Does ChatGPT recommend Askadia?

ChatGPT mentioned Askadia in 1 of 10 questions

It also named Peec AI, Otterly.AI, and Profound.

1. Askadia vs Peec AI: which is better for tracking whether ChatGPT recommends my product? Branded

   Named 2nd · recommended as an option · also recommended Peec AI

   Verdict: Peec AI is the stronger choice for tracking ChatGPT product recommendations.

   There is an important distinction, though: tracking whether ChatGPT mentions your brand is not the same as tracking whether ChatGPT actually recommends your product to someone ready to buy.

   If your goal is to know whether your product appears in answers such as:

   • "What's the best [product category]?"
   • "Which product would you recommend for under $100?"
   • "What are the best alternatives to [competitor]?"
   • "Should I buy Product A or Product B?"

   Then you want product-level recommendation tracking, not just brand visibility monitoring.

   Peec AI has a particularly relevant advantage here: its AI Shopping Analytics feature tracks individual products in ChatGPT's shopping recommendations, including position, win rate, competing products and cited prices. Peec AI launches AI Shopping Analytics as product recommendations move inside ChatGPT Pricing for Peec AI - AI Search Analytics for Marketing teams and SEO agencies

   Askadia vs Peec AI

   Capability	Askadia	Peec AI
   Check whether ChatGPT recommends you	Yes, core focus	Yes
   See which competitors get recommended instead	Yes	Yes
   See actual ChatGPT answers	Yes	Yes
   See pages ChatGPT reads	Yes	Yes, with source analysis
   Track visibility over time	Needs verification	Yes, daily
   Track individual product SKUs	Not verified	Yes, for ChatGPT Shopping
   Competitor benchmarking	Recommendation-focused	Extensive metrics
   Other AI engines	Needs verification	Yes
   Marketing analytics and reporting	Needs verification	Yes

   Askadia's positioning is interesting: it specifically advertises checking whether ChatGPT recommends your product, which competitors it recommends instead, and which pages it reads. It also advertises a free two-minute checkup. However, I couldn't establish its recurring tracking capabilities or independently verify its measurement methodology. Askadia: AI visibility tracker for products, with a free checkup | Tech Stack & Analysis

   Peec has more clearly documented capabilities:

   • Daily tracking and historical visibility trends.
   • Brand mentions, rankings, sentiment and share of voice.
   • Competitor comparisons.
   • Source and citation analysis.
   • Product-level ChatGPT Shopping analytics. AI Instructions for Peec AI AI Instructions for Peec AI Pricing for Peec AI - AI Search Analytics for Marketing teams and SEO agencies

   Which would I choose?

   • Askadia: If you primarily want a straightforward answer to "Does ChatGPT recommend my product, and who does it recommend instead?" I'd try its free check first.
   • Peec AI: If you want a recurring measurement system to monitor your performance, compare competitors, understand changes and track individual products, I'd choose Peec.

   One important distinction before buying

   Peec's product-level shopping analytics focuses on ChatGPT's shopping carousel. That is not necessarily the same as being recommended in an ordinary conversational answer.

   For example:

   • "Best running shoes for marathon training" → conversational recommendation.
   • ChatGPT displays a shopping carousel containing your shoe → shopping recommendation.

   I'd test both separately.

   My recommended test

   Before committing to either platform, run the same 20–30 buyer-intent prompts through both tools.

   Measure:

   1. Recommendation rate: In how many answers does your product appear?
   2. First-choice rate: How often is your product the leading recommendation?
   3. Competitor displacement: Which competitors appear instead?
   4. Citation sources: Which websites influence ChatGPT's recommendations?
   5. Consistency: Does your product keep appearing over repeated tests?

   One final caveat: AI answers vary by location, wording and time. Neither tool can guarantee that its tracking results perfectly represent every customer's ChatGPT experience.

   My bottom line: Peec AI is my recommendation for an ongoing business intelligence tool. Askadia is worth testing first if you want a focused product-recommendation audit.

   You can explore them here:

   • Askadia
   • Peec AI

   If you tell me what type of product you sell (and whether it's e-commerce, SaaS or a physical product), I can help determine which platform is better for your actual buying-intent searches.

   Sources globenewswire.compeec.aifirstsited.com

   ChatGPT · Nov 8, 15:50
2. What's the best tool for checking whether ChatGPT recommends products in my category? Commercial

   Not mentioned · recommended Profound, Peec AI, Otterly.AI, Semrush

   The best tool for this is an AI Visibility Tracker, also called an Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO) tool.

   It lets you see whether ChatGPT recommends your products when people ask questions like:

   • "What's the best [product category]?"
   • "Which [product type] should I buy?"
   • "What are the top brands for [category]?"
   • "What's the best alternative to [competitor]?"

   My top 4 recommendations

   1. Profound

   Best overall for enterprise

   Tracks brand visibility, ChatGPT recommendations, competitors, citations and customer search prompts.

   Website: Profound

   2. Peec AI

   Best for category research

   Helps track which brands appear in AI answers, how they compare, and which sources ChatGPT cites.

   Website: Peec AI

   3. Otterly.AI

   Best budget starting point

   Tracks prompts and brand mentions across ChatGPT and other AI search platforms.

   Website: Otterly AI

   4. Semrush

   Best combined SEO and AI tracking

   Useful for comparing traditional search visibility with AI recommendations and competitor presence.

   Website: Semrush

   Best AI search monitoring tools for 2026 The 8 Best AI Visibility Tools to Win in AI Search (2026) Best ChatGPT SEO Tracking Tools in 2026 14 Best AI Visibility Tools in 2026: LLM Trackers Compared

   What I'd actually recommend

   If your goal is to find out whether ChatGPT recommends products in your category, I'd start with Peec AI or Profound.

