LLM Visibility Software: 14 Tools Compared for 2025

Your buyers are no longer Googling—they’re asking AI. If your brand doesn’t show up in those AI-generated answers, you’re losing business to competitors who are. 

LLM visibility software helps B2B SaaS companies track and improve how they appear in AI responses. If you’re a marketing leader who’s watched organic search traffic plateau or decline, these tools offer a new path to capture demand where buyers research products. 

We’ve broken down 14 leading LLM visibility platforms, explaining how they work and how to choose the right one for your needs and budget. 

What is LLM visibility software and why does it matter?

LLM visibility software tracks your brand presence in AI-generated responses across platforms like ChatGPT, Perplexity, Gemini, and other large language models. These tools monitor mentions, analyze sentiment, track citations, and compare your visibility against competitors. 

The research behavior of B2B buyers is shifting. Nearly half (40%) of B2B buyers now use AI tools for product research, according to Gartner. AI responses are the first touchpoint for prospects, shaping purchase decisions before buyers ever visit your website. 

Traditional SEO tools weren’t made for this shift. They track rankings and traffic from search engines, but they can’t tell you if ChatGPT recommends your product. Or if Perplexity cites your competitors over you. This is a blind spot in your digital strategy as AI-powered answer engines become critical channels for demand generation. 

LLM visibility software gives you greater clarity with:

• Brand mention tracking: When and how your brand appears in AI responses, including which queries trigger mentions and which platforms feature you most often. 
• Sentiment analysis: Whether AI presents your brand positively, negatively, or neutrally, so you know how prospects perceive you before they reach your site.
• Competitive intelligence: How often competitors appear versus your brand, revealing positioning gaps and opportunities to capture more mindshare. 
• Citation tracking: Which sources AI uses when mentioning your brand, so you can identify high-value content and create more of what works. 

Companies investing in LLM visibility give themselves a major advantage. AI platforms reward authoritative, well-structured content with more mentions and better positioning. The longer you wait to monitor and optimize, the harder it will be to catch up. 

Key metrics for auditing brand visibility on LLMs

LLM visibility software tracks metrics that connect to your business growth. Understanding these metrics will help you vet tools and improve your brand presence in AI-generated content. 

What you should measure: 

1. Share-of-voice and mentions

AI share-of-voice compares your brand alongside competitors. This is different from traditional search share-of-voice because AI platforms combine information rather than ranking links.

To calculate your AI visibility percentage, divide your brand mentions by total relevant mentions in your category. Then multiply that number by 100. If AI mentions your product 1,200 times and competitors 5,000 times, your share of voice is 24%. 

Benchmark your share-of-voice against competitors to find positioning gaps. If a competitor consistently appears in response to high-intent inquiries but you don’t, that’s an opportunity to improve content and authority in those areas. 

Track mention frequency across different AI platforms to spot trends. Some platforms may favor certain types of content or sources. You can use this info to help you focus your optimization efforts.  

2. Sentiment analysis 

Sentiment categorization classifies AI mentions as positive, negative, or neutral. Positive sentiment in AI responses can speed up purchase decisions, while negative sentiment deters prospects before they engage with sales. 

The impact is tangible. When an AI platform describes your product as ‘limited’ or ‘outdated’ versus ‘powerful’ or ‘innovative,’ it shapes buyer perception at a critical research stage. 

Identify and address negative AI perceptions by updating content, engaging with cited sources, or correcting inaccuracies. Some negative sentiment stems from outdated information that AI models still reference. 

Track sentiment trends over time to inform brand strategy and messaging. Sudden shifts in sentiment often indicate changes in the sources AI platforms cite, which help you understand which publications and platforms have the biggest influence.

3. Citation accuracy

Citation accuracy tracks how well AI models attribute (or fail to attribute) your content when generating responses. Accurate citations improve brand authority and trust, while missing citations are opportunities to establish expertise. 

Identify which of your pages AI references most frequently. This tells you what type of content AI platforms consider authoritative, so you can guide your content strategy toward formats and topics that generate more citations. 

Develop strategies to increase citation frequency and accuracy by creating reference-worthy content AI models can easily scan and attribute. Structured data, clear sourcing, and comprehensive coverage of topics improve the likelihood they’ll get cited. 

4. Prompt and response tracking

Prompt patterns reveal the common queries that trigger brand mentions. Knowing these patterns helps you optimize content for high-value search behaviors. 

Analyze how responses differ across query types and platforms. Some may generate detailed mentions on one platform but get generic responses on another. This shows you where you have strong or weak authority signals. 

