What tools actually track your brand across AI platforms?

Traditional analytics miss most of your AI visibility. Someone asks Perplexity about digital consulting, gets an answer mentioning your firm, never clicks through. Google Analytics shows nothing. ChatGPT cites your methodology in a response to 500 users this week. You have no idea it happened.

LLM tracking tools solve this by automatically submitting queries to multiple AI platforms, parsing responses for brand mentions, and showing you what percentage of relevant searches actually surface your content.

Why traditional measurement doesn’t work here

Google Analytics tracks visitors who land on your site. Pretty straightforward when someone clicks a search result or follows a link.

AI search works differently. User gets the answer directly. Your brand appears in the response text. User reads it, forms an opinion about your expertise, moves on. No click, no pageview, no session recorded anywhere.

This creates a measurement blindspot. You might be getting cited in hundreds of AI responses weekly while your analytics show zero AI-related activity.

The volume issue makes manual checking impossible. Testing even 50 queries across three platforms (ChatGPT, Perplexity, Google AI Overviews) weekly means 150 manual tests. That’s hours of copying queries, waiting for responses, documenting results. Most organizations can’t sustain that.

What these tools actually do

They automate the tedious part. You provide a list of queries (questions people ask in your domain), the tool submits them to selected AI platforms on a schedule, extracts brand mentions from responses, tracks competitor citations for comparison, and reports changes over time.

Looking at Otterly.AI’s feature set (https://otterly.ai/), they track “ChatGPT, Perplexity, Google AI Overviews, AI Mode and other AI” platforms automatically. According to their blog (https://otterly.ai/blog/monitor-keywords-ai-overviews-chatgpt-perplexity/), “In our Keyword Monitoring Overview, Otterly.AI shows you the most important information. You can see all your keywords in one view.”

Semrush’s AI Visibility Toolkit (https://www.semrush.com/kb/1493-ai-visibility-toolkit) helps “track brand visibility, analyze competitors, monitor prompts, and optimize for AI-driven search.”

The practical value comes from comparison data. Seeing that you get cited in 15% of tracked queries while your main competitor appears in 40% tells you something actionable. Knowing you dominate Perplexity but barely register in ChatGPT reveals where to focus effort.

Which platforms do these tools actually cover

Platform coverage matters because different AI systems serve different audiences and have different citation behaviors.

Google AI Overviews gets tracked by most tools since it’s integrated into traditional Google search. Anyone searching Google encounters these AI-generated summaries when they appear.

Perplexity appears in nearly every tracking tool. Its explicit citation behavior and growing user base make it a priority target for visibility monitoring.

ChatGPT gets covered by major tools, though its search features are newer than its chat interface. Tracking what ChatGPT cites when users ask questions (particularly with search enabled) shows a different audience from traditional search.

Gemini (Google’s standalone AI) has growing coverage as adoption increases.

Claude gets tracked by some tools but less universally than ChatGPT or Perplexity.

Before buying a tool, verify which specific platforms it monitors. Missing coverage for a platform where your audience concentrates creates a blindspot in your measurement.

Pricing ranges wildly based on what you need

Entry-level plans often start around $100-200 monthly with limits on query volume and platform coverage. You might get 50-100 queries tested across 2-3 platforms with weekly or monthly updates.

Mid-tier plans ($300-500 monthly) expand query limits to 200-500, add more platforms, provide more frequent testing (daily instead of weekly), and include competitor tracking for 2-3 competitors.

Enterprise plans go higher (often custom pricing) with thousands of queries, full platform coverage, real-time alerting, API access for integration with your dashboards, and dedicated support.

Most tools offer free trials or demo accounts. Test before committing since interface usability, reporting quality, and actual platform coverage vary significantly between tools claiming similar features.

Otterly.AI starts at $29 monthly for around 15 prompts, making it accessible for smaller teams testing visibility on a limited query set. The Standard plan runs $189 monthly for 100 prompts, which works for mid-sized teams tracking core questions across ChatGPT, Perplexity, Google AI Overviews, and AI Mode. Probably the most approachable entry point if you’re just starting to measure beyond traditional analytics (https://otterly.ai/pricing, https://www.rankability.com/blog/otterly-ai-review/).

Semrush AI Visibility Toolkit costs $99 monthly as a standalone addition to existing Semrush subscriptions. You get one domain, one user, and 300 daily queries tracking ChatGPT, Perplexity, Google AI Overviews, and Gemini. The Grow plan expands platform coverage and query volume. If you’re already paying for Semrush, this becomes a relatively affordable add-on rather than another vendor relationship. The lowest tier limits you to ChatGPT only; Perplexity and Google AI Overviews appear at higher tiers (https://www.semrush.com/kb/1493-ai-visibility-toolkit, https://www.tryprofound.com/blog/semrush-ai-visibility-toolkit-review).

Peec AI positions somewhere between consumer and enterprise. Reviews mention it as more budget-friendly than enterprise tools but with serious features including competitor benchmarking and optimization guidance. Pricing seems to range from roughly €199 monthly upward depending on query volume and platform coverage (https://zapier.com/blog/best-ai-visibility-tool/, https://www.tryaivo.com/blog/best-ai-visibility-monitoring-tools-2025-profound-peec-comparison).

