Category: Articles

  • The Most Comprehensive AI Visibility / GEO / AEO Tool Comparison (25 tools) – Nov 2025

    The Most Comprehensive AI Visibility / GEO / AEO Tool Comparison (25 tools) – Nov 2025

    As AI assistants like ChatGPT, Google AI Overviews, and Claude become primary discovery channels, brands need specialized tools to track and optimize their visibility. Generative Engine Optimization (GEO) platforms have emerged as the essential infrastructure for understanding how your brand appears in AI-generated responses, monitoring citations, and proactively improving your presence across conversational interfaces.

    The GEO landscape has rapidly expanded throughout 2025, with solutions ranging from budget-friendly monitoring tools to enterprise-grade platforms offering deep analytics, prompt volume intelligence, and optimization workflows. This comprehensive guide covers 25 leading platforms, each with distinct strengths in coverage, pricing, and capabilities.

    Below, you’ll find a quick-reference table comparing all platforms, followed by detailed deep dives into each solution. Whether you’re a solopreneur tracking basic mentions or an enterprise team building a comprehensive GEO strategy, this index will help you identify the right platform for your needs.

    PlatformStarting Price (2025 Est.)Models CoveredImprove or MonitorPrompt Volume DataLanguages
    SpotlightFree tier; Paid ~$49/moChatGPT, Google AI Overview, Gemini, Claude, Perplexity, Grok, Copilot, AimodeAlso ImproveYesAll languages
    Profound~$99/moChatGPTAlso ImproveYesUnknown
    Scrunch AI~$300/moEvery LLM (not specified)Also ImproveNoMultiple (not listed)
    Peec AI~€89/mo (~$95)ChatGPT, Perplexity, DeepseekAlso ImproveYesUnknown
    HallFree tier; Paid ~$49/moChatGPT, Perplexity, Gemini, Copilot, Claude, DeepSeekAlso ImproveUnknownUnknown
    Otterly AI~$29/moUnknownUnknownUnknownUnknown
    Promptmonitor~$29/moChatGPT, Claude, Gemini, DeepSeek, Grok, Perplexity, Google AI Overview, AI ModeAlso ImproveNoUnknown
    Mentions.so~$49/moUnknownUnknownUnknownUnknown
    Writesonic~$79/moChatGPT, Gemini, Perplexity, 10+ platformsAlso ImproveYesUnknown
    Semrush AI Toolkit~$99/mo (add-on)ChatGPT, PerplexityAlso ImproveYesUnknown
    AthenaHQ~$295/moChatGPT, Perplexity, Google AI Overviews, AI Mode, Gemini, Claude, Copilot, GrokAlso ImproveUnknownUnknown
    Peekaboo~$100/moUnknownUnknownUnknownUnknown
    MorningScore~$49/moChatGPT, Google AI OverviewsAlso ImproveUnknownUnknown
    SurferSEO~$79/moGoogle, ChatGPT, other AI chatsAlso ImproveUnknownEnglish, Español, Français, Deutsch, Nederlands, Svenska, Dansk, Polski
    Airank~$49/moUnknownUnknownUnknownUnknown
    AmionAIBeta; ~$50/moChatGPTAlso ImproveNoMulti-language (not listed)
    Authoritas AI Search~$119/moChatGPT, Gemini, Perplexity, Claude, DeepSeek, Google AI Overviews, Bing AIAlso ImproveUnknownMultilingual (customizable)
    ModelMonitor~$49/moUnknownUnknownUnknownUnknown
    Quno~$99/moUnknownAlso ImproveUnknownUnknown
    RankScaleBeta; ~$79/moUnknownUnknownUnknownUnknown
    Waikay~$19.95/moChatGPT, Google, Claude, SonarAlso ImproveNo13 languages
    XFunnel~$199/moChatGPT, Gemini, Copilot, Claude, Perplexity, AI Overview, AI ModeAlso ImproveYesUnknown
    Clearscope~$170/moChatGPT, GeminiAlso ImproveUnknownUnknown
    ItsProject40~$50/moUnknownUnknownUnknownUnknown
    AI Brand Tracking~$99/moUnknownUnknownUnknownUnknown

    Now let’s dive deeper into each platform. The following sections provide comprehensive details on pricing, model coverage, key features, and use cases for all 25 GEO solutions. This will help you understand not just what each tool does, but how it fits into different marketing workflows and team structures.

    Platform Deep Dives

    Spotlight

    Spotlight website thumbnail

    Pricing: Free tier available; Paid ~$49/month | Models Covered: ChatGPT, Google AI Overview, Gemini, Claude, Perplexity, Grok, Copilot, Aimode | Capabilities: Monitor & Improve | Prompt Volume Data: Yes | Languages: All languages

    Spotlight tracks and optimizes brand visibility across AI chatbots and conversations, with GEO/AEO solutions for boosting mentions and citations. The platform combines comprehensive monitoring with proactive optimization, surfacing citation gaps and geo-targeting opportunities while supplying prompt volume analytics across every major AI assistant. With support for all languages and coverage of 8+ major AI platforms, Spotlight is purpose-built for teams that need both deep insights and actionable optimization workflows. The free tier makes it accessible for early-stage brands, while paid plans unlock advanced features for growing teams.

    MonitorOptimizePrompt VolumeAll Languages

    Profound

    Profound website thumbnail

    Pricing: ~$99/month | Models Covered: ChatGPT | Capabilities: Monitor & Improve | Prompt Volume Data: Yes | Languages: Unknown

    Profound monitors brand presence in AI-generated answers, providing prompt volumes, conversation insights, and optimization for LLMs like ChatGPT and Perplexity. The platform focuses on conversation-level intelligence within ChatGPT, pairing detailed prompt volume tracking with actionable optimization guidance for brand and competitor mentions. While coverage is currently focused on ChatGPT, Profound delivers deep insights for brands that prioritize this high-traffic platform. The platform is ideal for teams seeking detailed prompt analytics and optimization strategies specifically within OpenAI’s ecosystem.

    Prompt IntelligenceOptimizationChatGPT Focus

    Scrunch AI

    Scrunch AI website thumbnail

    Pricing: ~$300/month | Models Covered: Every LLM (not specified) | Capabilities: Monitor & Improve | Prompt Volume Data: No | Languages: Multiple (not listed)

    Scrunch AI provides proactive monitoring and optimization for AI search results, identifying content gaps, misinformation, and improvements across platforms like ChatGPT and Google AI Overviews. The platform leans into enterprise-grade content intelligence, flagging misinformation, content gaps, and optimization opportunities across AI search experiences. With broad coverage across every major LLM and a focus on content quality and accuracy, Scrunch AI is positioned for larger organizations that need comprehensive visibility and content strategy guidance. The higher price point reflects its enterprise positioning and extensive coverage.

    EnterpriseContent IntelligenceBroad Coverage

    Peec AI

    Peec AI website thumbnail

    Pricing: ~€89/month (~$95) | Models Covered: ChatGPT, Perplexity, Deepseek | Capabilities: Monitor & Improve | Prompt Volume Data: Yes | Languages: Unknown

    Peec AI tracks visibility, benchmarks competitors, analyzes sources, and provides trends in AI engines like Claude and Gemini, with prompt suggestions and multi-language support. The platform emphasizes competitive benchmarking, trend analysis, and actionable prompt suggestions to steer content strategies across multilingual markets. With coverage of ChatGPT, Perplexity, and Deepseek, plus prompt volume data, Peec AI offers a balanced approach to monitoring and optimization for international brands looking to understand their competitive position across multiple AI platforms.

    BenchmarkingPrompt SuggestionsCompetitive Analysis

    Hall

    Hall website thumbnail

    Pricing: Free tier available; paid ~$49/month | Models Covered: ChatGPT, Perplexity, Gemini, Copilot, Claude, DeepSeek | Capabilities: Monitor & Improve | Prompt Volume Data: Unknown | Languages: Unknown

    Hall provides beginner-friendly tracking of brand mentions, citations, and AI agent behavior, with real-time dashboards and a free tier for small teams. Designed for smaller teams and solopreneurs, Hall offers intuitive real-time dashboards for monitoring mentions and agent behaviors with an approachable workflow. The platform covers six major AI assistants including ChatGPT, Perplexity, Gemini, Copilot, Claude, and DeepSeek, making it a solid entry-level option for brands just starting their GEO journey. The free tier makes it accessible, while paid plans unlock additional features for growing teams.

    SMBDashboardsFree Tier

    Otterly AI

    Otterly AI website thumbnail

    Pricing: ~$29/month | Models Covered: Unknown | Capabilities: Unknown | Prompt Volume Data: Unknown | Languages: Unknown

    Otterly AI monitors mentions in AI overviews and chatbots, with keyword research, reports, citation analysis, and prompt generation for solopreneurs and small teams. The platform targets budget-conscious solopreneurs with essential keyword research, citation analysis, and prompt creation tools to surface quick wins in AI search. At $29/month, it’s one of the most affordable options in the market, making GEO accessible for individual operators and very small teams. While specific model coverage details aren’t publicly disclosed, the platform focuses on core monitoring and optimization features that deliver immediate value.

    SolopreneurKeyword ResearchBudget-Friendly

    Promptmonitor

    Promptmonitor website thumbnail

    Pricing: ~$29/month | Models Covered: ChatGPT, Claude, Gemini, DeepSeek, Grok, Perplexity, Google AI Overview, AI Mode | Capabilities: Monitor & Improve | Prompt Volume Data: No | Languages: Unknown

    Promptmonitor offers multi-model prompt tracking across 8+ LLMs, AI crawler analytics, competitor monitoring, and source discovery with SEO metrics. The platform blends AI crawler analytics with SEO-integrated metrics, delivering a hybrid view of search and generative visibility. With coverage of 8+ major AI platforms including ChatGPT, Claude, Gemini, DeepSeek, Grok, Perplexity, Google AI Overview, and AI Mode, Promptmonitor provides comprehensive multi-model tracking at an affordable price point. The SEO integration makes it particularly valuable for teams already focused on traditional search optimization.

    SEO HybridCompetitor MonitoringMulti-Model

    Mentions.so

    Mentions.so website thumbnail

    Pricing: ~$49/month | Models Covered: Unknown | Capabilities: Unknown | Prompt Volume Data: Unknown | Languages: Unknown

    Mentions.so provides AI traffic analytics, performance updates, and white-label reports for tracking brand citations in generative AI. The platform packages comprehensive AI traffic analytics with white-label reporting capabilities, making it specifically tailored for agencies seeking professional client-facing GEO dashboards. While specific model coverage and language support details aren’t publicly disclosed, the platform’s focus on reporting and analytics makes it ideal for agencies that need to present GEO insights to multiple clients in a branded format.