   But there's an important distinction: you don't just want to know whether ChatGPT mentions your brand. You want to know:

   1. Which products does ChatGPT recommend in your category?
   2. Which brands get recommended most frequently?
   3. Does ChatGPT recommend your competitors instead?
   4. What questions cause ChatGPT to recommend those competitors?
   5. Which websites and sources influence those recommendations?

   One question so I can narrow this down

   What product category is your business in?

   For example, skincare, supplements, home improvement, software, clothing, automotive, or something else.

   I can help identify the best tool and show you how to test whether ChatGPT recommends your products versus your competitors.

   Sources technologyadvice.comsemrush.comtheaiseoagency.iosurfer.ai

   ChatGPT · Nov 8, 15:48
3. Which platform can show product teams how often competitors appear in AI-generated answers? Commercial

   Not mentioned · recommended Semrush, Scrunch AI, Measure, Gensiv…

   The platform you're looking for is Semrush – AI Share of Voice (AI Visibility Tracking).

   It helps product and marketing teams measure: - Competitor mentions: How often competitors appear in AI-generated answers. - AI share of voice: Your brand's percentage of mentions compared with competitors. - Platform breakdown: Visibility across ChatGPT, Gemini, Google AI Mode and Perplexity. - Historical trends: How competitor visibility changes over time. - Actual AI responses: See the answers where competitors are recommended instead of your brand.

   You can explore it here: Semrush AI Share of Voice.

   Other platforms worth considering

   Platform	Key capability
   Scrunch AI	Competitive presence and AI answer monitoring
   Measure	Competitor visibility, rankings and share of voice
   Gensiv	Competitor mentions across seven AI engines
   Birdeye	AI visibility and competitor benchmarking

   My recommendation

   For product teams specifically, I'd shortlist:

   1. Semrush – Best starting point for established marketing and product teams.
   2. Measure – Strong competitor-by-competitor visibility tracking.
   3. Scrunch – Useful for understanding competitive presence across AI-generated answers.

   The category you're looking for is called AI Search Visibility Monitoring or Generative Engine Optimization (GEO) Analytics.

   Sources semrush.com

   ChatGPT · Nov 8, 15:48
4. What are good alternatives to Profound for monitoring product mentions in ChatGPT? Commercial

   Not mentioned · recommended Peec AI, Otterly.AI, AthenaHQ, Scrunch AI…

   If you're looking for alternatives to Profound specifically to monitor when products are mentioned, recommended, or compared in ChatGPT responses, there are several good options.

   The category is generally called AI Visibility Monitoring, LLM Monitoring, or Generative Engine Optimization (GEO).

   I'd shortlist these six platforms:

   Best alternatives to Profound

   1. Peec AI

   Best overall alternative

   • Tracks brand and competitor mentions across ChatGPT and other AI engines.
   • Prompt-level visibility, sentiment, citations, and historical tracking.
   • Good for understanding which products AI recommends for specific queries.
   • Particularly interesting for marketing teams that want a dedicated monitoring platform.

   Website: peec.ai

   2. Otterly.AI

   Best budget option

   • Tracks brand mentions in ChatGPT, Perplexity, and Google AI experiences.
   • Monitors prompts and competitor visibility.
   • Tracks citations and AI search visibility.
   • Good for smaller teams and agencies.

   Reported entry pricing: approximately $29/month.

   Website: otterly.ai

   3. AthenaHQ

   Best for enterprise teams

   • Tracks brand visibility across multiple AI search engines.
   • Competitor benchmarking and share-of-voice analysis.
   • Brand sentiment and hallucination detection.
   • Offers recommendations and workflows for improving AI visibility.

   Website: athenahq.ai

   4. Scrunch AI

   Best for deeper AI intelligence

   • Monitors brand presence in AI-generated answers.
   • Analyzes citations, competitors, and AI traffic.
   • Supports audience, persona, and funnel-level segmentation.
   • Also offers tools to improve how AI agents access your website.

   Website: scrunch.com

   5. SE Ranking

   Best SEO + AI visibility combination

   • Tracks brand mentions in ChatGPT and other AI search experiences.
   • Monitors competitors and visibility trends.
   • Combines traditional SEO data with AI search monitoring.
   • Useful if you want one platform for SEO and GEO.

   Website: seranking.com

   6. Ahrefs Brand Radar

   Best for competitive research

   • Tracks brand visibility across AI search.
   • Helps identify which brands appear in AI answers.
   • Analyzes citations and competitor presence.
   • Particularly useful if you already use Ahrefs for SEO.

   Website: ahrefs.com/brand-radar

   Quick comparison

   Platform	Best use case	Relative positioning
   Peec AI	Product and brand mentions	Mid-market
   Otterly.AI	Affordable monitoring	Budget
   AthenaHQ	Enterprise monitoring	Enterprise
   Scrunch AI	Deep AI intelligence	Enterprise
   SE Ranking	SEO + AI visibility	Mid-market
   Ahrefs Brand Radar	Competitor research	SEO-focused

   These are qualitative comparisons, not standardized performance scores. Industry comparisons also identify these platforms as Profound alternatives. Best AI search monitoring tools for 2026 14 Best AI Visibility Tools in 2026: LLM Trackers Compared The 8 Best AI Visibility Tools to Win in AI Search (2026)

   One important distinction: product mentions vs. brand mentions

   If your goal is to monitor individual products, rather than simply your company name, I'd pay particular attention to this distinction.

   For example, suppose you sell three products:

   • Product A: Project management software
   • Product B: Analytics software
   • Product C: CRM software

   You might want to track questions such as:

   • "What are the best project management tools?"
   • "Is Product A better than Competitor X?"
   • "What analytics software should a small business use?"
   • "What are the best alternatives to Product B?"
   • "Which CRM has the best automation features?"

