What is the difference between LLM monitoring and AI brand monitoring?

LLM monitoring is a broader term that covers tracking how any large language model handles your brand, content, or domain - including both search-enabled and standalone AI interactions. AI brand monitoring is a subset focused specifically on AI search results. In practice, the terms are often used interchangeably in marketing contexts.

Which LLMs should brands monitor?

At minimum, monitor ChatGPT (OpenAI), Perplexity AI, Google Gemini, and Microsoft Copilot. These four platforms represent the vast majority of AI search traffic. ChatGPT alone drives roughly 85% of AI-sourced referral traffic to websites. Add Claude and DeepSeek if they are relevant to your audience.

How much does LLM monitoring cost?

LLM monitoring tools range from free manual methods to enterprise platforms costing several hundred to several thousand dollars per month. Otterly.ai and Peec AI offer plans starting around 50 to 200 dollars per month for startups. Manual monitoring through regular query checks costs only time but does not scale well.

Can you influence what LLMs say about your brand?

Yes, indirectly. You cannot edit an LLM's responses directly, but you can influence them by publishing structured, authoritative content that AI models pull from. Consistent publishing, cross-platform mentions, and technical optimization like structured data all shape how LLMs represent your brand over time.

What Is LLM Monitoring?

LLM monitoring is the practice of tracking how large language models - the AI systems powering ChatGPT, Perplexity, Google Gemini, Microsoft Copilot, and similar tools - reference, represent, and cite your brand, products, or content in their generated responses. For marketing teams, LLM monitoring means systematically understanding whether your brand appears when users ask AI models questions relevant to your business, and whether the information those models present is accurate and favorable.

Why LLM Monitoring Has Become Essential

Large language models have become a primary information source for millions of users. ChatGPT reached over 500 million weekly active users by mid-2025, and the broader category of AI-powered search tools continues to grow rapidly.

When someone asks an LLM "what is the best tool for [your category]," the model generates a response that may or may not include your brand. Unlike traditional search where you can check your ranking in Google, LLM responses are dynamic - they can change with every query, every model update, and every new piece of content the model encounters during web search.

This unpredictability is precisely why monitoring matters. Without systematic tracking, you have no way to know:

Whether LLMs mention your brand for your core queries
Whether the information they present is accurate
Whether your visibility is improving or declining
Which competitors appear instead of you

A study of nearly 2 million LLM sessions across nine industries found that brand visibility in AI responses varies significantly by category and query type, reinforcing the need for ongoing monitoring rather than point-in-time checks.

What LLM Monitoring Covers

LLM monitoring for marketing spans several areas:

Brand Mention Tracking

The most basic form of LLM monitoring tracks whether your brand appears in AI-generated responses. This includes:

Direct brand mentions by name
Product or feature mentions
Links and citations to your website
Indirect references ("tools like [your brand]" or "companies in this space include...")

Sentiment and Accuracy Analysis

LLMs can say things about your brand that are wrong, outdated, or negatively framed. Monitoring sentiment and accuracy helps you catch:

Factual errors about your product features or pricing
Outdated information from older training data
Negative characterizations that do not reflect current reality
Confusion between your brand and competitors with similar names

Competitive Intelligence

LLM monitoring reveals which competitors appear in AI responses for your target queries. This competitive intelligence shows you:

Which brands dominate share of voice in AI search
What content competitors have that earns citations
Which queries represent opportunities where no strong competitor is cited

Source Analysis

When LLMs cite sources, monitoring tracks which of your pages get cited most often and for which queries. This reveals your strongest content assets and identifies pages that need optimization to earn more AI citations.

LLM Monitoring Tools

The LLM monitoring tool category has grown significantly as demand for AI search visibility has increased. Backlinko identified over 20 tools focused specifically on tracking brand visibility across LLMs.

Otterly.ai tracks brand mentions across ChatGPT, Perplexity, Google AI Overviews, and other platforms. It monitors changes over time and alerts you to shifts in visibility.

Peec AI provides competitive analysis and share of voice tracking. It identifies which queries cite competitors instead of you and suggests content opportunities to close gaps.

Profound focuses on deep analytics around why specific sources get cited. It analyzes the content characteristics and authority signals that drive LLM source selection.

Semrush AI Visibility Toolkit integrates LLM monitoring alongside traditional SEO tools, making it practical for teams already using Semrush for search analytics.

How to Start Monitoring LLMs

For startups and small teams, here is a practical approach to starting LLM monitoring:

Step 1: Define your query set. List 20-50 queries that matter most to your business. Include branded queries, category queries, comparison queries, and how-to queries.

Step 2: Run baseline audits. Search each query in ChatGPT, Perplexity, and Google with AI Overviews enabled. Document which brands appear, what sources get cited, and whether your brand is mentioned.

Step 3: Set up automated tracking. Use a tool like Otterly.ai or Peec AI to monitor your query set automatically. This saves time and catches changes you would miss with manual checks.

Step 4: Review and act weekly. Review your monitoring data weekly. When you find gaps - queries where you should appear but do not - create or optimize content to address them. When you find inaccuracies, publish corrective content.

Step 5: Track trends monthly. Look at your AI visibility trends over time. Are you appearing in more or fewer AI responses month over month? Is your share of voice growing or shrinking?

The Connection Between LLM Monitoring and GEO

LLM monitoring and Generative Engine Optimization work together as a feedback loop. GEO is the practice of optimizing content for AI search visibility. LLM monitoring is how you measure whether your GEO efforts are working.

Without monitoring, you are optimizing blind - publishing content and hoping it gets cited. With monitoring, you can see exactly which content earns citations, which queries are underserved, and where your optimization efforts need to focus next.

For startups, this feedback loop is especially valuable because resources are limited. You cannot afford to waste content production on topics where you already have strong AI visibility. LLM monitoring ensures every piece of content you publish addresses a real gap in your AI search presence.