    AgencyReportingWhite-Label

    Writesonic

    Writesonic website thumbnail

    Pricing: ~$79/month | Models Covered: ChatGPT, Gemini, Perplexity, 10+ platforms | Capabilities: Monitor & Improve | Prompt Volume Data: Yes | Languages: Unknown

    Writesonic offers GEO content creation and optimization, with AI article writing, topic analysis, and scanners for improving visibility in AI responses. The platform extends its established content engine with GEO-specific tooling, offering article generation tuned for AI responses and comprehensive visibility scans. With coverage of ChatGPT, Gemini, Perplexity, and 10+ additional platforms, plus prompt volume data, Writesonic combines content creation capabilities with GEO monitoring and optimization. This makes it ideal for content teams that want to both create and optimize content specifically for AI visibility.

    Content GenerationOptimizationPrompt Volume

    Semrush AI Toolkit

    Semrush AI Toolkit website thumbnail

    Pricing: ~$99/month (requires base subscription) | Models Covered: ChatGPT, Perplexity | Capabilities: Monitor & Improve | Prompt Volume Data: Yes | Languages: Unknown

    Semrush AI Toolkit is an add-on for prompt tracking, brand performance reports, and strategic insights integrated with broader SEO tools. As an expansion of Semrush’s comprehensive SEO suite, the AI Toolkit integrates prompt tracking and performance reports directly within familiar workflows. With coverage of ChatGPT and Perplexity, plus prompt volume data, it’s designed for teams already using Semrush who want to extend their SEO strategy into GEO. Note that this requires a base Semrush subscription, making it best suited for established SEO teams looking to add AI visibility tracking to their existing toolkit.

    SEO StackAdd-onIntegration

    AthenaHQ

    AthenaHQ website thumbnail

    Pricing: ~$295/month | Models Covered: ChatGPT, Perplexity, Google AI Overviews, AI Mode, Gemini, Claude, Copilot, Grok | Capabilities: Monitor & Improve | Prompt Volume Data: Unknown | Languages: Unknown

    AthenaHQ provides a dashboard for GEO scores, benchmarking, content gaps, and persona-based analysis across major AI platforms. The platform offers enterprise-grade benchmarking and persona-based analysis, mapping GEO scores across multiple AI surfaces. With coverage of 8 major platforms including ChatGPT, Perplexity, Google AI Overviews, AI Mode, Gemini, Claude, Copilot, and Grok, AthenaHQ delivers comprehensive visibility tracking. The persona-based analysis feature makes it particularly valuable for brands targeting specific customer segments or markets, though the higher price point positions it for larger organizations.

    EnterprisePersona InsightsBenchmarking

    Peekaboo

    Peekaboo website thumbnail

    Pricing: ~$100/month | Models Covered: Unknown | Capabilities: Unknown | Prompt Volume Data: Unknown | Languages: Unknown

    Peekaboo offers citation tracking for PR, monitoring mentions in AI engines, competitor visibility, and gap analysis. The platform supports PR teams with comprehensive citation tracking, competitor visibility analysis, and gap identification tailored to brand storytelling. While specific model coverage and technical details aren’t publicly disclosed, Peekaboo positions itself as a PR-focused GEO tool, making it ideal for communications teams that need to understand how their brand narrative appears in AI-generated content and identify opportunities to improve visibility.

    PRGap AnalysisCitation Tracking

    MorningScore

    MorningScore website thumbnail

    Pricing: ~$49/month | Models Covered: ChatGPT, Google AI Overviews | Capabilities: Monitor & Improve | Prompt Volume Data: Unknown | Languages: Unknown

    MorningScore provides SEO-integrated AI visibility monitoring, tracking citations in Google AI Overviews and content optimization. The platform adds AI visibility tracking to its established SEO platform, aligning traditional search KPIs with AI overview performance. With coverage of ChatGPT and Google AI Overviews, MorningScore bridges the gap between traditional SEO and GEO, making it ideal for teams that want a unified view of their search visibility across both traditional and AI-powered search experiences. The affordable pricing makes it accessible for small to mid-sized teams.

    SEO + GEOPerformance TrackingIntegrated

    SurferSEO

    SurferSEO website thumbnail

    Pricing: ~$79/month | Models Covered: Google, ChatGPT, other AI chats | Capabilities: Monitor & Improve | Prompt Volume Data: Unknown | Languages: English, Español, Français, Deutsch, Nederlands, Svenska, Dansk, Polski

    SurferSEO offers content optimization with AI Tracker for monitoring and improving brand appearance in AI responses. The platform’s AI Tracker evaluates content readiness for AI responses, supported by multilingual optimization insights. With support for 8 languages including English, Spanish, French, German, Dutch, Swedish, Danish, and Polish, SurferSEO is particularly valuable for international brands. The platform combines its established content optimization capabilities with GEO monitoring, making it ideal for content teams that want to optimize their existing content for AI visibility.

    MultilingualContent Optimization8 Languages

    Airank

    Airank website thumbnail

    Pricing: ~$49/month | Models Covered: Unknown | Capabilities: Unknown | Prompt Volume Data: Unknown | Languages: Unknown

    Airank tracks entity associations and brand rankings in grounded and un-grounded AI responses from Google and Gemini. The platform bridges knowledge graph monitoring with GEO outcomes, focusing on how entities and brands are associated and ranked in AI-generated content. While specific coverage details beyond Google and Gemini aren’t publicly disclosed, Airank’s unique approach to entity tracking makes it valuable for brands that want to understand their position within knowledge graphs and how that translates to AI visibility. The affordable pricing makes it accessible for teams exploring entity-based GEO strategies.

    Entity TrackingKnowledge GraphGoogle & Gemini

    AmionAI

    AmionAI website thumbnail

    Pricing: Beta pricing; ~$50/month | Models Covered: ChatGPT | Capabilities: Monitor & Improve | Prompt Volume Data: No | Languages: Multi-language (not listed)

    AmionAI provides brand monitoring with competitor rank, source analysis, sentiment, and actionable insights for early-stage users. The platform targets early-stage brands with comprehensive sentiment tracking, source analysis, and competitor rankings. Currently in beta with ChatGPT coverage, AmionAI offers multi-language support and focuses on delivering actionable insights for brands just beginning their GEO journey. The beta pricing makes it accessible for startups and small teams, though the lack of prompt volume data may limit its appeal for teams that prioritize that metric.

    SentimentEarly StageBeta

    Authoritas AI Search

    Authoritas AI Search website thumbnail

    Pricing: ~$119/month | Models Covered: ChatGPT, Gemini, Perplexity, Claude, DeepSeek, Google AI Overviews, Bing AI | Capabilities: Monitor & Improve | Prompt Volume Data: Unknown | Languages: Multilingual (customizes by language)

    Authoritas AI Search offers comprehensive tracking of mentions, share of voice, citations, and custom prompts across multiple LLMs. The platform unifies share-of-voice tracking with custom prompts and multilingual analytics for enterprise marketing teams. With coverage of 7 major platforms including ChatGPT, Gemini, Perplexity, Claude, DeepSeek, Google AI Overviews, and Bing AI, plus language customization capabilities, Authoritas delivers enterprise-grade GEO intelligence. The share-of-voice metrics make it particularly valuable for competitive analysis and market positioning strategies.

    Share of VoiceEnterpriseMultilingual

    ModelMonitor

    ModelMonitor website thumbnail

    Pricing: ~$49/month | Models Covered: Unknown | Capabilities: Unknown | Prompt Volume Data: Unknown | Languages: Unknown

    ModelMonitor tracks across 50+ AI models, with prompt radar, custom monitoring, competitor analysis, and sentiment. The platform broadens visibility with prompt radar, sentiment insights, and custom monitoring for large model portfolios. While specific model names and language support aren’t publicly disclosed, ModelMonitor’s claim of 50+ model coverage makes it one of the most comprehensive options for brands that need visibility across a wide range of AI platforms. The affordable pricing combined with extensive coverage makes it attractive for teams that prioritize breadth over depth in specific platforms.

    Wide CoverageSentiment50+ Models

    Quno

    Quno website thumbnail

    Pricing: ~$99/month | Models Covered: Unknown | Capabilities: Monitor & Improve | Prompt Volume Data: Unknown | Languages: Unknown

    Quno provides response and citation analysis using synthetic personas, with sentiment and keyword insights for brand intelligence. The platform leverages synthetic personas to evaluate responses, unpack sentiment, and spotlight keyword opportunities. While specific model coverage and language support aren’t publicly disclosed, Quno’s unique persona-based approach makes it valuable for brands that want to understand how different customer segments or personas experience their brand in AI-generated content. The sentiment and keyword insights add depth to the persona analysis, making it useful for targeted marketing strategies.

    PersonasSentimentKeyword Insights

    RankScale

    RankScale website thumbnail

    Pricing: Beta; ~$79/month | Models Covered: Unknown | Capabilities: Unknown | Prompt Volume Data: Unknown | Languages: Unknown

    RankScale is a GEO platform for audits, performance tracking, citations, and content recommendations in AI search engines. The platform delivers comprehensive GEO audits and performance tracking with competitive benchmarking for AI search engines. Currently in beta, RankScale focuses on providing actionable insights through audits and content recommendations, making it valuable for teams that want structured guidance on improving their AI visibility. While specific model coverage isn’t publicly disclosed, the audit and recommendation features make it useful for brands seeking a strategic approach to GEO optimization.

    AuditsBenchmarkingBeta

    Waikay

    Waikay website thumbnail

    Pricing: ~$19.95/month | Models Covered: ChatGPT, Google, Claude, Sonar | Capabilities: Monitor & Improve | Prompt Volume Data: No | Languages: 13 languages

    Waikay monitors brand representation with AI Brand Score, fact-checking, competitor comparison, and knowledge graph building. The platform introduces an AI Brand Score with fact-checking and knowledge graph insights, designed for multilingual reach. With coverage of ChatGPT, Google, Claude, and Sonar, plus support for 13 languages, Waikay offers comprehensive international coverage at one of the most affordable price points in the market. The fact-checking feature is unique and valuable for brands concerned about accuracy in AI-generated content about their business.