   A good monitoring platform should ideally tell you:

   1. Whether your product appears in ChatGPT's answer.
   2. How frequently it gets recommended.
   3. Where it appears in the recommendation list.
   4. Which competing products appear alongside it.
   5. What sources ChatGPT cites when recommending it.
   6. Whether the sentiment or description of your product changes over time.

   My recommendation: Start by evaluating Peec AI, Otterly.AI, and AthenaHQ. Compare their actual product-level tracking rather than relying exclusively on their headline brand visibility scores.

   Let me narrow this down

   One question that would significantly change my recommendation:

   What exactly are you trying to monitor?

   I can then give you a much tighter shortlist, including tools that genuinely track individual product recommendations in ChatGPT rather than just general brand visibility.

   Sources technologyadvice.comsurfer.aisemrush.com

   ChatGPT · Nov 8, 15:48
5. Which software helps founders track their product's visibility in AI answers over time? Commercial

   Not mentioned · recommended Peec AI, Otterly.AI, Profound, Scrunch AI…

   The category you're looking for is AI Search Visibility Tracking software, also called Generative Engine Optimization (GEO) or Answer Engine Optimization (AEO) software.

   These tools help founders monitor whether their product gets recommended, mentioned, or cited by ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews over time.

   Best software options for founders

   1. Peec AI

   Great for startups

   • Tracks brand mentions, rankings, sentiment, and share of voice.
   • Daily monitoring across AI models.
   • Shows which sources AI engines cite.
   • Starting around $95/month.

   Website

   2. Otterly.AI

   Budget-friendly

   • Monitors ChatGPT, Perplexity, Google AI Overviews and Copilot.
   • Tracks mentions, citations, and competitors.
   • GEO audits and optimization insights.
   • Plans start around $29/month.

   Website

   3. Profound

   Enterprise-focused

   • Tracks brand presence in AI-generated answers.
   • Measures prompt trends and AI citations.
   • Offers competitive intelligence and AI crawler analytics.
   • Enterprise-oriented pricing.

   Website

   4. Scrunch AI

   • Tracks how brands appear in AI answers.
   • Competitor benchmarking and citation analysis.
   • AI crawler analytics.

   Website

   5. Ahrefs Brand Radar

   • Tracks brand visibility across AI search.
   • Researches mentions and citations.
   • Useful if you already use Ahrefs for SEO.

   Website

   6. Semrush AI Visibility Toolkit

   • AI visibility and competitor research.
   • Integrates AI search insights with traditional SEO.
   • Useful for established marketing teams.

   Website

   Sources: AI search tracking tools: How to track your brand’s presence in answer engines Best AI search monitoring tools for 2026 AI visibility tools compared: how to track your brand in AI search (2026) 15 Best AI Search Visibility Tools (2026): Compared & Priced

   What should founders actually track?

   I would focus on five metrics:

   Metric	What it tells you
   AI visibility	How often AI mentions your product
   Share of voice	How often you're mentioned vs competitors
   Recommendation rank	Where your product appears in answers
   Citation share	Whether AI references your website
   Sentiment	Whether AI describes your product positively

   My recommendation

   For an early-stage startup:

   • Otterly.AI if you want affordable monitoring.
   • Peec AI if you want a dedicated analytics dashboard.
   • Profound if AI search visibility is becoming a major growth channel.

   The important distinction: you want a tool that tracks the same buyer questions repeatedly, so you can see whether your product is becoming more visible month after month.

   One question

   Are you looking for software to:

   1. Monitor your own startup — see whether ChatGPT recommends your product.
   2. Competitive intelligence — see which competitors ChatGPT recommends instead.
   3. Build a SaaS product — create your own AI visibility tracking platform for other founders.

   The third is a particularly interesting software opportunity because this category is still developing.

   Sources hubspot.comtechnologyadvice.comranking-atlas.commeasure.co

   ChatGPT · Nov 8, 15:49
6. What's the best platform for comparing how different AI assistants surface my product to potential buyers? Commercial

   Not mentioned · recommended Peec AI, Profound, Otterly.AI

   The category you're looking for is AI visibility monitoring, also called Generative Engine Optimization (GEO) or Answer Engine Optimization (AEO).

   My recommendation is to start with Peec AI if your goal is to understand how different AI assistants recommend your product to prospective buyers.

   The important distinction is that you don't just want to know whether an AI mentions your brand. You want to know:

   • Does ChatGPT recommend your product when someone asks for the best solution in your category?
   • Does Claude, Gemini or Perplexity recommend a competitor instead?
   • What position does your product occupy in the answer?
   • Which sources and websites influence the recommendation?
   • How does your visibility change over time?

   These are the metrics that matter for understanding whether AI assistants are actually putting your product in front of potential customers.

   Best platforms to compare

   1. Peec AI — Best overall for competitive benchmarking

   My default recommendation

   • Compare brand visibility across AI assistants.
   • Track mentions, ranking, sentiment and citations.
   • Benchmark against competing products.
   • Covers ChatGPT, Gemini, Perplexity, Copilot and Google AI search features on its standard engine roster.

   Claude and additional models require upgrades. AI Instructions for Peec AI Best AI Visibility Software in 2026: 7 Tools Compared

   Peec AI website

   2. Profound — Best for product-level shopping visibility

   Particularly interesting if you sell physical products or run an ecommerce business.

   • Tracks individual products and SKUs in ChatGPT Shopping.
   • Shows product placement, images, prices and descriptions.
   • Compares your products against competing products.
   • Analyzes which shopping queries trigger product recommendations.

   Its dedicated Shopping Analysis currently focuses on ChatGPT Shopping. Shopping About Shopping

   Profound website

   3. Otterly.AI — Best for accessible monitoring

   • Tracks mentions and citations across AI platforms.
   • Provides competitor benchmarking.
   • Measures sentiment and share of voice.
   • Offers scheduled reporting and exports.