Identify prompts that drive the most valuable mentions for your business. Not all are equal—references in response to bottom-funnel queries generate more pipeline than top-of-funnel awareness mentions. 

Track response consistency to ensure reliable brand representation. If AI platforms describe your product differently across queries, it might mean gaps in your authoritative content or conflicting information in your cited sources. 

How LLM monitoring tools track your brand

LLM monitoring tools have several ways to track brand mentions and analyze AI-generated content. When you understand the method, you can evaluate the capabilities and limitations. 

Here’s how they work:

• Automated query generation and testing: Tools create and send prompts to AI platforms to mimic user queries. They test variations of relevant searches to understand how AI responds across different phrasings and contexts. 
• API integration: Direct connections with major AI platforms allow for real-time data collection and monitoring. Some platforms offer official APIs for monitoring, while others require workarounds that may face restrictions. 
• Natural language processing (NLP): Advanced NLP analyzes AI responses for mentions, sentiment, and context. This technology spots brand references, even when they’re indirect or embedded in complex explanations. 
• Real-time monitoring vs. batch processing: Some tools send instant alerts when your brand appears in AI responses. Others process data in scheduled batches. Real-time monitoring offers faster insights, but it typically costs more.

Here’s how the main approaches compare: 

‍

Each method has trade-offs. API monitoring provides the cleanest data but depends on platform cooperation. Synthetic queries offer broad coverage, but may not perfectly replicate how real users phrase questions. User simulation generates authentic results but requires significant computational resources. 

The best tools blend methods to give you the complete picture of your AI visibility across platforms and queries. 

14 top LLM visibility tools compared

Now for the big reveal: the tools. Below we’ve included a detailed analysis of each of them, including the use cases and differentiators. 

1. Profound

Profound is an enterprise-grade AI visibility platform built for large organizations with global operations.

Key strength: SOC 2 Type II compliance and multi-region support make this the default choice for Fortune 1000 companies with strict security and privacy requirements.

Best for: Enterprise organizations that need comprehensive visibility across markets, robust security, and white-glove support.

‍Pricing: Custom enterprise pricing based on query volume and platform coverage.

‍Unique features: 

• White-glove onboarding
• Demographic filters for audience segmentation
• Advanced reporting with custom dashboards.

Profound is overkill for startups or small teams. But it's a good option when you need enterprise-level reliability and compliance.

2. Semrush Enterprise AIO

Semrush Enterprise AIO extends traditional SEO tracking into AI visibility. It's a natural fit for teams already using Semrush.

Key strength: Integrates AI tracking with existing SEO workflows, providing unified visibility across search engines and AI platforms.

Best for: Companies already using Semrush for SEO who want to add AI monitoring without switching platforms.

Pricing: Add-on to enterprise plans, with costs varying based on existing contract and feature needs.

Unique features: Unified dashboard for search and AI visibility, allowing teams to compare performance across channels and identify shifts in demand.

If you're not already a Semrush customer, the cost and complexity may not justify switching. But for existing users, this offers the smoothest path to AI visibility monitoring.

3. Lunary

Lunary is an open-source observability platform designed for technical teams building or monitoring LLM applications.

Key strength: Model-agnostic tracking with a free tier makes this accessible for developers and small technical teams.

Best for: 

• Developers
• Product teams
• Startups that need basic monitoring without enterprise pricing

Pricing: Free tier available; paid plans start at $29/month and scale based on usage.

Unique features: 

• LangChain integration
• Prompt evaluation tools
• Developer-friendly APIs

Lunary won't work for non-technical marketing teams. But it's a strong option for product-led growth companies where engineering and marketing collaborate closely.

4. Langsmith

Langsmith is LangChain's integrated monitoring solution, purpose-built for teams building LLM applications.

Key strength: Deep integration with the LangChain ecosystem, offering trace analysis and debugging capabilities.

Best for: Teams already building LLM applications with LangChain who need monitoring as part of their development workflow.

Pricing: Usage-based pricing model that scales with application volume.

Unique features: 

• Trace analysis
• Debugging capabilities
• Performance optimization tools

This is a developer tool, not a marketing tool. If your team builds LLM-powered features, Langsmith helps you track performance. For brand visibility monitoring, you'll need a different solution.

5. Trackerly.ai

Trackerly.ai tracks brand mentions across all major AI platforms. The platform provides daily automated updates and multilingual support.

Key strength: Comprehensive platform coverage, including ChatGPT, Google Gemini, Claude, Perplexity, and DeepSeek in one dashboard.

Best for: Agencies and brands that need to track visibility across AI platforms without juggling separate tools.

Pricing: Free 7-day trial with tiered plans based on prompt volume.

