How can I see how my brand shows up in ChatGPT and Gemini?

After 12 years in enterprise search, I’ve seen the industry pivot from the wild west of keyword stuffing to the technical rigour of modern SEO. But today, the conversation in the boardroom has shifted again. It’s no longer just, “Why aren’t we ranking for X?” It’s “Why isn’t ChatGPT mentioning us?”

As an analyst, my first response is always the same: "Where does the data come from?" When you’re dealing with LLMs and AI answer engines, the "SERP" isn't a static page. It is a fluid, generative conversation. Tracking your brand visibility in this environment requires a departure from traditional rank tracking. If you’re still using the same legacy toolset you used in 2018, you aren’t just behind—you’re blind.

The Shift: AI Search Visibility vs. Traditional SEO

Traditional SEO tracks "positions" on a list of blue links. AI search visibility, however, is about contextual authority. Whether you are looking at Google AI Overviews or a direct answer from ChatGPT, you aren’t competing for a spot in a list; you are competing for the model’s "trust" or "knowledge graph relevance."

This is where most "AI visibility scores" fall apart. Many vendors present a single, proprietary number—a percentage score—without disclosing how it was derived. As someone who has spent years debugging BI dashboards, I find these scores infuriating. Without knowing if the score is based on sentiment analysis, frequency of mention, or source attribution, it’s just noise for a monthly report.

The "Regional Data" Trap: Beware of Prompt Injection

A common trend I’ve noticed in the market is companies promising "regional AI tracking." When you dig into the methodology, you find it's often based on prompt injection—manually forcing an LLM to "act" as if it is in a specific region. This is fundamentally flawed. If you have to tell a model "You are a user in London looking for X," you are measuring the model's performance on a prompt, not the model's actual organic behaviour in that region.

When choosing a tool for chatgpt brand visibility or gemini brand mentions, verify if they are using server-side local proxying or if they are just gaming the prompt. If they can’t explain the infrastructure behind their "regional" data, keep your wallet shut.

The Tool Landscape: Who is Actually Tracking AI Search?

The market is flooded with new entrants, but only a few are actually building infrastructure that matters to a data-driven marketer. Here is my current take on the landscape:

Peec AI

Peec AI has been interesting to https://bmmagazine.co.uk/business/top-3-ai-search-visibility-solutions-for-enterprise-teams-2026-rankings/ watch because they focus on the "answer" component of the query. They don’t just track if you’re mentioned; they track the context. For a brand, knowing that you’re mentioned in a negative or neutral context is just as important as being mentioned at all. They have shown some promise in bridging the gap between brand positioning and AI output.

Ahrefs

Ahrefs is the safe, enterprise-standard choice. They’ve begun integrating ai answer engine tracking into their ecosystem. The benefit here is the integration with existing data streams. However, I keep a running list of tools that hide their best features behind add-ons, and Ahrefs requires careful monitoring of your subscription tiers to ensure you aren't paying for "seats" that don't actually give you access to the data you need for your BI stack.

Otterly.AI

Otterly.AI has carved out a niche by focusing specifically on monitoring AI-generated answers. It is lean, focused, and—crucially—doesn't suffer from the "everything-to-everyone" bloat that slows down the bigger platforms. If your goal is granular tracking of how you appear in specific LLM responses, they are worth a look.

Data Integrity Table: What to look for

When evaluating these platforms, use this matrix to hold them accountable. If they can't answer "Yes" to these, they likely won't fit into a clean Looker Studio or PowerBI dashboard.

Feature The "Must-Have" Standard Why it matters Raw Data Access CSV/API/BigQuery Export If I can't pipe it to Looker Studio, it doesn't exist. Source Methodology Publicly documented "Proprietary algorithm" is code for "unverifiable." Regional Accuracy Real-IP geographic proxying Prompt injection is not valid local data. Pricing Transparency Per-project or fixed usage Per-seat pricing explodes when you involve stakeholders.

The BI Problem: If it doesn't export, it's just a dashboard

My biggest gripe with the current crop of AI tracking tools? The "walled garden" dashboard. Many vendors want you to live in their proprietary UI, giving you fancy charts that you cannot export cleanly to Looker Studio. As an analyst, I don't want another tab open. I want a raw data feed that I can join with my Google Search Console data and my CRM revenue figures.

When you are pitching these tools to your team, avoid the "visibility score" vanity metrics. Instead, push for raw mention data, sentiment analysis, and attribution links. If a tool provides a clean API endpoint that I can dump into my own BI layer, that’s where the real value lies.

Actionable Steps for Your AI Visibility Strategy

• Start with a Baseline Audit: Before buying a tool, do a manual audit of 50 high-intent keywords across ChatGPT, Google AI Overviews, and Gemini. Use different devices and clear cache. See what is actually coming back.
• Audit the Vendors: Ask them directly: "Is your regional data based on real-IP proxying or prompt injection?" If they pause, you have your answer.
• Prioritise Integration: Check the vendor’s documentation. Can you export their data via API? If they only offer a "Download to PDF" button, you are being held hostage to their UI.
• Ignore the "Visibility Score": It is a vanity metric designed to make executives feel good. Focus on Share of Voice in AI Answers—which is a much more actionable metric for your content strategy.

The transition from traditional SEO to AI search visibility is going to be messy. We are moving from a world of "how to trick the crawler" to "how to inform the intelligence." It requires more data rigour, not more hand-wavy marketing magic. When in doubt, ask for the data source, demand an exportable format, and never, ever trust a score you can’t calculate yourself in Excel.