new organic channel: AI search commerce. This also means adding a new playbook.

How ChatGPT Shopping Works

When a user enters a shopping-related query like “best lightweight stroller for travel”, here’s how ChatGPT processes it:

1. Intent Analysis

ChatGPT classifies the query as shopping-focused and uses semantic understanding to categorize the user’s need (e.g., comparison, deal hunting, browsing).

2. Context is Matched

If memory is enabled, ChatGPT may use past preferences (e.g., style preferences, price sensitivity). It also incorporates instructions in the query (“under $100,” “for wide feet”) to refine results.

3. Product Data is Pulled

ChatGPT then pulls structured product data from sources like Bing Shopping, Shopify, and merchant feeds. It also gathers reviews from across the web to incorporate into results.

4. AI Generates Product Cards

Each product includes a title, AI-written summary, price, availability, review highlights, and merchant link. Labels like “Top Pick” or “Budget-Friendly” are generated by AI based on review content, not paid promotion and placements.

5. Links Are Offered to Merchant Sites

Users can click to view and buy products directly from merchants. OpenAI doesn’t take a commission or rerank listings based on commercial agreements.

ChatGPT Shopping Might Not Display for Various Reasons

The ChatGPT Shopping feature is still rolling out and might not generate for every shopping query yet. When I asked why ChatGPT shopping didn’t show up for my product query, here are the explanations I got:

What Influences Product Rankings in ChatGPT

There is no pay-for-placement model. Products show up based on:

• Trust & Safety: Products flagged for low quality or lack of social proof may be filtered out.
• Structured Metadata: Title, description, image, brand, SKU, price, availability, GTIN, size, material (the richer and more complete the data, the better).
• Review Signals: Volume and quality of user feedback; summaries are generated by AI. Products with consistent praise or clear pros/cons are prioritized.
• Intent Relevance: Semantic alignment with the query (e.g., “compact,” or “quiet”).
• Schema & Feed Quality: Incomplete or outdated metadata can cause exclusion.

5 Ways to Optimize Your Products for ChatGPT Shopping Visibility

Welcome to the world of AI Search Optimization. Here are 5 essential strategies to get your products ranked and displayed.

1. Allow ChatGPT to Crawl Your Site

Update your robots.txt file to allow OpenAI’s crawler:

User-agent: OAI-SearchBot

Disallow:

Also, implement the llms.txt file to define access for LLM more broadly.

2. Structure Your Product Data Correctly

Whether you’re on Shopify, Magento, or headless, ensure your product feeds include these key attributes:

Use JSON-LD for schema markup. Validate it using Google’s Rich Results Test, but note that AI search engines may require broader attribute coverage than traditional SEO.

3. Use Conversational Copy

AI models prefer clarity. Rewriting titles and PDPs for semantic accuracy improves your chance of getting surfaced.

Examples:

• Instead of “Techwear Pants V3,” use “Waterproof Breathable Hiking Pants.”
• Add FAQ content like: “Are these pants machine-washable?” or “Do they run large?”

4. Leverage Review Sentiment

ChatGPT summarizes real user reviews. Your job is to:

• Ensure your reviews reflect usage and benefits
• Highlight sentiment keywords (“lightweight,” “easy to fold”) in your PDPs
• Get reviews published on third-party sites (Reddit, blogs, YouTube). Broader digital footprint = higher LLM visibility.

5. Measure ChatGPT as a Channel

Attribution is evolving. Here is what you need to start tracking:

• Add UTM tags to product URLs with utm_source=chatgpt.com
• Track GA4 events from ChatGPT traffic
• Include post-purchase surveys asking “How did you find us? (Google, ChatGPT, Perplexity, etc.)”
• Use AI-specific promo codes (e.g., “GPT10”)

The Goodie CHAT Framework™

Our proprietary AEO (answer engine optimization) framework for Shopping Optimization in AI search platforms helps you align with how ChatGPT ranks and recommends products.

Chat Framework Breakdown

BONUS: AEO Periodic Table of AI Search Commerce Visibility Factors

What Product Types Are Winning in ChatGPT Right Now

As of May 2025, ChatGPT Shopping appears strongest in these categories:

• Fashion and footwear
• Beauty & personal care
• Home goods (decor, storage, cookware)
• Consumer electronics & gadgets

These verticals tend to have rich structured data, strong review footprints, and frequent user-driven queries.

