Yes, there is a way to track how often your brand gets mentioned by AI tools. This is how it works, what to measure, and how the leading platforms compare.
The direct answer
Yes, AI brand mention tracking is possible and measurable. It works by running structured query sets across ChatGPT and Claude and recording which companies appear in responses, how they are described, and in which buyer journey stages. This is called a GEO audit. Dedicated platforms such as Persipica, Profound, Peec AI, and Goodie AI all offer versions of this capability, with meaningful differences in scope, methodology and what they do with the data.
How it works
AI platforms do not publish data about which brands they recommend or how often. There is no ChatGPT equivalent of Google Search Console. Tracking requires active measurement: designing a set of queries that mirror real buyer searches, running them systematically, and recording the output.
The core unit of measurement is the citation rate: the percentage of queries for which your brand appears in the AI's response. But citation rate alone is insufficient. A company can be mentioned as a cautionary example, misidentified or cited with inaccurate category framing. This is why quality scoring matters as much as citation volume.
Why brand-only tracking misleads
Most companies that attempt informal AI tracking ask: "does ChatGPT know who we are?" This test almost always returns a positive result. AI models know most established companies when asked directly by name. The dangerous blind spot is discovery. When a buyer describes their problem without mentioning your company, do you appear? This is the query pattern that determines shortlists and it is precisely where most companies are invisible.
Step by step
You can conduct a basic AI brand mention audit manually in an afternoon. A thorough, statistically reliable audit across all platforms and buyer journey stages is more involved, typically requiring 100 to 150 structured queries and systematic scoring. Here is the methodology for both.
The limitation of manual tracking
Manual audits give you a snapshot. AI responses have natural variability, and the same query run twice does not always produce the same result. Statistically reliable citation rates require running each query multiple times across multiple sessions. For 30+ queries across 3 platforms with 3 repetitions each, that is 270+ individual tests. Dedicated platforms add significant value over manual tracking at this scale.
Platform differences
Tracking across a single AI platform gives an incomplete picture. Citation patterns differ meaningfully between models because each draws on different training data, retrieval systems, and update cycles.
OpenAI systems are among the most-used AI platforms for research queries. Current frontier models combine extensive learned associations with retrieval capabilities depending on configuration. Companies with strong G2 presence, clear entity signals, and press coverage tend to score well.
Anthropic systems often require dense third-party corroboration before including companies in recommendations. Our audits consistently find that a company can be recognised in direct brand queries but remain absent from category recommendations when there is not enough external evidence anchoring it to the relevant market.
Primarily retrieval-based: it searches the web in real time and synthesises results. This means current content matters more than training data, and new pages can influence citation within days rather than months. However, Perplexity prioritises highly-ranked, authoritative sources, so SEO authority and recent press coverage are particularly important levers for improving citation here.
Tool comparison
The AI visibility tracking category is new. These four platforms approach the problem differently, with meaningful differences in what they measure, which platforms they cover, and what they do with the data.
| Feature | Persipica | Profound | Peec AI | Goodie AI |
|---|---|---|---|---|
| Primary focus | Enterprise AI visibility audit and GEO strategy | AI search monitoring and analytics | AI visibility tracking and alerts | GEO platform and optimisation tooling |
| Platforms tracked | ChatGPT, Claude | ChatGPT, Perplexity, Gemini | ChatGPT, Perplexity | ChatGPT, Perplexity |
| Claude tracking | ✓ Yes | ✗ No | ✗ No | ✗ No |
| Buyer journey stage analysis | ✓ All 6 stages | ~ Partial | ✗ Limited | ~ Partial |
| Semantic quality scoring | ✓ Yes, 0 to 4 scale | ✗ Citation volume only | ✗ No | ~ Sentiment only |
| Competitor benchmarking | ✓ Named competitor tracking | ✓ Yes | ~ Limited | ✓ Yes |
| Discovery query testing | ✓ Core focus | ~ Some coverage | ✗ Brand queries only | ~ Some coverage |
| GEO strategy and implementation | ✓ Included: prioritised action plan | ✗ Monitoring only | ✗ Monitoring only | ~ Platform tools, limited strategy |
| Agentic purchasing readiness | ✓ Forward-looking assessment | ✗ Not covered | ✗ Not covered | ✗ Not covered |
| Primary output | Audit report + prioritised GEO roadmap | Dashboard and alerts | Mention alerts and tracking | Optimisation recommendations |
| Best for | Enterprise teams who want to understand and fix their AI visibility across the full buyer journey | Teams who want ongoing monitoring of AI mention volume | Teams who want basic AI mention tracking and alerts | Teams who want a self-serve GEO optimisation platform |
Comparison based on publicly available platform information as of April 2026. Feature availability may vary by plan. Claude tracking reflects presence in structured audit methodology. Not all platforms have integrated Anthropic's Claude into monitoring tooling.
Find out your citation rate across ChatGPT and Claude
Persipica runs structured GEO audits that test your brand across all six buyer journey stages and three major AI platforms, then deliver a prioritised action plan showing exactly where you are invisible and how to fix it.