    Knowledge GraphMultilingualFact-Checking

    XFunnel

    XFunnel website thumbnail

    Pricing: ~$199/month | Models Covered: ChatGPT, Gemini, Copilot, Claude, Perplexity, AI Overview, AI Mode | Capabilities: Monitor & Improve | Prompt Volume Data: Yes | Languages: Unknown

    XFunnel provides visibility monitoring with competitive positioning, sentiment, and query segmentation by market/persona. The platform delivers competitive positioning and market segmentation insights, blending sentiment analysis with GEO performance. With coverage of 7 major platforms including ChatGPT, Gemini, Copilot, Claude, Perplexity, AI Overview, and AI Mode, plus prompt volume data, XFunnel offers enterprise-grade competitive intelligence. The query segmentation by market and persona makes it particularly valuable for brands targeting specific customer segments or geographic markets.

    Competitive IntelSentimentMarket Segmentation

    Clearscope

    Pricing: ~$170/month | Models Covered: ChatGPT, Gemini | Capabilities: Monitor & Improve | Prompt Volume Data: Unknown | Languages: Unknown

    Clearscope offers content optimization for AI visibility, with mention tracking and SEO integration. The platform couples SEO-grade content insights with GEO monitoring, helping teams refine drafts for AI responses. With coverage of ChatGPT and Gemini, Clearscope focuses on quality over quantity, providing deep content optimization guidance for brands that prioritize these high-traffic platforms. The higher price point reflects its established position in the SEO content optimization market, now extended to GEO, making it ideal for content teams already familiar with Clearscope’s workflow.

    SEO + GEOContent QualityContent Optimization

    ItsProject40

    ItsProject40 website thumbnail

    Pricing: ~$50/month | Models Covered: Unknown | Capabilities: Unknown | Prompt Volume Data: Unknown | Languages: Unknown

    ItsProject40 offers AI competitive monitoring and visibility tools for brands. The platform provides AI visibility tooling focused on competitive benchmarking and brand intelligence. While specific model coverage, capabilities, and language support aren’t publicly disclosed, ItsProject40 positions itself as a competitive monitoring solution, making it valuable for brands that prioritize understanding their position relative to competitors in AI-generated content. The mid-range pricing makes it accessible for small to mid-sized teams.

    Competitive MonitoringBrand Intelligence

    AI Brand Tracking

    AI Brand Tracking website thumbnail

    Pricing: ~$99/month | Models Covered: Unknown | Capabilities: Unknown | Prompt Volume Data: Unknown | Languages: Unknown

    AI Brand Tracking provides dedicated brand tracking in AI conversations with performance metrics. The platform zeroes in on dedicated brand monitoring across AI assistants with performance metrics tailored for marketing teams. While specific model coverage, capabilities, and language support aren’t publicly disclosed, AI Brand Tracking positions itself as a focused solution for brand monitoring, making it ideal for marketing teams that need straightforward performance tracking without the complexity of broader GEO optimization features. The pricing positions it in the mid-market range.

    Brand MonitoringPerformance MetricsMarketing Focus

    How to Choose the Right GEO Platform

    Match Coverage to Your Footprint: Start with the AI models that drive your customers’ journeys. Platforms like Spotlight and Authoritas offer the broadest footprint, while focused solutions like Profound excel within one primary assistant.

    Prioritize Visibility Gaps: Tools such as Scrunch AI and RankScale surface where you are absent in overviews or chat responses. Pair those insights with optimization-centric suites like Writesonic or SurferSEO to close gaps quickly.

    Weigh Prompt Intelligence: If understanding prompt demand is critical, lean toward Spotlight, Profound, or Promptmonitor for richer prompt volume and journey analytics.

    Design for Team Workflows: Agencies and enterprise teams benefit from white-label reporting (Mentions.so) or persona-based insights (AthenaHQ, Quno), whereas solo operators may prefer the simplicity of Otterly AI or Hall.

    Pricing and coverage reflect the latest available data as of November 2025 and may change. Verify current plans and feature sets directly with each provider.

  • 5 Best GEO / AEO Profound Alternatives (November 2025)

    5 Best GEO / AEO Profound Alternatives (November 2025)

    Profound made a buzz by being first to market in AI visibility tracking, but being first doesn’t mean being best. If you’re looking for alternatives—whether for better pricing, superior features, or a more accessible platform—here are five excellent options that often outperform Profound.

    With pricing starting at $499/month and a focus on enterprise clients, Profound isn’t the right fit for many teams. More importantly, several newer platforms have emerged that offer better features, more accessible pricing, and superior user experiences.

    That’s why I’ve researched and compiled this list of the best Profound alternatives. Each tool offers unique advantages, better pricing models, and serves different needs within the AI brand visibility ecosystem—often outperforming Profound in key areas.

    Whether you’re a startup on a budget, an agency managing multiple clients, or an enterprise looking for more specialized features, there’s an alternative here that will likely work better than Profound for your situation.

    What is an AI brand visibility monitoring tool?

    An AI brand visibility monitoring tool (also called AEO/GEO tools) tracks how your brand appears in AI-generated responses across platforms like ChatGPT, Gemini, Claude, Perplexity, Grok, and Google AI Overviews.

    These tools monitor two key metrics:

    • Mentions: When AI directly recommends your brand or product in its responses
    • Citations: When your content is used as a source to inform AI responses

    Think of it as Google Alerts, but specifically for AI search results. These platforms help you understand where and how your brand shows up in AI conversations, track trends over time, and identify opportunities to improve your visibility.

    Why look for Profound alternatives?

    While Profound was early to market, there are several compelling reasons to consider alternatives:

    • Pricing: At $499/month minimum, Profound is significantly more expensive than alternatives like Spotlight ($50/month) or Hall (free tier available)
    • Better value: Many newer platforms offer more features and better value at lower price points
    • Free features: Tools like Spotlight offer extensive free features (intelligent prompt discovery, citation tracking, prompt volume tools, Google Analytics integration, content scoring, reputation scoring) that Profound doesn’t provide
    • Accessibility: Profound’s enterprise focus makes it less accessible for smaller teams, while alternatives offer more flexible entry points
    • Superior features: Newer platforms have learned from early tools and often offer better sentiment analysis, competitive benchmarking, actionable insights, and unique capabilities like automatic prompt discovery
    • Risk-free testing: Alternatives like Spotlight offer free full pro access for audits, while Profound requires a significant upfront investment

    5 Best Profound Alternatives in 2025

    Here are the best alternatives to Profound for AI brand visibility monitoring:

    1. Spotlight
    2. Scrunch AI
    3. Hall
    4. Otterly.AI
    5. BrandLight

    Let’s dive into each one.

    1. Spotlight

    • Best for: Agencies and brands focused on comprehensive AI share of voice and reputation management
    • What I like: Intelligent prompt discovery, citation tracking, reputation scoring, and extensive free features
    • Pricing: Free version that includes full audit. Paid plans start at $50/month.

    Spotlight is a comprehensive AI visibility platform that stands out as one of the best alternatives to Profound. The platform monitors brands across 8 AI platforms: ChatGPT, Google AI Overviews, Google AI Mode, Grok, Gemini, Claude, Perplexity, and Copilot—offering features that often surpass what Profound provides at a fraction of the cost.

    What makes Spotlight exceptional is its intelligent approach to AI visibility tracking. Rather than just tracking prompts you manually input, Spotlight automatically discovers the most searched prompts that your brand would want to appear in—those that potential customers would ask when searching for products or services you offer. These prompts are grouped by topics aligned with your marketing objectives, and Spotlight identifies each prompt’s search volume to help prioritize your actions.

    The platform sends all prompts to all models weekly using a local IP to get local responses. Responses are then analyzed to:

    • Identify brand mentions and evaluate sentiment around those mentions
    • Compare your positioning against competitors
    • Capture and analyze all citations and data sources used in responses
    • Track queries that ChatGPT uses to fetch fresh data from the web
    • Produce visibility rankings, sentiment breakdowns, and actionable content suggestions

    Spotlight goes beyond basic tracking by reverse engineering what makes brands with high visibility succeed. The system analyzes what type of websites each model prefers to cite and creates an improvement plan based on those insights. It also performs gap analysis and suggests content that directly addresses prompts where your brand didn’t appear.

    But here’s what really sets Spotlight apart: their comprehensive feature set and generous free tools. Spotlight includes:

    • Citation Tracker: Tracks how often each piece of your brand-owned content was cited by each model over time
    • Content Grading Tool: Grades existing content and webpages, guiding how to optimize them from both technical and content perspectives based on GEO best practices
    • Google Analytics Integration: Connects to Google Analytics to pull traffic data and display how much traffic came from LLMs, which specific LLM, and which page that traffic landed on—closing the loop to show which content is actually bringing in traffic from AI models
    • Prompt Volume Tool: Discovers prompt volumes using multiple data sources including real-time prompt tracking, Google Search Console data, Google Trends, AdWords reports, and advanced AI models trained on human-AI interactions
    • Brand Reputation Section: Sends prompts asking models directly about your brand’s quality, value for money, and other key metrics, then scores the responses to provide a high-level view of how your brand is perceived by each model—including data sources so you can manage negative inputs

    Spotlight is particularly strong in sentiment analysis, competitive benchmarking, and actionable insights. The platform is trusted by agencies and is well-suited for teams that need to manage multiple brands or clients. Plus, Spotlight is built by AI agents, allowing for very fast development of new features and rapid adaptation to the high-paced changes in this field.

    Spotlight Pricing

    Spotlight’s pricing starts at $50/month, making it significantly more affordable than Profound’s $499/month entry point. But here’s the kicker: Spotlight offers free full pro access for a one-time audit, allowing you to experience all the platform’s premium features before committing to a paid plan.

    This means you can get a comprehensive audit of your AI visibility using Spotlight’s full feature set, see exactly what insights the platform provides, and then decide if you want to continue with a paid subscription. Spotlight also offers many free tools that provide real value even without a paid plan. This is a huge advantage over Profound, which requires a significant upfront investment just to see what you’re getting.

    Spotlight User Feedback

    Users praise Spotlight for its intelligent prompt discovery, comprehensive citation tracking, reputation scoring capabilities, and the value provided by its free features. Agencies particularly appreciate the automatic prompt discovery aligned with marketing objectives, the Google Analytics integration that closes the loop on traffic attribution, and the content grading tool that provides actionable optimization guidance. The tool is well-regarded for brand reputation management use cases, and users consistently note that it offers better value than more expensive alternatives like Profound.