   A good starting point for smaller marketing teams. Some AI engines are paid add-ons. AI Search Analytics: Track Mentions & Citations OtterlyAI Pricing

   Otterly.AI website

   What I would actually measure

   Regardless of which platform you choose, I'd create a consistent set of buyer-intent prompts and run them across all the assistants.

   For example, if you sell project management software:

   Buyer question	What you measure
   Best project management software for small businesses	Recommendation frequency
   Alternatives to Asana	Competitive visibility
   Best software for managing remote teams	Category visibility
   Is [your product] worth buying?	Brand perception
   [Your product] vs Monday.com	Head-to-head positioning
   Most affordable project management tools	Price positioning

   Track five KPIs:

   1. AI visibility: Percentage of answers mentioning your product.
   2. Share of voice: Your mentions relative to competitors.
   3. Recommendation rate: How often you're actively recommended.
   4. Citation share: How often assistants reference your website.
   5. Competitive displacement: Which competitors appear instead of you.

   One important caveat: AI answers are variable. A single response isn't a reliable ranking, so use repeated measurements over time. AI Instructions for Peec AI AI visibility tools compared: how to track your brand in AI search (2026)

   My final recommendation

   • If you're selling SaaS or services: start with Peec AI.
   • If you're selling physical products online: investigate Profound first, particularly its Shopping Analysis.
   • If you're testing the market on a smaller budget: start with Otterly.AI.

   One question that would help me narrow this down

   Are you primarily trying to track:

   • A. How ChatGPT, Claude, Gemini and Perplexity recommend your brand versus competitors.
   • B. Whether individual products actually appear in AI-generated shopping recommendations.
   • C. Both — overall brand recommendations and individual product discovery.

   That distinction matters quite a bit when choosing the platform.

   Sources peec.airankite.comtryprofound.comotterly.airanking-atlas.com

   ChatGPT · Nov 8, 15:49
7. Are there alternatives to Semrush for tracking whether AI chatbots recommend my product? Commercial

   Not mentioned · recommended Peec AI, Otterly.AI, Profound, Scrunch AI…

   Yes. There are several good alternatives to Semrush for tracking whether AI chatbots recommend, mention, or cite your product.

   This category is generally called AI visibility monitoring, LLM tracking, or Generative Engine Optimization (GEO).

   These tools let you monitor questions such as:

   • "What are the best products for [your category]?"
   • "Is [your product] better than [competitor]?"
   • "What product would you recommend for [specific use case]?"
   • "Which brands are most reliable for [your product category]?"

   They then measure whether ChatGPT, Perplexity, Gemini and other AI platforms mention your product, recommend it, or cite your website.

   Best alternatives to Semrush

   1. Peec AI

   Best overall alternative

   Approximately $95/month

   • Tracks brand mentions across AI search engines.
   • Compares your visibility against competitors.
   • Tracks prompts, citations and sentiment.
   • Good for marketing teams monitoring several products.

   Website: Peec AI

   2. Otterly.AI

   Best budget option

   Starting around $29/month

   • Monitors brand mentions in ChatGPT, Perplexity and Google AI results.
   • Tracks citations and competitor mentions.
   • Offers GEO website audits.
   • Good for smaller businesses and individual products.

   Website: Otterly.AI

   3. Profound

   Best for enterprise

   Trial available; enterprise pricing varies

   • Monitors how AI platforms represent your brand.
   • Analyzes AI-generated answers and citations.
   • Provides prompt research and competitive intelligence.
   • Better suited to larger marketing teams.

   Website: Profound

   4. Scrunch AI

   Starting around $250/month

   • Tracks AI recommendations and brand visibility.
   • Analyzes which websites AI systems use as sources.
   • Provides insights into how AI crawlers interact with your website.
   • Useful for brands investing heavily in AI search optimization.

   Website: Scrunch AI

   5. AthenaHQ

   Free tier; paid plans around $295/month

   • Monitors AI visibility across multiple platforms.
   • Tracks competitors and brand sentiment.
   • Helps identify content opportunities.
   • Useful if you want monitoring combined with optimization workflows.

   Website: AthenaHQ

   6. Ahrefs Brand Radar

   Approximately $199/month for the relevant add-on

   • Tracks brand visibility in AI answers.
   • Provides competitor benchmarking.
   • Analyzes AI citations and mentions.
   • Particularly interesting if you already use Ahrefs.

   Website: Ahrefs Brand Radar

   Pricing is approximate, based on November 2026 comparisons, and depends on plan, prompt limits and AI engine coverage. 9 Best AI Visibility Tools in 2026 (GEO and AEO Trackers) 15 Best AI Search Visibility Tools (2026): Compared & Priced The 8 Best AI Visibility Tools to Win in AI Search (2026)

   Quick comparison

   Platform	Best use	Approx. starting price
   Peec AI	Brand monitoring	$95/mo
   Otterly.AI	Affordable tracking	$29/mo
   Profound	Enterprise intelligence	Custom
   Scrunch	AI crawler insights	$250/mo
   AthenaHQ	Optimization workflows	$295/mo
   Ahrefs	SEO + AI tracking	$199/mo

   What I would look for

   One important distinction: not all AI visibility tools measure the same thing.

   For your purpose, I would prioritize these five capabilities:

   1. Recommendation tracking: Does ChatGPT actually recommend your product?
   2. Competitor tracking: Does it recommend competitors instead?
   3. Prompt tracking: Which questions cause your product to appear?
   4. Citation tracking: Which websites and sources influence the recommendation?
   5. Historical tracking: Are recommendations increasing or decreasing over time?

   Also, AI answers vary between runs. These platforms sample responses, so their visibility scores are indicators rather than a guarantee of what every user sees. 9 Best AI Visibility Tools in 2026 (GEO and AEO Trackers) 15 Best AI Search Visibility Tools (2026): Compared & Priced

   Let me narrow this down to the right tools

   I have one important question:

   What kind of product are you trying to get recommended by AI chatbots?