What’s next? Expect expansion into:

• Specialty foods
• Health & supplements
• Furniture
• B2B SaaS tools (think: “best CRM for small businesses”)

The Competitive Edge: How to Win in AI Search Commerce

Invest in Metadata Infrastructure

Treat your product data like a core asset. Clean, enrich, and normalize it across every listing. Use tools like:

• Goodie AI for end-to-end AEO, including brand and product monitoring and optimization
• Hypotenuse AI for deep schema review and enhancement
• Feedonomics for managing product feeds
• Contentful or Sanity.io for structured CMS delivery

Build AI-Ready Brand Presence

Your product reviews, mentions on Reddit, presence in YouTube roundups, and media citations feed into how LLMs perceive your brand. AI visibility is not just on your PDPs—it’s everywhere you’re mentioned.

Shift Your SEO Team’s Mindset

It’s no longer about ranking #1 on Google. It’s about being the right answer in a zero-click environment. That requires:

• Understanding how LLMs ingest, interpret, and rerank product data
• Working cross-functionally across product, content, engineering, and PR
• Moving from keyword strategy to intent modeling

How ChatGPT Shopping Compares to Google and Perplexity

To help you decide where to focus your optimization efforts and understand how AI shopping tools compare, here is a deeper look into how ChatGPT, Google Shopping, and Perplexity’s Shop Like a Pro stack up:

What Consumer Brands Should Do Right Now

• Audit your structured product data and schema
• Monitor and build an optimization engine using a native AEO platform
• Join OpenAI’s merchant feed beta
• Reformat PDPs for clarity, benefits, and AI readability
• Train your team on AI search commerce basics
• Build dashboards to track ChatGPT traffic and performance

Where We Go from Here: The Future of AI Search Commerce

We are only in the early innings of AI product discovery. Here’s what the next 12–36 months of AI search commerce could look like:

1. AI-Native Shopping Assistants and Agents Will Become Ubiquitous

Tools like OpenAI’s Operator or Anthropic’s Claude agents will evolve into fully personalized shopping concierges that browse, compare, and buy on your behalf—automating entire decision cycles. Brands must optimize not only for visibility but for interpretability and transactional readiness.

2. Sponsored Listings & Commercial Models Will Arrive

Though ChatGPT and Perplexity currently have no paid placement model, it’s inevitable. Expect native sponsored product slots and affiliate integrations that look and feel organic. Brands should prepare for a hybrid world where paid + organic optimization coexist inside conversational environments.

3. Multimodal and Voice Shopping Will Emerge

Soon, consumers will ask their AI assistant to “find a backpack like this photo” or “buy me the same vitamins I ordered last time.” Shopping via voice and images will increase the need for structured product data and consistent labeling across channels.

4. Product Discovery Will Be Layered Into Social and Messaging Apps

With ChatGPT integrating into platforms like WhatsApp and iMessage, the AI shopping layer will embed itself directly into how people communicate. Brands that seed conversations and are prepared to respond to AI-curated product intent will lead.

5. AI Visibility Will Require Cross-Channel Consistency

It won’t be enough to optimize your PDP. AI systems pull from schema, reviews, Reddit, TikTok, YouTube, and third-party commerce APIs. If your product content isn’t consistent, up-to-date, and high quality across platforms, you risk invisibility.

6. AEO is the new SEO

As AI becomes the gateway to product discovery, Answer Engine Optimization (AEO) will sit alongside SEO, performance marketing, and retail media in the brand playbook. Forward-thinking teams will invest in AEO infrastructure, monitoring, and attribution.

In short, The winners in AI search commerce will be those who think like machines, act like publishers, and perform like retailers.

Final Word: AI Is the New Shelf

ChatGPT Shopping is more than a feature. It’s a paradigm shift. Consumers no longer need to Google and sift through 10 tabs. They ask. The AI answers.

And if your product isn’t structured, described, or visible in the right way? You won’t be part of the conversation.

In the new era of AI search commerce, visibility isn’t earned by backlinks or bidding, it’s earned by clarity, trust, and structured product data.

If you want your brand to win on the new digital shelf, the time to optimize is now.

The post ChatGPT Shopping: Definitive AEO Guide to AI Search Commerce appeared first on NoGood™: Growth Marketing Agency.