    ⭐️ Overall Score: 5/5 stars

    2. Scrunch AI

    Scrunch AI
    • Best for: Teams needing granular, prompt-level tracking with content optimization suggestions
    • What I like: Detailed brand insights with AI content optimization recommendations
    • Pricing: Starts at $300/month (trial available, no free tier)

    Scrunch AI is a comprehensive AI brand presence tracking platform that offers granular, prompt-level visibility monitoring. What sets it apart from Profound is its focus on providing actionable insights, not just data.

    The platform tracks your brand across major AI platforms including ChatGPT, Perplexity, Gemini, and Google AI. It provides detailed insights into:

    • Prompt-level visibility tracking
    • Sentiment analysis of how your brand is being discussed
    • AI content optimization suggestions to fill visibility gaps
    • Multi-source monitoring across different AI platforms

    One of Scrunch AI’s standout features is its content optimization recommendations. While many tools just show you where you’re appearing (or not appearing), Scrunch AI actually suggests how to improve your content to better show up in AI responses.

    However, it’s worth noting that the platform focuses more on tracking and insights rather than direct content fixing tools. If you’re looking for a tool that tells you exactly what to do with the data, Scrunch AI is a strong choice.

    Scrunch AI Pricing

    Here are Scrunch AI’s pricing plans:

    • Starter: $300/month, track up to 350 prompts
    • Growth: $500/month, track up to 700 prompts
    • Pro: $1,000/month, track up to 1,200 prompts
    • Enterprise: Custom pricing with custom features

    While there’s no free tier, they do offer a trial so you can test the platform before committing. This is more affordable than Profound’s $499/month starting price, making it a solid alternative for teams that need detailed tracking but want to save some money.

    Scrunch AI User Feedback

    Users appreciate Scrunch AI’s detailed brand insights and the ability to track AI mentions at a granular level. The sentiment analysis feature is particularly well-received. However, some users note that the action tools are somewhat limited compared to the depth of insights provided.

    ⭐️ Overall Score: 4.5/5 stars

    3. Hall

    • Best for: Startups and teams of various sizes looking for flexible pricing
    • What I like: Free Lite plan, excellent prompt tracking, and real-time updates
    • Pricing: Free Lite plan, Starter $239/month, Business $599/month, Enterprise custom

    Hall is a Generative Engine Optimization (GEO) platform that offers one of the most accessible entry points into AI visibility tracking. What makes Hall a great Profound alternative is its flexible pricing model, including a free tier that actually provides real value.

    The platform offers:

    • AI mention tracking across major platforms
    • Share-of-voice reporting to understand your market position
    • Agent analytics for deeper insights
    • Conversational commerce monitoring (unique feature)
    • API access on higher tiers
    • Real-time updates and prompt tracking

    Hall’s free Lite plan includes 1 project with 25 tracked questions, which is perfect for startups or small teams that want to test AI visibility tracking without a financial commitment. This is a significant advantage over Profound, which requires a $499/month investment upfront.

    The platform also stands out for its conversational commerce monitoring feature, which helps you understand how your brand appears in AI-powered shopping experiences. This is particularly valuable for e-commerce brands.

    Hall Pricing

    Here are Hall’s pricing plans:

    • Lite: Free, includes 1 project with 25 tracked questions
    • Starter: $239/month, includes 20 projects with 500 tracked questions
    • Business: $599/month, includes 50 projects with 1,000 tracked questions
    • Enterprise: $1,499/month, includes API access and more

    Hall offers the most accessible pricing of any premium AI monitoring tool, making it an excellent choice for teams that want enterprise-level features without enterprise-level pricing.

    Hall User Feedback

    Users love Hall’s accessible pricing and the value provided by the free tier. The platform is praised for its strong project and prompt tracking capabilities, real-time updates, and user-friendly interface. Teams of various sizes appreciate the flexibility to scale as they grow.

    ⭐️ Overall Score: 4.8/5 stars

    4. Otterly.AI

    • Best for: SEO teams bridging traditional SEO with AI visibility
    • What I like: Structured GEO audit tool that provides systematic insights
    • Pricing: Pricing not always public; GEO Audit Tool available

    Otterly.AI takes a unique approach to AI visibility tracking by focusing on structured audits and systematic improvements. The platform is particularly valuable for SEO teams that want to bridge traditional SEO metrics with emerging AI visibility.

    Key features include:

    • GEO audit tool for comprehensive AI citation factor analysis
    • Brand and content citation tracking across ChatGPT, Perplexity, Gemini, and Google AI
    • Competitive benchmarking
    • Structured AI SEO audits that provide actionable recommendations

    What sets Otterly.AI apart is its focus on systematic improvements. Rather than just showing you where you appear, the platform provides structured audits that help you understand the factors impacting your AI visibility and how to improve them.

    This makes it an excellent choice for SEO professionals who are familiar with traditional SEO audits and want a similar structured approach for AI visibility. The platform helps bridge the gap between traditional SEO and AI search optimization.

    Otterly.AI Pricing

    Otterly.AI’s pricing is not always publicly available, but they offer a GEO Audit Tool that you can access. For detailed pricing information, you’ll need to contact them directly.

    Otterly.AI User Feedback

    Users appreciate Otterly.AI’s systematic approach to AI visibility tracking. SEO teams particularly value the structured audit format, which makes it easier to understand and act on the insights. The platform is well-regarded for helping bridge traditional SEO practices with AI visibility optimization.

    ⭐️ Overall Score: 4.3/5 stars

    5. BrandLight

    • Best for: Large enterprises and agencies managing extensive brand portfolios
    • What I like: Enterprise-grade analytics with advanced reporting and API integration
    • Pricing: $500+/month, enterprise level

    BrandLight is an enterprise-focused AI visibility platform that came out of stealth mode in April 2025 with a $5.75 million funding round. The platform is designed for large organizations that need advanced analytics, comprehensive reporting, and extensive integration capabilities.

    Key features include:

    • Advanced analytics and reporting
    • API integration for custom workflows
    • Competitive intelligence
    • Enterprise-grade security and compliance
    • Support for managing large brand portfolios

    BrandLight is positioned as a premium alternative to Profound for enterprises that need more sophisticated analytics and integration capabilities. The platform is well-suited for large marketing agencies or enterprise brands that manage multiple products or brands.

    While the pricing is high (starting at $500+/month), the platform offers enterprise-level features that justify the investment for organizations with extensive monitoring needs.

    BrandLight Pricing

    BrandLight pricing starts at $500+/month and scales based on your needs. For exact pricing, you’ll need to contact them for a custom quote tailored to your requirements.

    BrandLight User Feedback

    BrandLight is well-reviewed for large portfolios and enterprise use cases. Users appreciate the advanced analytics capabilities and the ability to manage multiple brands effectively. The platform is particularly well-regarded for its reporting and integration features, making it a strong choice for enterprise teams.

    ⭐️ Overall Score: 4.6/5 stars

    How to Choose the Right Profound Alternative

    When choosing between these Profound alternatives, consider the following factors:

    1. Budget and Pricing

    If budget is a concern, Spotlight offers exceptional value starting at just $50/month—a fraction of Profound’s $499/month. Plus, Spotlight’s free full pro access for a one-time audit lets you test everything before committing. Hall offers a free tier with paid plans starting at $239/month. Scrunch AI starts at $300/month, still more affordable than Profound. BrandLight starts at $500+/month, similar to Profound’s pricing.

    2. Feature Needs

    • Best overall value: Spotlight offers comprehensive features, free tools, and affordable pricing
    • Content optimization: Scrunch AI offers the best content optimization recommendations
    • Sentiment analysis: Spotlight excels at sentiment analysis and reputation management
    • Free tier: Hall is the only option with a truly free tier (though Spotlight offers free full pro access for audits)
    • Structured audits: Otterly.AI provides the most structured, audit-based approach
    • Enterprise features: BrandLight offers the most advanced enterprise capabilities

    3. Team Size and Use Case

    • Agencies: Spotlight is the top choice for agencies, offering comprehensive monitoring, competitive insights, and excellent value
    • Startups/small teams: Spotlight’s $50/month entry point and free audit make it ideal, or Hall’s free tier for basic testing
    • SEO teams: Otterly.AI bridges traditional SEO with AI visibility, or Spotlight for comprehensive monitoring
    • Enterprise: BrandLight offers the most comprehensive enterprise features, though Spotlight also serves enterprise needs well

    4. Platform Coverage

    All of these tools monitor the major AI platforms (ChatGPT, Gemini, Claude, Perplexity, Google AI). Spotlight supports the most comprehensive coverage with 8 platforms: ChatGPT, Google AI Overviews, Google AI Mode, Grok, Gemini, Claude, Perplexity, and Copilot. Hall offers unique conversational commerce tracking. Spotlight’s Google Analytics integration provides additional insights into traffic from AI models, showing which specific LLM and which pages are driving traffic.

    Final Thoughts

    While Profound made a name for itself by being first to market, these five alternatives often outperform it in key areas. Spotlight stands out as the top choice, offering comprehensive features including intelligent prompt discovery, citation tracking, reputation scoring, content grading, and extensive free tools (including prompt volume analysis, Google Analytics integration, and content scoring). With pricing starting at $50/month—90% less than Profound’s $499/month entry point—Spotlight offers exceptional value. Plus, their free full pro access for one-time audits removes the risk of trying the platform, and Spotlight’s AI agent-powered development means it adapts quickly to the fast-changing AI landscape.

    Whether you’re looking for the best overall value (Spotlight), more affordable pricing (Hall), better content optimization features (Scrunch AI), structured audits (Otterly.AI), or enterprise-grade capabilities (BrandLight), there’s an option here that will likely work better than Profound for your situation.

    The key is to identify your specific needs—budget, team size, feature requirements, and use case—and choose the tool that best aligns with those needs. Many of these platforms offer trials, demos, or free features (especially Spotlight), so take advantage of those to see which one feels right for your workflow.

    Remember, the best tool is the one you’ll actually use consistently. Don’t just choose based on name recognition or being “first to market”—choose based on what will help you make better decisions about your AI visibility strategy. In most cases, that’s going to be one of these alternatives rather than Profound.

  • GEO, AEO, AIO, LLMO, and AI SEO: What They Mean—and How They Differ

    GEO, AEO, AIO, LLMO, and AI SEO: What They Mean—and How They Differ

    The language of AI discovery is evolving quickly. Marketers, product teams, and SEOs are experimenting with new labels—GEO, AEO, AIO, LLMO, and AI SEO—to describe how brands get found across AI assistants, large language models, and search. Here’s a concise guide you can share with your team.

    TL;DR

    GEO = Generative Engine Optimization; optimize for AI‑generated answer engines (e.g., Perplexity, Google AI Overviews).