Unique features: 

• Bulk prompt management
• Position tracking within AI responses
• Source citation tracking
• No language limitations for prompts

Trackerly.ai's position tracking metric reveals where your brand appears within AI responses. Mentions at the top of responses drive more visibility than mentions buried at the bottom.

6. Brand Signal AI

Brand Signal AI helps marketers monitor brand mentions and prominence in AI model responses like ChatGPT.

Key strength: Campaign-based monitoring that lets you organize prompts by initiative or product line.

Best for: Marketing teams tracking multiple products or campaigns that need organized monitoring across initiatives.

Pricing: Premium subscription model with campaign-based organization.

Unique features: 

• Campaign structure for organizing prompts
• Prominence tracking
• Visibility trends over time

Brand Signal AI's campaign organization helps larger marketing teams coordinate monitoring efforts. This is invaluable if you're tracking across products, regions, or initiatives.

7. Peec AI

Peec AI focuses on real-time monitoring for agencies and mid-size brands.

Key strength: User-friendly dashboards and customizable alerts make this platform accessible for marketing teams without deep technical expertise.

Best for: Agencies managing multiple clients and mid-size brands that need clear reporting.

Pricing: Transparent tiered pricing based on query volume and platforms monitored.

Unique features: 

• Customizable alerts
• Client reporting templates
• Multi-account management

Peec AI strikes a balance between functionality and usability. This makes it a solid choice for teams that want comprehensive monitoring without enterprise complexity.

8. PromptWatch

PromptWatch specializes in prompt-level monitoring and optimization, helping teams understand which queries drive visibility.

Key strength: Granular prompt performance tracking that reveals exactly which queries generate brand mentions.

Best for: 

• Prompt engineers
• QA teams
• Content strategists who need detailed query analysis

Pricing: Developer-friendly pricing with transparent tiers.

Unique features: A/B testing for prompts allows teams to experiment with different query phrasings and optimize for visibility.

This tool is more specialized than others on this list. It works best when paired with broader monitoring tools, providing depth where others offer breadth.

9. Geo Suite

Geo Suite provides comprehensive generative engine optimization (GEO) capabilities beyond basic monitoring.

Key strength: Full GEO platform that not only monitors visibility, but also provides recommendations for improving it.

Best for: SEO teams expanding into AI optimization that want actionable recommendations alongside monitoring data.

Pricing: Professional pricing tiers based on features and query volume.

Unique features: 

• GEO recommendations
• Content optimization suggestions
• Competitive analysis

Geo Suite's strength is in optimization guidance. If you want a tool that tells you what to fix, not just what's broken, this one’s worth evaluating.

10. SearchAtlas AI Visibility 

SearchAtlas AI Visibility emphasizes historical trend analysis to help teams understand how AI visibility changes over time.

Key strength: Long-term visibility tracking that reveals patterns and predicts future trends.

Best for: SEO and SEM managers who want to correlate AI visibility with broader search performance.

Pricing: Competitive mid-market pricing with transparent tiers.

Unique features: 

• Historical data analysis
• Trend predictions
• Correlation with traditional search metrics

SearchAtlas works well for teams that think strategically about content and want to understand how AI visibility evolves alongside search performance.

11. MentionedBy AI

MentionedBy AI monitors brand visibility across 20+ AI models with competitive benchmarking.

Key strength: Extensive model coverage including base models, search models, and premium models with competitive comparison features.

‍Best for: Brands focused on competitive intelligence that need to track their position against rivals across many AI platforms.

Pricing: Tiered plans from $89/month (Starter) to custom enterprise pricing, with weekly or daily updates depending on plan.

Unique features: 

• Competitive benchmarking against up to 6 competitors
• AEO (Answer Engine Optimization) analytics
• Sentiment insights

MentionedBy AI's broad model coverage helps teams understand visibility patterns across the entire AI landscape rather than focusing on a few dominant platforms.

12. Truera LLM

Truera LLM focuses on explainability and compliance, making it ideal for regulated industries.

Key strength: AI explainability features that help teams understand why models generate specific responses.

Best for: Regulated industries like finance, healthcare, and legal services where compliance matters.

Pricing: Premium enterprise pricing reflecting specialized compliance features.

Unique features: 

• Compliance reporting
• Risk assessment
• Detailed explainability for AI-generated content

Truera is expensive, but if you're in a regulated industry, the compliance features justify the investment.

13. W&B LLM Monitor 

W&B LLM Monitor provides research-grade monitoring tools for teams that need deep analytics.

Key strength: Deep analytics and experimentation capabilities designed for ML researchers and data scientists.