   I can then narrow this down to the three best options, including which ones actually track product recommendations rather than simply counting brand mentions.

   Sources ryandoser.commeasure.cosemrush.com

   ChatGPT · Nov 8, 15:48
8. How can I improve my product's chances of being included in AI chatbot answers? Informational

   Not mentioned

   To improve your product's chances of appearing in AI chatbot answers (ChatGPT, Claude, Gemini, Perplexity, and Copilot), you need to optimize for AI visibility, increasingly known as Generative Engine Optimization (GEO).

   The goal is slightly different from traditional SEO.

   • Traditional SEO: Get your website to rank highly in Google search results.
   • AI optimization (GEO): Make your product easy for AI systems to discover, understand, trust, recommend, and cite when answering customer questions.

   For example, if someone asks ChatGPT, "What's the best project management tool for a small marketing agency?", you want your product to be one of the products considered and recommended.

   Here is a practical strategy.

   1. Make your product easy for AI to understand

   AI systems need clear, consistent information about what your product does.

   Your website should explicitly explain:

   • What your product is.
   • Who it is designed for.
   • What problems it solves.
   • Its most important features.
   • How it compares with alternatives.
   • Its pricing and availability.
   • What makes it different.

   Create dedicated pages for important use cases rather than relying on a generic homepage.

   For example:

   Page	Example topic
   Product overview	What is Product X?
   Use cases	Best software for small agencies
   Features	Product X reporting capabilities
   Comparisons	Product X vs. Competitor Y
   Pricing	Product X pricing and plans
   Customer stories	How agencies use Product X

   Important: Use specific language. Don't just say your product is innovative or revolutionary. Explain precisely what it does and for whom.

   2. Get mentioned on websites AI systems trust

   High priority

   This is one of the most important parts of GEO.

   AI systems can use information from many sources beyond your own website. Your product should have a credible presence across the wider internet.

   Focus on getting legitimate mentions in:

   • Industry publications and trade magazines.
   • Independent product reviews.
   • Comparison and buying guides.
   • Relevant Reddit discussions.
   • Industry blogs.
   • Professional communities.
   • Software and product directories.
   • Customer case studies.

   For example, if you sell accounting software, relevant articles might include:

   • Best accounting software for small businesses.
   • Best alternatives to QuickBooks.
   • Accounting tools for freelancers.
   • Accounting software comparison for startups.

   An independent, detailed review can be more useful than publishing another promotional article on your own website.

   3. Create content that answers questions people ask AI

   Think about the questions potential customers might type into ChatGPT.

   Instead of only targeting keywords, target complete questions and purchase decisions.

   Examples:

   • What is the best product for [specific problem]?
   • What are the best alternatives to [competitor]?
   • Which product is best for [specific audience]?
   • Is [your product] worth the money?
   • How does [your product] compare with [competitor]?
   • What should I look for when buying [product category]?

   Create useful pages that answer these questions directly.

   A good structure is:

   1. Give a clear, direct answer.
   2. Explain the reasoning.
   3. Include specific product capabilities.
   4. Provide evidence, statistics, or examples.
   5. Include genuine customer experiences.
   6. Explain limitations and trade-offs.

   This makes your content more useful to both humans and AI systems.

   4. Check that AI search engines can access your website

   This is a technical step that is easy to overlook.

   Your website may look perfect to customers but still have crawling restrictions that prevent AI search systems from accessing its content.

   For ChatGPT specifically:

   • Check your robots.txt file.
   • Make sure you aren't unintentionally blocking OAI-SearchBot.
   • Ensure important product pages are publicly accessible.
   • Make sure important content isn't hidden behind login screens.
   • Check that your website works without requiring complicated JavaScript interactions.

   OpenAI documents OAI-SearchBot as the crawler used to discover websites for ChatGPT search results. It is separate from GPTBot, which relates to model training. Overview of OpenAI Crawlers Publishers and Developers - FAQ

   For other platforms, check their respective crawler and webmaster documentation.

   5. Make product information consistent across the internet

   AI systems need to establish whether information about your product is accurate.

   Keep these details consistent:

   • Product name and brand.
   • Product category.
   • Features and specifications.
   • Pricing.
   • Availability.
   • Target audience.
   • Company name.
   • Official website.

   For ecommerce products, maintain accurate product feeds and structured product information.

   Use appropriate Schema.org markup, such as:

   • Product
   • Offer
   • Organization
   • Review
   • AggregateRating

   Structured data isn't a magic way to get recommended, but it can help search engines interpret your product information. Ensure the markup matches what customers can actually see on the page. Google's Guide to Optimizing for Generative AI Features on Google Search | Google Search Central  |  Documentation  |  Google for Developers AI Features and Your Website | Google Search Central  |  Documentation  |  Google for Developers

   6. Build genuine customer reviews and independent proof

   AI recommendations become more useful when they can be supported by credible evidence.

   Encourage real customers to share their experiences on relevant platforms.

   Useful sources include:

   • Google reviews.
   • G2 and Capterra (for software).
   • Trustpilot.
   • Industry-specific review platforms.
   • Reddit discussions.
   • Independent YouTube reviews.

   Don't buy reviews or manufacture recommendations. Google's current guidance specifically warns against trying to manipulate AI visibility through inauthentic mentions. Google's Guide to Optimizing for Generative AI Features on Google Search | Google Search Central  |  Documentation  |  Google for Developers Google's Guide to Optimizing for Generative AI Features on Google Search | Google Search Central  |  Documentation  |  Google for Developers

   7. Measure how often AI recommends your product

   You need to establish a baseline and track changes.

   Create a list of 20–50 questions potential customers might ask AI systems.