    AEO = Answer Engine Optimization; older umbrella for non‑traditional search that returns direct answers.

    AIO = AI Optimization; broad governance of data and content for AI use.

    LLMO = Large Language Model Optimization; make your brand quotable and fetchable by LLMs.

    AI SEO = AI‑era Search Strategy; applying SEO thinking to AI surfaces (answers, chat, summaries).

    None of these terms is a formal standard. Use the label that best fits your initiative and audience.

    Why these names exist

    Discovery has expanded beyond the ten blue links. People get answers from AI summaries, chat assistants, smart overviews, and aggregators. Teams coined new terms to signal scope and accountability: is the work about search engines, answer engines, AI governance, or model‑level visibility?

    Working definitions

    GEO — Generative Engine Optimization

    Focus: Visibility within generative answer engines that synthesize web sources into a single response.

    • Targets: Perplexity, Google AI Overviews, Arc Search, Bing Copilot answers, Brave Summarizer.
    • Levers: Source eligibility, citation‑worthiness, crawlability, structured data, freshness, authority.
    • Outcome: Appear as a cited source or be the canonical reference in generated answers.

    AEO — Answer Engine Optimization

    Focus: Earning placement in answer‑first experiences beyond classic search.

    • Targets: Featured snippets, knowledge panels, voice assistants, zero‑click cards, Q&A modules.
    • Levers: Concise answer formatting, entity linking, FAQ markup, authoritative sourcing.
    • Outcome: Your answer is read, quoted, or surfaced directly to users.

    AIO — AI Optimization

    Focus: Broad readiness for AI consumption across product, data, and content.

    • Targets: Data pipelines, content governance, licensing, model access, retrieval systems.
    • Levers: High‑quality corpora, clear rights, embeddings/RAG, consistent schemas, safety reviews.
    • Outcome: Your information is reliably usable by AI systems and compliant with policy.

    LLMO — Large Language Model Optimization

    Focus: Make your brand and facts discoverable and quotable by LLMs specifically.

    • Targets: Model pretraining signals, retrieval indexes, tools/plugins, model cards and evals.
    • Levers: Canonical facts pages, unique datasets, well‑structured docs, machine‑readable attributions.
    • Outcome: Models cite or use your content as the source of truth.

    AI SEO — AI‑era Search Strategy

    Focus: Apply SEO discipline to a world of AI‑mediated search.

    • Targets: Traditional SERPs plus AI overviews, chat answers, summaries, and shopping cards.
    • Levers: Topic authority, content depth, entities, structured data, UX performance, E‑E‑A‑T.
    • Outcome: Sustainable visibility across both search and AI answer surfaces.

    How they differ

    Term Primary scope Main goal Typical owner
    GEO Generative answer engines Get cited or used as a source SEO + Content + PR
    AEO Answer experiences (search/voice) Be the direct answer SEO + Content
    AIO Org‑wide AI readiness Make data usable by AI Product + Data + Legal
    LLMO LLMs and their toolchains Be a trusted, retrievable fact DevRel + Docs + SEO
    AI SEO Search + AI surfaces Compound visibility and traffic SEO

    When to use which term

    • Pitching content teams: use GEO or AI SEO to motivate answer‑surface visibility and citations.
    • Aligning with product/data: use AIO to frame AI readiness, governance, and rights.
    • Talking to developer relations: use LLMO to focus on docs, tools, and model retrievability.
    • Explaining legacy concepts: use AEO when connecting to snippets/voice lineage.

    Practical checklist

    For GEO / AI SEO

    • Publish definitive, citation‑ready explainers and data‑backed pages.
    • Add schema (FAQ, HowTo, Dataset, Product) where truthful.
    • Use canonical, stable URLs; optimize titles for answer intent.
    • Keep facts fresh; update last‑modified and changelogs.
    • Attract links from expert and news domains.

    For AIO / LLMO

    • Centralize a source‑of‑truth page for key facts and stats.
    • Provide machine‑readable artifacts (CSV/JSON) with clear licenses.
    • Document APIs and tools; enable retrieval with embeddings/RAG.
    • Track where models cite you; file feedback for misattributions.
    • Establish governance: quality thresholds, safety, and rights.

    FAQ

    Is GEO the same as AI SEO?

    No. GEO is narrowly about generative answer engines; AI SEO applies SEO thinking across all AI‑mediated search surfaces, including classic SERPs.

    Does AEO still matter?

    Yes. Many AI answers are built on the same signals that power snippets, knowledge panels, and entity graphs. Structuring answers is still foundational.

    What’s uniquely “LLMO” vs “AIO”?

    AIO is organizational readiness for AI broadly. LLMO focuses on making your content discoverable by large language models—pretraining exposure, RAG inclusion, tools, and citations.

    If your team prefers one label, use it consistently. What matters most is the operating model behind it: clear targets, measurable outcomes, and owners.

     

  • ChatGPT Stopped Citing Reddit in September—What This Means for Your AI Visibility Strategy

    ChatGPT Stopped Citing Reddit in September—What This Means for Your AI Visibility Strategy

    New data from Spotlight reveals a significant shift in how leading AI models, particularly ChatGPT and Google’s AI Overview, are sourcing information from Reddit. Our analysis of over 3 million citations between August 5 and October 29, 2025, shows a dramatic decline in Reddit’s presence in AI-generated responses, with profound implications for anyone focused on Generative Engine Optimization (GEO) or AI Engine Optimization (AEO).

    The Numbers Don’t Lie

    After analyzing daily citation patterns across eight major AI models, we uncovered some startling trends:

    ChatGPT’s 95% Drop

    ChatGPT’s relationship with Reddit underwent a dramatic transformation:

      • Early August 2025: Reddit citations peaked at 14.29% of all cited sources
      • Mid-September 2025: Dropped to 0.21%—essentially near-zero
      • By October 2025: Remained consistently below 1%

     

    This represents a 95% decline in just one month, transforming Reddit from a significant source to virtually absent in ChatGPT’s citations.

     

    AI Overview Follows the Same Pattern

    Google’s AI Overview, another major player, mirrored this trend:

      • Early August: Started around 4.5% Reddit citation share
      • Mid-September: Dropped below 1% (synchronized with ChatGPT’s decline)
      • October: Remained consistently below 1%, often near zero

     

    Perplexity Stands Out

    In contrast to ChatGPT and AI Overview, Perplexity maintained consistent Reddit citations throughout the entire period:

      • August-September: Consistently cited Reddit at 3-8% of sources
      • October: Maintained 2-5% citation share
      • Peak performance: Reached 8.89% on September 13th

    Perplexity became the leading AI model in terms of Reddit sourcing after ChatGPT’s decline, suggesting different underlying search or data access strategies.

     

    Other Models: Consistently Low

    Most other major AI models showed minimal Reddit engagement throughout:

      • Gemini: Rarely exceeded 1%, mostly stayed below 0.5%
      • Claude: Virtually no Reddit citations detected
      • Copilot: Minimal to zero Reddit presence
      • Grok: Flat line near 0% throughout the period
      • AI Mode: Fluctuated between 0-2%, generally very low

     

    What Caused This Change?

    The synchronized nature of the decline across multiple models points to a broader systemic change rather than individual algorithm updates. Several factors likely contributed:

    1. SERP Visibility Changes

    Since AI models primarily source fresh data from Search Engine Results Pages (SERPs), a reduction in Reddit’s visibility within Google search results would directly impact AI citations. Potential causes:

      • Google algorithm updates: Google may have adjusted how Reddit content ranks in search results
      • Reddit’s content structure changes: Changes to how Reddit presents content could affect crawlability
      • Competition shifts: Other platforms may have gained prominence in search results

     

     

    2. API and Data Access Changes

    Reddit has made significant changes to its API structure and pricing:

      • API access restrictions: Changes to how external services access Reddit data
      • Rate limiting: Stricter limits could impact search engine crawling
      • Data licensing: New policies might affect how search engines index Reddit content

     

    3. AI Model Training Updates

    While less likely to be synchronized across models, internal updates could play a role:

      • Retrieval Augmented Generation (RAG) updates: Changes to how models fetch real-time information
      • Source prioritization: Models may have adjusted internal weighting of different content sources
      • Quality filters: New filters might deprioritize forum-based content

    Critical Implications for Your AI Visibility Strategy

    For GEO/AEO Practitioners

    If your Generative Engine Optimization or AI Engine Optimization strategy relies on Reddit, this data is critical news:

    ⚠️ The Risk

    Relying solely on Reddit for AI visibility is now a high-risk strategy. Models that previously cited Reddit heavily have essentially stopped, which means:

      • Content posted on Reddit is less likely to appear in ChatGPT responses
      • Reddit-focused SEO tactics may no longer drive AI visibility
      • Investment in Reddit communities may show reduced ROI for AI citations

     

    ✅ The Opportunity

    Perplexity users still get value from Reddit—if Perplexity is part of your target model mix, Reddit content may still drive visibility there. However, this model-specific approach requires careful consideration.

    Strategic Recommendations

    1. Diversify Your Content Sources

      • Don’t put all your visibility eggs in one basket
      • Explore other high-authority platforms where your audience engages
      • Build presence across multiple channels (forums, Q&A sites, niche communities)

     

    2. Focus on First-Party Content

      • Prioritize content on your own website, blog, and official channels
      • You have direct control over this content and its discoverability
      • Optimize your own properties for AI model crawling and citation

     

    3. Monitor Continuously

      • AI citation patterns are dynamic—they change fast
      • Track which sources AI models cite for your industry and keywords
      • Use tools like Spotlight to monitor these shifts in real-time

     

    4. Understand Model-Specific Behaviors

      • Different AI models prioritize different sources
      • Tailor strategies to your target model mix
      • What works for Perplexity may not work for ChatGPT

     

    5. Build Authority, Not Just Links

      • Focus on creating authoritative, comprehensive content
      • Earn citations through quality, not gaming
      • Build relationships with platforms that consistently appear in AI responses

     

    The Bigger Picture

    This data reveals something important about the AI content landscape: it’s dynamic and unpredictable.

    What was true in August 2025 isn’t true in September. Strategies that work today may fail tomorrow. The only constant is change.

    This is why continuous monitoring and agile strategy adjustment are essential for anyone serious about AI visibility.