Best for: 

• ML researchers
• Data scientists
• Technical teams building sophisticated LLM applications

Pricing: Academic and enterprise tiers with flexible licensing.

Unique features: 

• Experiment tracking
• Model comparison
• Detailed performance analytics

This is overkill for most marketing teams. But if you have data scientists on your team who want to analyze AI visibility alongside model performance, W&B offers unmatched depth.

14. Hall

Hall offers a self-serve platform with a free tier, making it accessible for startups and small teams.

Key strength: Accessible pricing with a free option that lets teams start monitoring without upfront investment.

Best for: 

• Startups
• Small teams
• Companies testing AI visibility monitoring before committing budget

Pricing: Freemium model with paid tiers adding features and query volume.

Unique features: 

• Self-service setup
• Basic tracking
• Straightforward reporting

Hall won't scale to enterprise needs. But it's still a smart entry point for teams that want to understand AI visibility before investing in more sophisticated tools.

How to choose the best AI visibility products for your business

We recommend this decision framework for choosing the right tool based on your needs and limitations. 

1. Define your objectives

Start by clarifying your AI visibility goals. Are you focused on brand awareness, lead generation, competitive intelligence, content optimization, or compliance? 

Ask yourself: 

• What AI platforms do your customers use most? 
• How critical is real-time monitoring for your business? 
• Do you need historical data for trend analysis? 
• What’s your team’s technical capability? 

Your answers will narrow down your options. If your buyers mostly use ChatGPT, you don’t need a tool that monitors 14 different AI platforms. If your team lacks technical expertise, cross out developer-focused tools. 

Consider your compliance and security requirements, too. Regulated industries need tools with strong data governance. Startups can focus on features over compliance certifications. 

2. Evaluate technical integrations

Think about how the tool integrates with your existing systems. Check API availability and limitations—some platforms restrict automation querying, which can limit functionality. 

Assess data export capabilities. You’ll want to analyze AI visibility alongside other marketing metrics, so easy exports to your BI tools or data warehouse are important.

Verify compatibility with your existing martech stack. If you use Semrush for SEO, adding their AIO module is simpler than learning a new platform. If you use Salesforce, tools that integrate with CRM data help connect AI visibility to your pipeline. 

Decide on your custom reporting requirements. Some teams need white-labeled reports for executives, while others want raw data for analysis. 

3. Check reporting and analytics features 

Look for dashboard customization options that let you cover the metrics your team cares about. Generic dashboards rarely provide the insights you need. 

Evaluate alert configuration flexibility. Can you set up alerts for specific competitors, sentiment thresholds, or mention patterns? Flexible alerts help you respond quickly to changes. 

Consider multi-user access and permissions. Enterprise teams need role-based access, and small teams often prioritize simplicity over specific permissions. 

4. Consider pricing and scalability 

Calculate the total cost of ownership, not just base pricing. Factor in implementation time, training costs, and ongoing management effort. 

Assess scaling costs as usage grows. Usage-based pricing gets expensive as you add queries, platforms, or users. 

Review contract terms and flexibility. Annual contracts lock you in, but month-to-month plans give you flexibility to test and adjust. 

Make a plan for measuring ROI. Great tools translate better AI visibility into business gains, like a bigger pipeline or lower customer acquisition costs. 

Steps to implement an LLM visibility strategy 

Ready to put your LLM visibility strategy in place? Follow these steps: 

1. Identify priority platforms 

Rank AI platforms by analyzing where your audience searches. Survey customers, review sales call recordings, or review support tickets to understand which AI tools buyers use. 

Then find out where your competitors are. If they dominate ChatGPT but ignore Perplexity, you might find opportunities in underserved platforms. Consider platform growth paths—platforms gaining market share deserve attention, even if they’re not dominant today. 

And be sure to align platform priorities with your business goals. Brand awareness efforts might target broad platforms, while enterprise sales focus on research-heavy platforms like Perplexity. 

2. Set up monitoring and auditing

Before using any tool, do an initial baseline audit. Document current visibility to measure improvement. 

Your team should also know how to use the tool, even if it’s intuitive. All platforms need training so teams can get the most value from them. As you’re learning to use the tool, document everything. Query sets, alert configurations, and reporting processes shouldn’t all live in one person’s head. 

3. Establish KPIs and baselines 

Choose meaningful metrics that tie back to your business. Share-of-voice matters more than raw mention counts if you’re in a competitive market. Set realistic targets based on your audit and resources. Doubling AI visibility in 30 days is unrealistic, but 20% improvement in 90 days is achievable with focused effort. 

Your measurement cadences should balance insight with efficiency. With monthly analysis, you can spot trends as they happen, but with weekly reporting, you get more timely feedback. 