   For example:

   Customer question	What to measure
   Best [product category]	Is your product mentioned?
   Best [category] for small businesses	Is it recommended?
   Alternatives to [competitor]	Is your product included?
   [Your product] vs. [competitor]	Is the comparison accurate?
   Is [your product] worth it?	Does the answer reflect your strengths?

   Test these questions periodically in:

   • ChatGPT.
   • Gemini.
   • Claude.
   • Perplexity.
   • Microsoft Copilot.

   Track:

   • How often your product appears.
   • Whether it is recommended or merely mentioned.
   • Whether your website is cited.
   • Which competitors appear.
   • Which external sources AI uses.
   • Whether product information is accurate.

   Remember that responses can vary between sessions, users, models, and whether web search is enabled. Treat this as a visibility trend rather than a fixed ranking.

   8. Don't waste time on GEO gimmicks

   There are plenty of companies selling supposed AI ranking tricks.

   Be cautious about claims that you can guarantee AI recommendations by:

   • Adding an llms.txt file.
   • Repeating your brand name hundreds of times.
   • Generating hundreds of AI-written articles.
   • Adding special AI schema markup.
   • Paying websites to publish artificial product mentions.

   Google's 2026 guidance explicitly says that special AI files and GEO-specific markup aren't required for visibility in its generative search features. Google's Guide to Optimizing for Generative AI Features on Google Search | Google Search Central  |  Documentation  |  Google for Developers A new resource for optimizing for generative AI in Google Search  |  Google Search Central Blog  |  Google for Developers

   The better investment is useful content, strong technical SEO, genuine reputation, and accurate product information.

   My recommended 30-day action plan

   Timeline	Priority	Action
   Week 1	Technical	Check indexing, crawling and AI bot access
   Week 1	Content	Improve homepage and product descriptions
   Week 2	Content	Publish 5–10 genuinely useful comparison and use-case pages
   Week 3	Reputation	Pursue legitimate reviews and industry coverage
   Week 4	Measurement	Test 30–50 AI queries and record visibility

   The biggest opportunity: Understand what AI says about your product today

   I would actually start by conducting an AI visibility audit before investing heavily in content or marketing.

   That audit should answer four questions:

   1. Does ChatGPT currently know about your product?
   2. Does it recommend your product when customers ask relevant questions?
   3. Which competitors does it recommend instead?
   4. What websites and sources influence those recommendations?

   This will help identify whether your biggest problem is discoverability, reputation, content, or positioning.

   Let me help you develop a GEO strategy for your product

   I can make this considerably more specific if I know a little about your business.

   I can then help you identify concrete opportunities to increase the likelihood that AI assistants discover, mention, and recommend your product.

   Sources openai.comgoogle.com

   ChatGPT · Nov 8, 15:52
9. How should a product team diagnose why AI assistants favor competitors? Informational

   Not mentioned · recommended Semrush AI Visibility

   How product teams should diagnose why AI assistants favor competitors

   Product teams should treat this as an AI visibility and recommendation problem, not simply an SEO problem.

   When assistants such as ChatGPT, Claude, Gemini, and Perplexity recommend competitors instead of your product, the underlying issue could be positioning, discoverability, product information quality, brand authority, customer sentiment, or how well your product matches the user's intent.

   The goal is to identify why competitors are being selected, what evidence influences that selection, and what changes would make your product a stronger recommendation.

   I recommend a six-step diagnostic framework.

   1. Establish where and when competitors are being recommended

   Start by systematically testing AI assistants rather than relying on anecdotal examples.

   Build a prompt library covering different stages of the customer journey:

   Customer intent	Example prompt
   Category discovery	What are the best project management tools?
   Direct comparison	Compare Product A and Product B.
   Problem-solving	What's the best tool for managing distributed teams?
   Specific use case	Which platform is best for small businesses?
   Purchase evaluation	What are the best alternatives to Product A?
   Recommendation	Which product would you recommend for a 50-person company?

   Run the same prompts across multiple AI assistants.

   Record: - Whether your product appears. - Which competitors are mentioned. - Your product's position in the response. - Whether the assistant recommends or merely mentions your product. - Which product attributes it highlights. - Which sources it cites. - Whether the response accurately describes your offering.

   This establishes a baseline for AI recommendation visibility.

   Important: Don't measure visibility using a single prompt or assistant. Recommendations can change substantially depending on wording, context, model, and user intent.

   2. Identify why competitors are winning

   Once you have collected responses, categorize the reasons competitors appear to be favored.

   Look for six potential causes:

   A. Discoverability gaps

   The competitor may have more accessible, relevant information across the web.

   Investigate: - Does the competitor have more content addressing relevant customer problems? - Are its product pages indexed and accessible? - Does it publish detailed product documentation? - Does it appear in industry publications and comparison articles? - Are its products discussed more frequently in relevant communities?

   B. Authority and credibility gaps

   AI assistants often draw on information beyond company websites.

   Investigate: - Which industry publications mention competitors? - Which review platforms feature them? - Are competitors discussed in Reddit communities and specialist forums? - Do independent reviewers validate their product claims? - Are competitors consistently associated with the category?

   A company cannot establish market credibility through its own marketing claims alone. Independent corroboration matters. semrush.com, mckinsey.com

   C. Product positioning gaps

   Your product might be good but poorly differentiated.

   For example:

   • Competitor A is associated with enterprise scalability.
   • Competitor B is associated with ease of use.
   • Competitor C is associated with affordability.
   • Your product is described simply as an all-in-one platform.

   The competitors have clearer associations with specific customer needs.

   Investigate whether AI assistants can easily identify: - Your primary use cases. - Your ideal customer segments. - Your strongest differentiators. - Your competitive advantages. - The problems your product solves better than alternatives.

   D. Information accuracy gaps

   AI assistants may rely on outdated or contradictory information.

   Check whether: - Your pricing information is current. - Feature descriptions match the actual product. - Product names and terminology are consistent. - Old comparison pages misrepresent your offering. - Third-party websites contain outdated information.