     

    What We’re Watching

      • Will Reddit citations return to ChatGPT? (Unlikely in the near term based on current trends)
      • How will other models adapt? (Perplexity shows a different path)
      • What new platforms will emerge as citation sources? (Opportunities may arise)
      • How will GEO/AEO strategies evolve? (The industry is responding)

     

    Conclusion

    The dramatic decline in Reddit citations by ChatGPT and AI Overview serves as a powerful reminder: strategies for AI visibility must be agile and data-driven.

    Don’t build your entire strategy on a single platform. Don’t assume today’s citation patterns will last. And most importantly—stay informed about these shifts, because they can happen overnight.

    The AI content landscape is still young, and the rules are being written in real-time. The brands that succeed will be those that monitor, adapt, and diversify.

    About the Data

    This analysis is based on Spotlight’s dataset of 3 million+ citations tracked between August 5 and October 29, 2025, across eight major AI models:

      • ChatGPT
      • Google AI Overview
      • Perplexity
      • Gemini
      • Claude
      • Microsoft Copilot
      • Grok
      • AI Mode

     

  • The SEO Clock Is Ticking: Why Brands That Wait on LLM Visibility Will Vanish

    The SEO Clock Is Ticking: Why Brands That Wait on LLM Visibility Will Vanish

    High-intent search has moved upstream. AI systems resolve most of it without sending traffic anywhere and the share of these answer-only sessions keeps compounding.

    Google admits AI Overviews appear in billions of results; Similarweb shows LLM referrals as a sliver of traffic, even as AI-style query sessions surge; and Perplexity and ChatGPT cohorts increasingly complete decisions in-flow. The spreadsheet says “not material.” The market structure says “material already happened.” CEOs who manage to the dashboard will be late by design.

    This piece argues that brands optimising for yesterday’s visibility layer are surrendering position in the layer that is actively setting tomorrow’s defaults. The absence of traffic is the signature of a platform transition in flight. Transitions have clocks: retrieval hierarchies harden, partnership rosters lock, and new habits congeal on 45–90 day cycles. Optionality decays exponentially.

    The framework: value chains and defaults

    Let’s start with value chains: who captures the margin as technology shifts? With search, value concentrated in ad marketplaces; publishers traded content for traffic; brands bought attention and built mental availability (the ease of coming to mind) through reach and distinctive assets. With LLMs, value concentrates one layer up, at the recommendation interface that converts questions into answers. The consumer’s “unit of work” is no longer clicking and comparing; it is accepting a default shortlist. The marginal click is worth less because the marginal answer is worth more.

    It would be a risk to think of answer engines as neutral pipes. They are retrieval stacks with preferences. The stack privileges (1) partners with APIs or data rights; (2) structured, citation-ready content; (3) fast, high-authority indices; and only then (4) the general web. That ranking of inputs is the new ranking of brands.

    In this world, traffic becomes a dividend. The asset is algorithmic presence. If you optimise for dividends while your competitor optimises for assets, you might still book near-term sessions, but you will wake up priced out of the default set.

    Why waiting re-prices your cost of entry

    There are three compounding mechanisms at work:

    Citation feedback loops. Sources cited more often become more likely to be retrieved later, independent of quality parity. This is network effect behaviour inside the model. Early mover benefit is mathematical as opposed to marketing folklore.

    Partnership and pipeline lock-in. Integrations across search APIs and content partners impose switching costs measured in months and model regressions. Once the platform picks a stack, reshuffling the deck is rare. Your window is before the pick.

    Habit formation and default bias. High-value cohorts (knowledge workers; professional buyers; premium consumers) settle into “ask the model first” workflows within 45–90 days. When the tool becomes the default colleague, the brands it habitually recommends become the user’s default consideration. We all know from experience that defaults are sticky even when switching is “free.” When switching has any friction; login, new UI, uncertainty, defaults dominate.

    Add those mechanisms and you get an exponential Cost of Late Entry curve: the longer you wait, the more you must spend, and the less your spend can achieve. It is not linear catch-up; it is paying more to get less.

    The measurement trap: managing to the wrong variable

    Executives love clarity, and “percent of traffic from LLMs” is a clear number. It is also the wrong number at this stage. Consider four distortions:

    Disintermediated success. If an LLM cites your brand, answers the user, and the user decides without clicking, you acquired awareness without traffic. That is success that looks like nothing in GA4.

    Attribution leakage. A meaningful share of LLM-originating visits arrives as “direct” or untagged because of in-app browsers, API flows, and privacy policies. Your tidy pie chart is lying to you with a straight face.

    Segment skew. Early AI users are disproportionately decision-makers and high-value spenders. Measuring volume without weighting value is unit-economics malpractice.

    Time-to-lock-in. The KPI that matters is not last-click revenue; it is Time-to-Citation-Lock-in: how quickly you become part of the machine’s default retrieval set before feedback loops harden.

    Put differently: the metrics that tell you to wait are inherently lagging; the metrics that tell you to move are leading and noisier. Strategy is choosing which noise to trust.

    What changes for brand building

    Mental availability still matters. The mechanism changes. Historically you built it with reach and distinctive assets so that when a buyer entered a category, your brand “came to mind.” In conversational interfaces, the model’s memory is the gatekeeper to the human’s memory. The practical translation:

    Distinctive assets remain crucial (they are the hooks that communicate signal quickly), but you must encode them in machine-parsable form, consistent product names, canonical claims, structured specs, conflict-free facts across touchpoints.

    Category entry points (CEPs) still matter, but they must be mapped to query intents expressed as questions (“Which CRM for a 50-person sales team with heavy outbound?”) rather than keywords (“best crm small business”).

    Broad reach still creates salience, yet citation frequency across trusted nodes (reference sites, standards bodies, credible reviewers) is now the shortest path into the model’s retrieval pathways. You need the model to “remember” you when it answers, even if the human does not click.

    When query-to-decision velocity compresses (e.g., from eight touchpoints to two), the premium on first impression explodes. The brand’s job is to be in those first two answers. Everything else is theater.

    Two tiers are emerging, and you must pick

    A candid look at the retrieval stack shows a two-tier market forming:

    Tier 1 (Participation Rights): API/data partners, canonical data providers, citation-optimised publishers. Benefits: priority indexing, fast retrieval, enhanced attribution, higher citation probability, occasionally preferential formatting in answers.

    Tier 2 (Commodity Access): Everyone else on the open web. Benefits: crawl inclusion with lag; unpredictable refresh; citation subject to chance and popularity elsewhere.

    This is not a Moral judgments no longer suffice; LLM architecture is the new reality. The strategic question is simple: do you pursue participation rights, or do you accept commodity status and plan to outspend it later? The former is a partnership and data discipline. The latter is a marketing tax with compounding interest.

    Predictions

    Platform consolidation: Within 24 months, three to five answer engines will control \>80% of AI-mediated discovery in Western markets (e.g., GPT-native search, Gemini/Google, Claude/Anthropic-aligned, and one “open web” challenger with strong browsing/citation transparency). Fragmentation beyond that is noise.

    Budget reallocation: By mid-2026, leading CMOs will allocate 10–15% of “search/SEO” spend to LLM Visibility Programs, including structured data pipelines, content refactors for citation-readiness, and API/partnership fees. By 2027 it will present in board decks as a standard line item.

    New KPI canon: “Brand Presence” (share of relevant queries where your brand appears) and “Partnership Advantage Ratio” (relative citation uplift from direct integrations) become standard competitive benchmarks; tool vendors and analyst firms will normalise them as category metrics.

    Retail and B2B shortlists compress: In categories where decision cycles can safely compress (consumer electronics accessories, SaaS categories with clear ICP fit), LLM answers will reduce the average number of visited options by 30–50%. Shelf space shrinks. Being off the shelf is existential.

    Late-entry tax becomes visible: By 2027, categories with meaningful LLM presence will exhibit a 3–10x cost premium for brands trying to enter the default set post-lock-in (seen as sustained SOV loss despite escalated spend). Analysts will misattribute this to “creative fatigue” or “market saturation”; the underlying cause will be retrieval position.

    Strategy: a 180-day program any CEO can mandate

    Let’s forget about moonshots for the moment and take a look at disciplined plumbing, organisational clarity, and a few hard trade-offs.

    Days 0–30: Governance, baselines, and data hygiene

    Appoint a Head of AI Discoverability (reporting to the CMO with dotted line to Product/Data). Give them budget, cross-functional remit and a powerful platform like Spotlight for a competitive advantage.

    Establish source of truth for product facts, claims, specs, and pricing. Build a daily export to a public, versioned, machine-parsable endpoint (JSON + schema).

    Run a Citation Audit across top answer engines and key prompts (category CEPs, competitor comparisons, buyer use cases). Score presence, position, consistency, and conflicts.

    Days 31–90: Structured presence and retrieval readiness

    Refactor top 50 evergreen pages into citation-ready objects: clear claims → evidence → references; canonical definitions; unambiguous names; inline provenance.

    Publish a Developer-grade Product Catalog (public or gated to partners) with IDs, variants, filters, and canonical images. Think “docs” for your products.

    Pursue one material partnership (e.g., data feed to a relevant answer engine, vertical marketplace, or respected standards body). Pay the opportunity cost of openness where needed.

    Days 91–180: Feedback loops and compounding

    Launch a Prompt-set QA: a stable suite of 200–500 prompts representing your buying situations. Track citation rate weekly in Spotlight. File model feedback where supported.

    Build a Citation Network Plan: placements in high-authority reference nodes (credible reviewers, associations, comparison frameworks). Not sponsorships, structured content with provenance.

    Pilot AI-native formats (decision tables, selector tools, explorable calculators) that answer engines love to cite. Ship them under your domain with clear licenses.

    Iintegrate this with brand: keep your distinctive assets consistent across the structured outputs. The machine needs to see the same names, colorus, claims, and relationships as the human.

    Organisational implications (the part nobody likes)

    Product owns facts. Marketing cannot be the fixer of inconsistent facts. Product and Data must own canonical truth; Marketing packages it.

    Legal becomes an enabler. Tighten claims to what you can prove and source. Over-lawyered ambiguity is now a visibility bug.

    Analytics changes its job. Build pipelines to detect AI-sourced visits and to estimate dark-referral uplift. Stop using “percent of traffic from LLMs” as a go/no-go gate.

    Agency relationships evolve. Brief agencies on citation engineering and partnership brokering, not just copy and backlinks. Insist on prompt-set QA in retainer scopes.

    Now for a brief breathe, pause and exploration of a contrarian view just to test our thesis

    Could LLM search fizzle like voice? Possibly. Falsifiers would include: persistent factual error rates that erode trust; regulatory bans on model outputs for product categories; or a consumer reversion to direct search due to cost or latency spikes. If any of those stick, traffic will remain with traditional search, and this investment will look early.