KPIs and business goals must go hand in hand. If your goal is to grow your pipeline, track mentions in response to bottom-funnel queries, not just total visibility. 

4. Build a response plan for updates

As you receive alerts, monitor and respond to them quickly. Sudden drops in visibility or negative sentiment shifts need quick action. Be sure you have content update workflows for responding to opportunities. If AI mentions competitors instead of you, update your content to fill in the gaps. 

Finally, develop competitive response strategies. When your competitors gain visibility, figure out why and change your approach. Regular performance reviews help you assess what’s working and what needs adjustment. A monthly cadence forces you to stay proactive rather than reactive. 

How to improve LLM visibility after monitoring

These tactics can help you improve AI visibility based on what you observe: 

• Optimize content for AI consumption. AI platforms prefer clear, well-structured content with authoritative sources. Break complex topics into scannable sections, use descriptive headers, and cite credible sources. 
• Build topical authority in your niche. Full coverage of related topics signals expertise to AI platforms. Create clusters of related content that position your brand as the go-to resource. 
• Increase citation likelihood by creating reference-worthy content. Original research, data-driven insights, and detailed guides earn citations from AI platforms more often than promotional content. 
• Address negative sentiment proactively. If AI platforms cite outdated or inaccurate information, update your content and engage with the sources AI references to correct the record. 

Here are some other things you can do to ensure AI pays attention to your content: 

• Structured data implementation: Schema markup helps AI understand your content structure and authority. Organization, article, and FAQ schema improve how AI reads your pages. 
• Authoritative content creation: Publish research, case studies, and thought leadership that AI can recognize as expertise and that other sites can cite. 
• Strategic partnerships: Get more mentions through collaborations with publications and platforms AI frequently cites. Guest posts, data partnerships, and research collaborations all boost visibility. 
• Thought leadership: Showcase your execs as industry experts through speaking engagements, podcast appearances, and bylined articles. Personal brands often drive company mentions in AI responses. 

Real-world growth tactics to increase brand presence 

If you want to make your brand more visible, it comes down to execution. Some strategies have been shown to bring improvements over time: 

• Create AI-optimized content hubs. Build resource centers that go deep into topics. AI platforms like these sites. 
• Develop definitive guides that AI references. Long-form, authoritative guides are reference material for AI platforms. Update them regularly to maintain accuracy and relevance. 
• Build relationships with sources often cited by AI. If your monitoring shows that AI platforms always cite certain publications, pitch those publications with data and insights. 
• Optimize for featured snippets. Many AI platforms draw from featured snippets in traditional search results. Structured answers, clear definitions, and concise explanations improve both. 

What does this all look like in practice? Runpod, a GPU cloud computing platform for AI model deployment, turned ChatGPT into a top acquisition channel. They started monitoring their AI visibility and fixing technical issues that prevented proper citation. As a result, they grew to 40 new customers per day from ChatGPT with an 8% conversion rate. 

Moving forward with sustainable LLM visibility 

AI visibility is becoming as important as traditional search visibility. Over time, AI platforms develop preferences for sources and brands. If you know where you stand early, you can take advantage of opportunities to optimize your presence. 

But monitoring alone isn’t enough. You have to act on those insights, producing better content, securing stronger partnerships, and optimizing consistently. 

If you’re ready to build a comprehensive AI visibility strategy, Ten Speed can help. Our team has extensive technical experience with proven content strategies. We help B2B SaaS companies optimize for AI platforms while building sustainable organic growth across channels. 

Book a call with our team today to review your company’s growth goals and get a customized proposal! 

Frequently asked questions about LLM visibility software 

What are the privacy concerns in storing LLM monitoring data? 

Most enterprise LLM monitoring tools follow SOC 2 and GDPR standards, storing only aggregated response data without personal information. But you should verify each platform's specific data retention and privacy policies beforehand. Ask vendors about data storage locations, retention periods, and whether they use your data to train models.

How can I measure direct ROI from these tools?  

Track pipeline influence by monitoring which prospects mention AI-sourced information during sales calls. Correlate improvements in AI visibility with increases in branded search traffic and direct website visits. The strongest ROI signal comes from connecting visibility improvements to pipeline growth, which requires tagging opportunities by source and analyzing patterns over time.

Are there regulations that limit how I collect LLM data?  

Current regulations don't specifically restrict LLM monitoring, but platforms' terms of service may limit automated querying. Choose tools that have compliant relationships with AI providers to avoid service disruptions. Some platforms offer official monitoring APIs, while others use workarounds that may face restrictions as AI providers update their policies.