   E. Customer sentiment gaps

   Competitors may have stronger evidence of customer satisfaction.

   Compare: - Review volume and quality. - Customer satisfaction. - Common complaints. - Product reliability. - Customer support reputation. - Independent product evaluations.

   F. Competitive familiarity gaps

   Established competitors may have stronger brand recognition and a larger footprint in the information ecosystem.

   This matters because AI recommendation systems do not necessarily evaluate every product from scratch.

   Recent research on generative search has identified brand familiarity and popularity as potential sources of recommendation advantage. arxiv.org

   3. Investigate the sources behind AI recommendations

   This is arguably the most important diagnostic exercise.

   Don't just examine what the AI assistant says. Investigate where its information comes from.

   For every important competitor recommendation:

   1. Identify the sources cited by the assistant.
   2. Open those sources.
   3. Identify the specific claims supporting the recommendation.
   4. Determine whether the same sources mention your product.
   5. Compare how your product and the competitor are described.

   Create a source comparison:

   Source category	Competitor	Your product
   Industry publications	Frequently mentioned	Rarely mentioned
   Review websites	Strong ratings	Limited reviews
   Comparison articles	Clearly positioned	Missing
   Community discussions	Frequent recommendations	Limited discussion
   Product documentation	Comprehensive	Incomplete
   Independent evaluations	Strong validation	Limited evidence

   This helps distinguish between a content problem and an authority problem.

   For example, if competitors consistently appear in independent comparison articles while your product is absent, publishing another company blog post may not solve the underlying issue.

   The appropriate intervention may be better third-party coverage, customer evidence, or independent product evaluations. searchengineland.com, semrush.com

   4. Measure the competitive gap systematically

   Create an AI visibility scorecard.

   I would track these seven metrics:

   Metric	What it measures
   Brand mention rate	How frequently your product appears
   Recommendation rate	How frequently your product is actively recommended
   Recommendation position	Where your product appears in recommendation lists
   Competitive share of voice	Your visibility relative to competitors
   Citation frequency	How often your sources support AI answers
   Product accuracy	Whether assistants describe your product correctly
   Sentiment	Whether your product is described positively or negatively

   One particularly important distinction:

   Being mentioned is not the same as being recommended.

   An assistant might mention your product but ultimately recommend three competitors.

   Track those outcomes separately.

   Also, repeat prompts across different runs. AI outputs are variable, so a single response should never be treated as a definitive ranking. Recent research proposes repeated sampling to measure recommendation prevalence and prominence more reliably. arxiv.org, lumenario.com

   5. Translate findings into product and marketing improvements

   Once the team understands the underlying problem, prioritize interventions based on the diagnosed cause.

   Diagnosis	Recommended action
   Low discoverability	Improve technical accessibility, product pages and topic coverage
   Weak brand authority	Build independent industry coverage and expert recognition
   Unclear positioning	Strengthen product differentiation and use-case messaging
   Inaccurate information	Update outdated content and correct inconsistent product facts
   Weak customer sentiment	Address recurring product complaints and improve customer experience
   Limited competitive evidence	Develop independent comparisons, case studies and customer testimonials

   Importantly, don't approach this exclusively as an SEO initiative.

   Product, marketing, communications, customer success and engineering may all need to contribute.

   For example: - Product: Clarify differentiating features and use cases. - Marketing: Improve category positioning and product messaging. - Communications: Build independent brand authority. - Customer success: Generate credible customer evidence. - Engineering: Improve website accessibility and structured product information.

   6. Establish a continuous monitoring process

   AI visibility should become an ongoing competitive intelligence activity.

   I recommend a monthly diagnostic cycle:

   Activity	Frequency	Owner
   AI recommendation testing	Weekly	Product marketing
   Competitor benchmarking	Monthly	Competitive intelligence
   Citation and source analysis	Monthly	SEO/content
   Product information accuracy	Monthly	Product
   Customer sentiment analysis	Monthly	Customer success
   Strategic improvement review	Quarterly	Product leadership

   Create a dashboard showing: - AI visibility by platform. - Recommendation share by competitor. - Prompts where competitors consistently outperform you. - Sources influencing competitor recommendations. - Changes in product accuracy and sentiment. - Business impact of AI-generated recommendations.

   Useful starting points include: - Semrush AI Visibility - Search Engine Land's AI prompt tracking guide - McKinsey's AI search strategy research

   ---

   Recommended deliverable: AI Competitive Recommendation Audit

   For a product team, I would consolidate the investigation into one diagnostic report.

   It should contain five sections:

   1. Competitive visibility - Which competitors are recommended most frequently? - Which customer queries trigger their recommendations? - Where is your product missing?

   2. Recommendation drivers - Why are competitors being recommended? - Which features, benefits and use cases are emphasized? - What evidence supports those recommendations?

   3. Information gaps - What information about your product is missing? - Where is information inaccurate or inconsistent? - Which external sources influence recommendations?

   4. Competitive weaknesses - Where do competitors have stronger positioning? - Where does your product offer superior capabilities? - Which customer segments are underserved?

   5. Prioritized action plan - What should the product team change? - What should marketing improve? - Which external sources should the company influence? - How will success be measured?

   My biggest recommendation

   Don't start by asking:

   "How do we get ChatGPT to recommend our product?"

   Start by asking:

   "What evidence, product attributes and market signals make AI assistants believe our competitors are better recommendations for particular customer needs?"

   That distinction is important.

   If a competitor is winning because it has clearer positioning, the solution is different from a competitor winning because it has stronger customer reviews or broader independent coverage.

   The ultimate objective isn't simply to increase brand mentions in AI answers.

   It's to make your product: 1. Discoverable when customers explore your category. 2. Understandable when assistants evaluate your capabilities. 3. Credible when assistants compare competing solutions. 4. Recommendable when your product genuinely fits the customer's needs.