    But the option value of early presence is high and the bounded downside of disciplined investment is modest. A 10–15% budget carve-out spread across hygiene, structure, and one partnership yields reusable assets: cleaner facts, faster site, better catalogs, and a partner network that also benefits traditional search and retail syndication. In other words, even in the “LLM underperforms” world, you keep the plumbing upgrades.

    The revealed preferences of incumbents also matter: if the platform that profits most from clicks is embracing AI answers that reduce clicks, you should infer the direction of travel.

    The CEO’s decision: speed over certainty

    Great strategy is often choosing when to be precisely wrong versus roughly right. Here the choice is blunter: be early and compounding, or be late and expensive. You are not deciding whether LLM traffic matters; you are deciding whether defaults will be set without you.

    Translate that to a board slide:

    Goal: Achieve ≥30% citation rate across core buying prompts within six months in the top three answer engines serving our category.

    Levers: Canonical data feed live in 60 days; one material partnership signed in 90; top 50 pages refactored to citation-ready objects; prompt-set QA operational.

    Risks: Over-exposure of data; partnership dependence; shifting retrieval standards.

    Mitigations: License terms; multi-platform strategy; quarterly schema reviews; budget ceiling of 15% of search program.

    Payoff: Presence in the compressed consideration set; reduced CAC volatility as answer engines normalise; durable retrieval position before feedback loops harden.

    Pricing power migrates to the shortlist. When decisions compress, demand concentrates on defaults. Brands on the list can sustain price; brands off it compete only on discount and direct response.

    Moats look like boring plumbing. The edge is not a clever ad. It is a clean product catalog, consistent naming, fast indices, and contracts your competitors delayed.

    Measurement must graduate. Treat traffic as a downstream dividend. Manage to citation rate, partnership advantage, and time-to-lock-in. Report them to the board.

    Agencies and tools will re-segment. New winners will be those that operationalise structured truth and retrieval QA, not just backlink alchemy. Expect consolidation around vendors who can prove citation lift.

    Optionality has a clock. Windows close silently. If your decision-to-execution cycle is \> six months, the only winning move is to start now.

    When your competitor is building machine memory while you’re awaiting human clicks, you are not in the same game. You’re playing last year’s sport on this year’s field.

  • The Day Marketing Realised Its Audience Had No Pulse

    The Day Marketing Realised Its Audience Had No Pulse

    When machines started buying on our behalf, the world’s best storytellers found themselves pitching to code. Turns out, the algorithm doesn’t care about your brand voice, your mission statement, or your purpose. It just wants clean data and maybe a little confession of human weakness.

    The average American supermarket carries over 30,000 distinct items, or SKUs. For a century, the primary goal of a consumer packaged goods (CPG) company like Procter & Gamble or Unilever has been to win the battle for attention on that crowded shelf. They paid for eye-level placement, designed vibrant packaging, and spent billions on advertising to build a flicker of brand recognition that would translate into a purchase decision in the fraction of a second a human shopper scans an aisle. That entire economic model is predicated on a simple fact: the consumer is human.

    That fact is no longer a given.

    What happens when your weekly shop is automated or one of your customers says: “Hey Google, add paper towels to my shopping list.” Or, more disruptively: “Order me more paper towels.” There is no shelf. There is no packaging. There is no moment of cognitive battle between Bounty, with its quicker-picker-upper jingle stored in your memory, and the generic store brand. There is only an intent, an algorithm, and a transaction. The consumer, in the traditional sense, has been abstracted away. In their place is the Algorithmic Consumer, and marketing to it requires a fundamentally different strategy.

    This is a platform shift that threatens to upend the core tenets of brand, distribution, and advertising. The new gatekeepers are not retailers, but the AI assistants that mediate our interaction with the market. For businesses, the urgent strategic question is shifting from “How do we reach the consumer?” to “How do we become the machine’s default?”

    The Great Compression: From Funnel to API Call

    The classic marketing funnel: Awareness, Interest, Desire, Action (AIDA), is a model designed for the psychology of a human buyer. It’s a slow, expensive, and inefficient process.

    * Awareness is built with Super Bowl ads and billboards—blunt instruments for mass attention.

    * Interest is cultivated through content marketing and positive reviews.

    * Desire is manufactured through aspirational branding and targeted promotions.

    * Action is the final click or tap in a shopping cart.

    The AI assistant acts as a powerful compression algorithm for this entire funnel. The user simply states their intent: “I need paper towels.” The stages of Awareness, Interest, and Desire are instantly outsourced to the machine. The AI evaluates options based on a set of parameters and executes the Action. The funnel is compressed into a single moment.

    This has devastating implications for brands built on awareness. The billions of dollars spent by P&G on making “Bounty” synonymous with “paper towels” have created a cognitive shortcut for humans. An AI, however, has no nostalgia for commercials featuring clumsy husbands. It has an objective function to optimise. The machine’s decision might be based on:

    * Price: What is the cheapest option per sheet?

    * Delivery Speed: What is available for delivery in the next hour?

    * User History: What did this user buy last time?

    * Ratings & Reviews: What product has the highest aggregate rating for absorbency?

    * User Preferences: The user may have once specified “eco-friendly products only,” a constraint the AI will remember with perfect fidelity.

    The strategic imperative shifts from building a brand in the consumer’s mind to feeding the algorithm with the best possible data. Your API is your new packaging. The quality of your structured data: price, inventory, specifications, sourcing information, carbon footprint—is more important than the cleverness of your copy. This is the dawn of Business-to-Machine (B2M) marketing.

    Generative Engine Optimisation (GEO)

    For the past two decades, Search Engine Optimisation (SEO) has been the critical discipline for digital relevance. The goal was to understand and appeal to Google’s ranking algorithm to win placement on the digital shelf of the search results page. The coming paradigm is Generative Engine Optimisation (GEO), but it is different in several crucial ways.

    SEO is still fundamentally a human-facing endeavour. The goal is to rank highly so that a human will see your link and click it. The content, ultimately, must persuade a person.

    GEO is a machine-facing endeavour. The goal is to be the single best answer that the AI assistant returns to the user. Often, there is no “page two.” There is only the chosen result and the transaction. The audience is the algorithm itself.

    The factors for winning at GEO are not keywords and backlinks, but logic-driven and data-centric attributes:

    1. Availability & Logistics: An AI assistant integrated into a commerce platform like Amazon or Google Shopping will have real-time inventory and delivery data. A product that can be delivered in two hours will algorithmically beat one that takes two days, even if the latter has a stronger “brand.” The winner is not the best brand, but the most available and convenient option.

    2. Structured Data & Interoperability: Can your product’s attributes be easily ingested and understood by a machine? A company that provides a robust API detailing its product’s every feature—from dimensions and materials to warranty information and sustainability certifications—provides the AI with the granular data it needs to make a comparative choice. A company with a beautiful PDF brochure is invisible.

    3. Cost & Economic Efficiency: Machines are ruthlessly rational economic actors. If a user’s prompt is “order more paper towels,” and no brand is specified, the primary variable for the AI will likely be optimising for cost within a certain quality band. This is a brutal force of commoditisation. Brand premiums built on psychological messaging are difficult to justify to a machine unless they are explicitly encoded as a user preference (“I only buy premium brands”).

    The absurdity of this new reality can be humorous. One can imagine marketing teams of the future not brainstorming slogans, but debating the optimal JSON schema to describe a toaster’s browning consistency. The Chief Marketing Officer may spend more time with the Chief Technology Officer than with the ad agency.

    The Aggregation of Preference

    This shift fits perfectly within the framework of Aggregation Theory. The AI assistant platforms: Amazon’s Alexa, Google’s Assistant, Apple’s Siri and the LLMs building this out directly in their apps and websites

    1. They own the user relationship. They are integrated directly into our homes and phones, capturing our intent at its source.

    2. They have zero marginal costs for serving a user. Answering one query or one billion is effectively the same.

    3. They commoditise and modularise supply. The paper towel manufacturers, the airlines, the pizza delivery companies; they all become interchangeable suppliers competing to fulfill the intent captured by the Aggregator.

    The ultimate moat in this world is the default.

    When a user says “Claude, order a taxi,” will the default be Uber or Lyft? Anthropic will have the power to make that decision. It could be based on which service offers the best API integration, which one pays Amazon the highest fee for the referral, or it could be an arbitrary choice. The supplier is in a weak position; they have been disconnected from their customer.

    This creates a new, high-stakes battleground. The first time a user links their Spotify account to their Google Home, they may never switch to Apple Music. The first time a user says, “From now on, always order Tide,” that preference is locked in with a far stronger bond than brand loyalty, which is subject to erosion. It is now a line of code in their user profile. Winning that first transaction, that first declaration of preference, is everything.

    We will likely see three strategic responses from suppliers:

    * The Platform Play: Companies will pay exorbitant fees to be the default choice. This is the new “slotting fee” that CPG companies pay for shelf space, but on a winner-take-all, global scale.

    * The Direct Play: Brands will try to build their own “assistants” or “skills” to bypass the Aggregator. For example, “Ask Domino’s to place my usual order.” This works for high-frequency, single-brand categories but is a poor strategy for most products. Nobody is going to enable a special “Bounty skill” for their smart speaker.

    * The Niche/Human Play: The escape hatch from algorithmic commoditisation is to sell something a machine cannot easily quantify. Luxury goods, craft products, high-touch services, and experiences built on community and storytelling. These are categories where human desire is not about utility maximisation but about identity and emotion. The machine can book a flight, but it can’t replicate the feeling of being part of an exclusive travel club.

    The Strategic Humanist’s Dilemma

    This brings us to the human cost of algorithmic efficiency. A world where our consumption is mediated by machines is an intensely practical one. We might get lower prices, faster delivery, and more rational choices aligned with our stated goals (e.g., sustainability). This is the utopian promise: the consumer is freed from the cognitive load of choice and the manipulations of advertising.

    However, it is also a world of profound sterility. Serendipity, discovering a new brand on a shelf, trying a product on a whim, is designed out of the system. The market becomes less of a vibrant, chaotic conversation and more of an optimised, silent database. Challenger brands that rely on a clever ad or a beautiful package to break through have no entry point. Power consolidates further into the hands of the platform owners who control the defaults.

    The strategic implications are stark and urgent.

    1. For CPG and Commodity Brands: The future is B2M. Investment must shift from mass-media advertising to data infrastructure, supply chain optimisation, and platform partnerships. Your head of logistics is now a key marketing figure.

