Can I just check ChatGPT and call it done?

No. Engines disagree constantly. A prompt that recommends you in one assistant may omit you in another because each retrieves different sources and updates on its own schedule. Track every engine your buyers actually use to avoid a false sense of visibility.

What is share of voice in the context of AI answers?

It is your brand's presence across a fixed basket of prompts relative to competitors. Instead of asking 'did we appear in this one answer,' it aggregates many prompts and engines into a single comparable figure you can track and improve over time.

How often should I track AI visibility?

Weekly works well for most brands. It is frequent enough to catch model updates and competitor moves but not so frequent that normal output variance becomes noise. The key is running the same prompts on a consistent schedule to build a trustworthy trend.

Do I need a tool, or can I do this manually?

You can start manually with a small prompt set, but it scales poorly. Running dozens of prompts across many engines every week, parsing mentions and citations, and comparing to competitors is exactly the repetitive work a dedicated tracker like GetNamed automates.

How to Track Your Brand in AI Answers

Why AI Visibility Needs Its Own Measurement

When buyers ask an AI engine 'what are the best tools for X' or 'who should I use for Y,' the answer they get shapes their shortlist before they ever reach a search results page or your site. If your brand is missing from that answer, you are invisible at the exact moment of consideration, and your analytics will not tell you why.

Traditional metrics like rankings and traffic do not capture this. They measure the classic search funnel, not what a generative model says about you in conversation. AI visibility is a distinct surface, and it needs its own deliberate measurement rather than being inferred from web analytics.

Step 1: Define the Prompts That Matter

Start from the real questions your buyers ask, not the keywords you wish you ranked for. These cluster into a few types: category questions ('best CRM for startups'), problem questions ('how do I reduce churn'), comparison questions ('Tool A vs Tool B'), and direct brand questions ('is Brand X any good'). Each type tests a different part of your visibility.

Build a representative basket of prompts across those types and keep it stable so you can compare results over time. A moving target makes trends meaningless. Aim for coverage of your core use cases rather than an exhaustive list, then expand as you learn which prompts drive the most consideration.

Category prompts that surface a shortlist of vendors
Problem prompts where you want to be the recommended solution
Comparison prompts against named competitors
Branded prompts that test how the model describes you

Step 2: Test Across Every Engine That Matters

Buyers do not all use the same assistant, and the engines do not agree with each other. A prompt that names you in one model may omit you entirely in another, because each is trained differently, retrieves different sources, and updates on its own schedule. Checking a single engine gives you a dangerously partial picture.

Cover the major surfaces your audience actually uses, which today span assistants like ChatGPT, Perplexity, Gemini, Claude, Grok, DeepSeek, and Meta AI, plus Google AI Overviews on the search side. Run the same prompt basket against each so you can see exactly where you are strong and where you are absent.

Step 3: Measure the Right Signals

Raw 'did we appear' is too blunt. The signals that matter are whether you were mentioned at all, whether you were cited as a source with a link, your position relative to competitors, and your overall share of voice across the prompt basket. Share of voice, in particular, turns scattered observations into a single number you can move.

Pay attention to how you are described, not just whether you appear. A mention that frames you as the budget option, or that repeats an outdated claim, is a visibility problem even though you technically showed up. Tracking the substance of the answer is as important as tracking presence in it.

Mention rate: how often you appear across the prompt basket
Citation rate: how often you are linked as a source
Share of voice: your presence versus competitors overall
Sentiment and framing: how the model characterizes you

Step 4: Choose the Right Cadence

AI answers are not static. Models update, retrieval indexes refresh, and competitors publish new content, so a single audit ages quickly. Spot-checking once a quarter will miss the swings that matter. A weekly cadence is a practical sweet spot for most brands, frequent enough to catch movement without drowning you in noise.

Consistency beats frequency, though. Running the same prompts on the same schedule is what lets you separate a real trend from the natural variance in model outputs. The point of a regular cadence is to build a time series you can trust, not to refresh dashboards for their own sake.

Step 5: Turn Findings Into Action

Measurement only pays off if it feeds work. Each cycle should surface gaps: prompts where competitors are named and you are not, topics where no source covers the question well, and answers that misrepresent you. Those gaps become a content and positioning backlog aimed squarely at the prompts that matter to revenue.

Doing this by hand across many prompts and many engines every week is impractical, which is why dedicated trackers exist. A tool like GetNamed runs your prompt basket across the major AI engines on a recurring schedule, reports share of voice and competitor comparisons, highlights the gaps, and suggests on-brand content to close them, with any outreach kept opt-in and approved by you rather than automated or manipulative. That keeps the loop honest while making it sustainable.