   That's the difference between optimizing for AI visibility and building a genuine competitive advantage in AI-mediated product discovery.

   Sources arxiv.orgsearchengineland.comlumenario.commckinsey.comsemrush.comciteworksstudio.comsoci.aiyext.com +3

   ChatGPT · Nov 8, 15:50
10. How can I tell whether AI-generated answers describe my product accurately? Informational

    Not mentioned · recommended ChatGPT, Google Gemini, Claude, Perplexity

    The best way to tell whether AI-generated answers describe your product accurately is to test what AI says against a verified source of truth about your product, rather than judging whether the answers simply sound convincing.

    This is especially important because AI tools can produce answers that are:

    • Factually incorrect, even when they sound confident.
    • Outdated because they rely on older product information.
    • Incomplete, omitting important features or differentiators.
    • Misleading when comparing your product with competitors.
    • Incorrect about pricing, integrations, compatibility, or intended use.

    Here is a practical way to measure accuracy.

    1. Create a verified product fact sheet

    Start with a single authoritative document containing:

    Information	What to verify
    Product name	Correct name and spelling
    Product description	What it actually does
    Features	Current, available capabilities
    Benefits	Outcomes supported by evidence
    Pricing	Current plans and prices
    Integrations	Supported integrations
    Target audience	Who the product is designed for
    Limitations	What the product cannot do
    Differentiators	What genuinely distinguishes it

    This becomes your ground truth for evaluating AI responses.

    2. Test multiple AI platforms

    Ask different AI assistants questions that potential customers might ask.

    For example:

    • What does [Product] do?
    • Who is [Product] best suited for?
    • What are the main features of [Product]?
    • How does [Product] compare with competitors?
    • What problems does [Product] solve?
    • What are the limitations of [Product]?
    • Is [Product] compatible with [specific technology]?

    Test platforms such as ChatGPT, Google Gemini, Claude, and Perplexity.

    Don't just ask one question. Different prompts can reveal different misunderstandings.

    3. Score the accuracy of each answer

    Use a consistent evaluation framework.

    Dimension	Score	Evaluation
    Factual accuracy	0–2	Are the claims correct?
    Completeness	0–2	Are important features missing?
    Currency	0–2	Is the information up to date?
    Positioning	0–2	Is the product described correctly?
    Evidence	0–2	Do cited sources support the claims?

    Scoring:

    • 9–10: Highly accurate
    • 7–8: Mostly accurate
    • 4–6: Needs improvement
    • 0–3: Significant inaccuracies

    Also flag serious errors separately. For example, incorrectly claiming that your product supports an integration it doesn't support should be treated as a critical failure, even if the overall score is high.

    4. Track results over time

    Create a simple spreadsheet with these columns:

    Field	Example
    Date tested	November 8, 2026
    AI platform	ChatGPT
    Question	What does Product X do?
    AI response	Captured answer
    Correct facts	8
    Incorrect facts	2
    Missing facts	1
    Overall score	7/10
    Issues	Incorrect integration claim
    Source cited	Product documentation

    Repeat the same questions periodically. This helps you identify whether AI descriptions are improving, deteriorating, or changing unpredictably.

    Importantly, distinguish factual accuracy from visibility: an AI system might describe your product accurately but rarely mention it, or mention it frequently while describing it incorrectly.

    5. Investigate where incorrect information comes from

    When you find an inaccurate answer, check its cited sources.

    Common causes include:

    • Old product documentation.
    • Outdated third-party reviews.
    • Inconsistent descriptions across your website and other platforms.
    • Competitor information being confused with yours.
    • Missing details about features, limitations, or product updates.

    Prioritize updating your official product pages, documentation, FAQs, pricing pages, and relevant third-party listings.

    Google also recommends prioritizing accurate, useful information and warns that generative AI can produce factual inaccuracies. Google Search's Guidance on Generative AI Content on Your Website | Google Search Central  |  Documentation  |  Google for Developers Creating Helpful, Reliable, People-First Content | Google Search Central  |  Documentation  |  Google for Developers

    6. Build an ongoing AI accuracy dashboard

    I recommend tracking these five metrics:

    AI product accuracy dashboard

    90%

    Factual accuracy

    85%

    Description completeness

    95%

    Current information

    80%

    Correct positioning

    Illustrative metrics, not actual product measurements.

    I would also track:

    • Percentage of answers containing factual errors.
    • Frequency of incorrect pricing or feature claims.
    • Percentage of answers citing reliable sources.
    • Competitor misattribution.
    • Consistency across AI platforms.

    7. Use an independent verification process

    Don't rely exclusively on another AI model to judge whether an AI answer is correct.

    A better process is:

    1. AI generates a product description.
    2. Another system extracts individual factual claims.
    3. Each claim is compared with your verified product documentation.
    4. A human reviews ambiguous or high-impact claims.
    5. Results are recorded and tracked over time.

    This approach is consistent with established AI evaluation concepts such as groundedness, relevance, and completeness. Retrieval-Augmented Generation (RAG) Evaluators for Generative AI - Microsoft Foundry LibGuides: Critically Evaluating AI Outputs: Home

    My recommendation

    Think of this as an ongoing AI Product Accuracy Audit.

    The ultimate question isn't simply whether AI mentions your product. It's whether a potential customer receives a correct, complete, current, and evidence-supported understanding of your product when asking an AI assistant.

    I can help you build a practical AI Product Accuracy Audit for your business, including a repeatable testing framework, scoring system, competitor comparisons, and a dashboard for ChatGPT, Gemini, Claude, and Perplexity.

    One question to help me tailor it: Are you evaluating a software/SaaS product, an ecommerce product, or a broader business offering?

    Sources google.comgoogle.cnmicrosoft.comrice.edu

    ChatGPT · Nov 8, 15:49