Profound operates at the enterprise tier. The base plan reportedly starts around $99 monthly for ChatGPT-only tracking, but full three-engine coverage including ChatGPT, Perplexity, and AI Overviews runs about $399 monthly. Enterprise plans with SOC 2 Type II certification, real-time monitoring across 10+ AI engines, and advanced features cost significantly more. This tool targets organizations treating AI visibility as critical infrastructure rather than experimental measurement (https://www.reddit.com/r/b2bmarketing/comments/1p8ck0g/i_found_15_ai_visibility_tool_in_the_market_right/, https://nicklafferty.com/blog/profound-vs-peec-ai/).

ZipTie gets mentioned primarily for deep analysis and developer-friendly features. Pricing details seem less publicly documented, suggesting it may target technical teams comfortable with API-driven workflows rather than plug-and-play dashboards (https://ziptie.dev/blog/best-tools-for-tracking-brand-visibility-in-ai-search/, https://zapier.com/blog/best-ai-visibility-tool/).

The practical consideration here isn’t “which tool is best” but “what query volume can you actually act on.” If you’re testing 15 strategic queries, Otterly’s Lite plan works fine. If you need daily monitoring of 200+ queries across multiple competitors, you’re looking at Semrush Standard, Peec, or Profound depending on platform coverage requirements.

Platform coverage matters more than features sometimes. Most tools track Google AI Overviews, ChatGPT, and Perplexity as baseline. Gemini, Claude, and Microsoft Copilot coverage varies. If your audience disproportionately uses a specific platform based on demographic research, prioritize tools covering that engine even if they’re weaker elsewhere.

Picking your query list probably matters more than tool selection.

Query selection determines everything

Tool sophistication doesn’t matter if you’re tracking the wrong questions. Start with 15-30 queries representing actual customer intent. What questions lead people to need your solution? What do they ask when comparing options? What educational searches happen before they understand their problem well enough to search for vendors?

Core business queries (what you actually do), competitor comparison queries (your brand versus alternatives), problem-focused queries (the pain points that create demand), educational queries (concepts where you want thought leadership visibility).

Most teams guess at their query list initially. One month of tracking reveals which queries actually matter. Some queries you thought were critical show zero AI citations for anyone. Other queries you overlooked turn out to generate significant visibility opportunities.

That’s when you expand to 50-100 queries, covering variations and long-tail permutations of the core set that proved relevant.

Going beyond 100 queries makes sense for large enterprises or agencies managing multiple clients, but smaller organizations often struggle to take meaningful action on that much data. Better to deeply optimize for 40 priority queries than shallowly monitor 200.

Metrics that actually inform decisions

Citation frequency shows raw visibility. How many times do you appear per 100 queries tested? Below 10% means you’re essentially invisible. 20-30% indicates solid presence. Above 40% suggests market-leading visibility for your query set.

Share of voice reveals competitive positioning. If Competitor A gets cited 40% of the time and you appear 15% of the time on identical queries, that gap becomes the strategic opportunity rather than vague “we should rank better” goals.

Platform distribution shows where your content resonates. You appear in 35% of Perplexity responses but only 8% of ChatGPT answers? That platform preference signals either content format alignment or underlying training data differences worth investigating.

Position tracking matters when you get cited alongside four other brands. First citation seems to carry more perceived authority than fourth citation, though hard data on user behavior here remains limited as of late 2025.

Link citation ratio (how often your citation includes a clickable link versus just a mention) directly impacts whether visibility converts to site visits. Mentions build awareness. Link citations drive traffic. Both matter, but differently.

Hardik Shah of ScaleGrowth.Digital notes, “We track 200 queries weekly for enterprise clients, but really make decisions based on 40 priority queries where movement indicates strategic progress or regression. The rest provide context and catch unexpected opportunities, but trying to optimize all 200 simultaneously means optimizing nothing effectively.”

What happens when your tool shows citation rate dropped 15% in one week

First check whether the tool itself changed. Platform API updates, query syntax modifications, sampling adjustments. Tools tracking real-time AI responses experience noise because AI platforms themselves update continuously.

Second, investigate whether specific query categories dropped or whether decline spread evenly. If only one topic cluster declined, that suggests competitor content improvements or your content aging out of relevance for that specific area. If everything dropped proportionally, platform algorithm changes become more likely.

Third, look at competitor movements. If everyone’s citations declined together, the platform probably changed how it surfaces sources. If competitors gained while you declined, content gaps or authority signals became the issue.

Most drops need 2-3 weeks of data to confirm actual trends versus measurement noise. Reacting to single-day fluctuations wastes effort on variance rather than meaningful signal.

Actually validating whether this investment matters

Some teams manually track 5-10 critical queries weekly before paying for automation. Just to establish whether AI citations correlate with any downstream business metrics they already measure.

If citation rate changes precede branded search volume increases by 4-6 weeks, that validates investing in proper tracking. If citations move but nothing downstream changes over 90 days, either your queries don’t represent actual customer research behavior or your business doesn’t benefit from zero-click awareness.

Not every business model needs AI visibility measurement. If you sell exclusively through paid channels, partnerships, or outbound sales, AI citations might build nice awareness but rarely drive pipeline. Tracking them becomes expensive validation of something that doesn’t matter to revenue.

But if prospects research independently before engaging sales, if you compete on thought leadership, if organic discovery drives meaningful business, then not measuring AI citations means flying blind on an increasingly important channel.

Free trials reveal more than feature sheets. Three weeks of actual usage across your query set shows whether the tool’s reporting matches your decision-making workflow. Some tools provide technically accurate data in formats that don’t translate to actionable insights for your specific organization.