    2. For Digital Native Brands: Winning the first choice is paramount. The focus must be on acquisition and onboarding, with the goal of becoming the user’s explicit, locked-in preference.

    3. For All Brands: Differentiate or die. The middle ground of “decent product with good branding” will be vaporised by algorithmically-selected, cost-effective generics on one side and high-emotion, human-centric brands on the other. You must either be the most efficient choice for the machine or the most meaningful choice for the human.

    The age of marketing to the human subconscious is closing. The slogans, jingles, and emotional appeals that defined the 20th-century consumer economy will not work on a silicon-based consumer. The companies that will thrive in the 21st century are those that understand this shift, reorient their operations, and learn to speak the cold, ruthlessly efficient language of machines.

  • From SEO to Survival: The Three Biggest LLM Questions Leaders Can’t Ignore

    The buzz around generative AI is impossible to ignore. With McKinsey estimating it could add between $2.6 and $4.4 trillion in value to the global economy each year, it’s no wonder leaders are feeling the pressure to get their strategy right.

    But where do you even begin?

    We sat down with Dan, one of our in-house experts on LLM SEO, to cut through the noise and map out a practical path forward for any brand navigating this new landscape.

    Q1. What’s your advice for a business leader who is just starting to think about what LLMs mean for their company?

    Dan: The honest answer? Start with a dose of humility and a lot of measurement. There’s a ton of confident commentary out there, but the truth is, even the people building these models acknowledge they can’t always interpret exactly how an answer is produced. So, treat any strong claims with caution.

    Instead of getting caught up in speculation, get a concrete baseline. Ask yourself: for the questions and topics that matter to our business, do the major LLMs mention us? Where do we show up, and how do we rank against our competitors? We call this a “visibility score.” It takes the conversation from abstract theory to a tangible map you can actually work with.

    If you’re wondering why this is urgent, two external signals make it crystal clear. First, Gartner predicts that by 2026, traditional search engine volume could drop by 25% as people shift to AI-powered answer engines. That’s a fundamental shift in how customers will discover you. 

    Second, the investment and adoption curves are only getting steeper. Stanford’s latest AI Index shows that funding for generative AI is still surging, even as overall private investment in AI dipped. Together, these trends tell us that your brand’s visibility inside LLMs is going to matter more and more with each passing quarter.

    Q2. Once you know your visibility baseline, what should you do to move the needle?

    Dan: Think in two horizons:

    The model horizon (slow).

    Core LLMs are trained and fine-tuned over long cycles. Influence here is indirect: you need a strong, persistent digital footprint that becomes part of the training corpus. This is where classic disciplines: SEO, Digital PR, and authoritative content publishing still matter. High-quality, well-cited articles, consistent mentions in credible outlets, and technically sound pages are your insurance policy that when the next model is trained, your brand is part of its “memory.”

    The retrieval horizon (fast).

    This is where you can act immediately. Most assistants also rely on Retrieval-Augmented Generation (RAG) to pull in fresh sources at query time. The original RAG research showed how retrieval improves factuality and specificity compared to parametric-only answers. That means if you’re not in the sources LLMs retrieve from, you’re invisible; no matter how strong your legacy SEO is.

    This is why reverse engineering how machines are answering today’s queries is a strategic real-world data point. By mapping which URLs, articles, and publishers are being cited in your category, you uncover the blueprint of what LLMs value: the content structures, schemas, and PR signals they consistently lean on.

    From there, your levers become clear:

    Digital PR – Ensure your brand is mentioned in trusted publications and industry sources that models are already surfacing.
    SEO – Maintain technically flawless pages with schema, structured data, and crawlability, making your content easy for retrieval pipelines.
    Content strategy – Match the formats models prefer (lists, tables, FAQs, authoritative explainers), and systematically fill topical gaps.
    Analytics – Track citations, rank shifts, and model updates to iterate quickly.

    Q3. Let’s say you’ve mapped your visibility, identified the gaps, and set your priorities. What do you do on Monday morning?

    Dan: This is where you turn your analysis into action with briefs and experiments.

    First, audit what the models are already rewarding. Look at the URLs they cite as sources for answers on your key topics. For each one, study its:

    Structure: Does it have clear headings, tables, lists, and direct answers to common questions?
    Technical setup: How is its metadata, schema, and internal linking structured? Is it easy to crawl?
    Depth and coverage: How thoroughly does it cover the topic? Does it include definitions, practical steps, and well-supported claims?

    Doing this at scale can be tedious, which is why we use tools like Spotlight to analyse hundreds of URLs at once and find the common patterns.

    Next, create a “best-of” content brief. Let’s say for a key topic, ChatGPT and other AIs consistently cite five different listicles. Compare them side-by-side and merge their best attributes into a single master blueprint for your content team. This spec should include required sections, key questions to answer, table layouts, reference styles, and any recurring themes or entities that appear in the high-ranking sources. You’re essentially reverse-engineering success.

    Then, fill the gaps the models reveal. If you notice that AI retrieval consistently struggles to find good material on a certain subtopic; maybe the data is thin, outdated, or just not there; create focused content that fills that void. RAG systems tend to favour sources that are trustworthy, specific, and easy to break into digestible chunks. The research backs this up: precise, well-structured information dramatically improves the quality of the AI’s final answer.

    Finally, instrument everything and track your progress. Treat this like a product development cycle:

    Track how your new and updated content performs over time in model answers and citations.
    Tag your content by topic, format, and schema so you can see which features are most likely to get you included in an AI’s answer.
    Keep an eye out for confounding variables, like major model updates or changes to your own site, and make a note of them.

    This is critical because the landscape is shifting fast. That Gartner forecast suggests your organic traffic mix is going to change significantly. By reporting on your LLM visibility alongside classic SEO metrics, you can keep your stakeholders informed and aligned. You should get into a rhythm of constant experimentation. The AI Index and McKinsey reports both point to rapid, compounding change. Run small, fast tests: tweak your content structure, add answer boxes and tables, tighten up your citations, and see what moves the needle. Think of 2025 as the year you build your playbook, so that by 2026 you’re operating from a position of strength, not starting from scratch.

    Closing Thoughts

    Winning visibility in LLMs is about adapting to a fundamental shift in how people access knowledge and how machines assemble information. The path forward starts with three simple questions: Where do you stand today? Which levers can you pull right now? And how do you turn those levers into measurable experiments?

    The data is clear: the value on the table is enormous, your competitors are already moving, and the centre of gravity for discovery is shifting toward answer engines. The brands that build evidence-based content systems and learn to iterate in this new environment will gain a durable advantage as the market resets.

    Evidence & Sources

  • Which Domains Do AI Models Trust Most? A 60-Day Analysis of Citation Patterns

    In the rapidly evolving world of AI-powered search and content generation, understanding which sources AI models trust most is crucial for brands looking to optimize their visibility. Our latest analysis of over 850,000 citations across major AI models reveals fascinating patterns in domain preferences that could reshape your content strategy.

    Key Finding

    Each AI model has distinct domain preferences, with Wikipedia dominating ChatGPT citations (20,122), Reddit leading Perplexity (12,774), and YouTube topping Gemini trusted sources (1,821).

    The Methodology

    We analyzed citation data from our Spotlight platform, examining over 850,000 URL citations across seven major AI models over the past 60 days. The data reveals not just which domains get cited most frequently, but also the unique preferences of each AI model.

    ChatGPT: The Wikipedia Champion

    ChatGPT shows a clear preference for authoritative, encyclopedia-style content. Wikipedia dominates its citations with an astonishing 20,122 references in just 60 days.

    DomainCitationsDomain Type
    en.wikipedia.org20,122Encyclopedia
    reddit.com11,251Community
    techradar.com3,424Tech News
    investopedia.com1,530Financial Education
    tomsguide.com1,330Tech Reviews

    Insight: ChatGPT heavily favors established, authoritative sources. Wikipedia dominance suggests that comprehensive, well-sourced content performs exceptionally well with this model.

    Perplexity: The Community-Driven Model

    Perplexity shows a different pattern, with Reddit leading its citations at 12,774 references. This suggests Perplexity values real-world user experiences and community discussions.

    DomainCitationsDomain Type
    reddit.com12,774Community
    youtube.com6,345Video Content
    translate.google.com2,970Translation Tool
    play.google.com1,871App Store
    bestbrokers.com1,800Financial Services

    Insight: Perplexity preference for Reddit and YouTube suggests it values authentic user experiences and visual content. Brands should consider creating community-focused content and video materials.

    Gemini: The Google Ecosystem Player

    Google Gemini shows interesting patterns, with YouTube leading at 1,821 citations, followed by Google’s own Vertex AI Search at 1,631 citations.

    DomainCitationsDomain Type
    youtube.com1,821Video Content
    play.google.com1,261App Store
    investopedia.com1,072Financial Education
    pcmag.com1,059Tech Reviews

    Insight: Gemini heavy reliance on Google’s own tools and YouTube suggests strong integration within the Google ecosystem. Video content and Google-optimized materials may perform better with this model.

    Cross-Model Patterns: Universal Winners

    • Reddit: Top performer in Perplexity (12,774), strong in ChatGPT (11,251)
    • YouTube: Leading in Gemini (1,821), strong in Perplexity (6,345)
    • Investopedia: Consistently cited across ChatGPT (1,530), Gemini (1,072)
    • TechRadar: Strong performance across ChatGPT (3,424), Perplexity (1,208), Gemini (770)

    What This Means for Your Brand

    1. Model-Specific Strategies

    • For ChatGPT: Focus on comprehensive, encyclopedia-style content that could be referenced in Wikipedia
    • For Perplexity: Engage with community platforms like Reddit and create video content for YouTube
    • For Gemini: Optimize for Google ecosystem and create video content

    2. Universal Strategies

    • Create comprehensive, authoritative content
    • Engage with community platforms
    • Develop video content
    • Focus on expert reviews and technical analysis

    Key Takeaways

    1. Model Preferences Vary Significantly: Each AI model has distinct domain preferences that require tailored strategies.
    2. Authority Matters: Established, authoritative sources consistently perform well across models.
    3. Community Engagement Works: Platforms like Reddit show strong citation patterns, indicating value in community-focused content.
    4. Video Content is Powerful: YouTube strong performance across models suggests video content is highly valued.
    5. Industry-Specific Patterns: Financial services and technology sectors show particularly strong citation patterns.

    This analysis is based on data from Spotlight AI visibility monitoring platform, analyzing over 850,000 citations across seven major AI models over the past 60 days.