Topic-level visibility measures how prominently and favorably a brand appears in AI-generated answers across a cluster of related prompts that all sit under one subject. Prompt-level visibility scores a single question, while topic-level visibility rolls many questions into one subject and reports your standing for the whole area.
You care because buyers rarely ask one question before choosing a vendor, so a win on a single prompt says little about whether AI recommends you across the decision. Track it and you see which subjects competitors own and where you are absent, then fix the gaps that move pipeline.
Topic-level visibility aggregates your prompt-level results into subject clusters and reports one score for how often and how prominently AI answer engines name your brand within each cluster. A topic groups the prompts a buyer asks about one area, spanning early problem questions through head-to-head comparisons.
The metric depends on a defined prompt set that represents the topic and a repeated run of those prompts across engines like ChatGPT, Perplexity, and Google AI Overviews. A scoring method then records how often your brand is mentioned, your share of voice against competitors, and your average position in the answer, rolled up for the whole cluster.
Topic-level visibility sits between two other views of the same data. Prompt-level visibility shows one question in detail, and platform-level visibility shows one engine across everything. The topic view maps to how buyers explore a category, which is why tools like AirOps report mention rate by topic alongside the individual prompts behind each score.
Resources: See which AI visibility metrics to track by topic and how to act on them
Topic-level visibility runs the same measurement loop for every prompt in a cluster, then aggregates the results into one score. The loop is repeatable, so you can compare one week to the next and one topic against another.
Define the topic. Group the prompts that represent one subject area, covering the buyer's journey from awareness through comparison.
Build the prompt set. Write the unbranded questions a buyer would type, then choose the engines you want to track.
Run the prompts. Send each prompt to engines like ChatGPT, Perplexity, and Gemini on a fixed cadence so results stay comparable.
Score each answer. Record whether your brand is mentioned, where it lands, and which competitors show up alongside it.
Roll up the cluster. Aggregate the per-prompt results into one topic score for mention rate, share of voice, and average position.
The topic score tells you where you stand for a subject and which competitors hold the answer. It does not tell you why an engine chose them, so read it next to the sources cited in each answer.
Resources: Follow a step-by-step guide to measuring AI search visibility
Buyers now shortlist vendors inside a single AI answer, and they ask a series of questions before they settle on one. Topic-level visibility tells you whether you show up across that whole series or only for a few scattered prompts, which decides whether AI puts you on the shortlist. Most subjects are still up for grabs: Semrush and Kevin Indig found that only 15.2% of the 1,094 US ChatGPT categories they analyzed monthly from January through June 2026 had a clear brand owner. That gap is the opening, and the brand that shows up consistently across a topic takes it.
Budget follows proof. A topic score ties AI search work to a category a CMO already funds, so you can defend spend with pipeline math your finance team accepts.
Prompt wins can hide gaps. Single-question optimization looks like progress while a whole subject stays lost, letting you miss the comparison prompts that close deals.
Competitors sit on the same scale. Share of voice by topic shows which subjects a rival owns, so you can target the clusters worth a content push.
SEO managers use topic-level visibility to find the subject clusters where their brand is absent from AI answers and prioritize which pages to build first.
Content strategists use topic-level visibility to map their editorial calendar to the topics where a rival owns share of voice.
Demand gen leads use topic-level visibility to report AI search progress to the CMO by category instead of by isolated prompt.
The topic score is only as honest as the prompts behind it, so the set has to cover the real questions a buyer asks at each funnel stage while staying unbranded, which is what lets you measure genuine category presence instead of recall of your own name.
A topic score is an average of many prompts, so a single strong prompt can hide several weak ones, which is why you read the full spread inside a cluster before trusting the headline number for the whole topic.
AI answers shift from week to week, so a topic score carries meaning only when you run the same prompt set on a fixed schedule and keep the engine list steady, so each period compares like with like.
Spot the subject clusters where competitors own the AI answer and you never appear.
Prioritize content work by topic value instead of chasing one prompt at a time.
Track your share of voice by topic across ChatGPT, Perplexity, and Google AI Overviews.
Report AI search progress to leadership as category movement tied to pipeline.
Catch a topic losing ground before the drop reaches your traffic.
Define each topic around a real buying decision, so the score maps to revenue instead of vanity questions.
Keep prompts unbranded, because branded prompts measure recall of your own name and hide true category presence.
Run the same prompt set on a fixed cadence, since comparability over time is what makes a trend real.
Track more than one engine, because your standing on ChatGPT can differ sharply from Perplexity or Gemini.
Pair the topic score with the cited sources, so you learn which pages and domains the engine trusts for that subject.
Read the spread of prompts inside a cluster, because an average can hide the weak questions that need work.
Avoid treating a single topic score as a finished verdict. Competent teams pull a clean number, celebrate it, then stop reading the prompts underneath, which is where a rising average quietly hides three comparison questions you are losing.
AirOps: tracks mention rate, share of voice, citation rate, and average position by topic across ChatGPT, Gemini, Perplexity, Google AI Mode, and Google AI Overviews, then connects the gaps to content work.
Google Search Console: shows which queries and pages already earn impressions and clicks for a topic, giving you the organic baseline behind a cluster before you track it in AI answers.
Semrush: groups keywords into topic clusters and tracks position and share of voice across a subject, so you can map the questions that make up a topic and watch them over time.
Pick one topic. Choose a single buying decision your brand should win, and write down the outcome a customer wants from it. You can do this in an afternoon with no tools and no budget approval.
Draft the prompt set. List the unbranded questions a buyer asks about that topic across the funnel, aiming for enough to represent the subject without ballooning the set into noise.
Run and record. Send each prompt to two or three engines and log whether your brand appears, where it sits, and who else is named in the answer.
Set your baseline. Roll the results into one score for mention rate and share of voice, so you have a starting point to beat.
Schedule the rerun. Put the same prompt set on a fixed cadence and assign an owner, so the topic score becomes a trend you can act on.
Topic-level visibility scores how well AI answer engines feature your brand across a cluster of related prompts instead of a single question.
You measure it by running a fixed, unbranded prompt set across engines and rolling the results into mention rate, share of voice, and average position.
The score is only as trustworthy as the prompt set and the cadence behind it.
A clean topic average can hide the specific comparison prompts where you are losing deals.
Your gains compound when you choose which clusters to win, then point content and earned media at the subjects that move pipeline.
Topic-level visibility aggregates many prompts into one subject score, while prompt-level visibility reports a single question on its own. Both read the same underlying data at different zoom levels. A prompt-level view is where you diagnose a specific question, like a head-to-head comparison you keep losing, and it shows the exact answer text and sources for that one query. The topic view sits above it and tells you whether the subject as a whole is working, which is the level a marketing lead reports on and funds. You need both. Track only prompts and you drown in detail and miss the pattern. Track only topics and you see a subject slipping but cannot tell which questions caused it. The practical workflow moves between them: read the topic score to decide where to act, then open the prompts inside that cluster to see what to fix and which competitors are taking your place.
Run your topic-level visibility measurement on a fixed cadence, and weekly is the sweet spot for most B2B teams. AI answers change often enough that a single reading tells you almost nothing, and a trend only appears once you have several runs of the same prompt set under identical conditions. Weekly runs catch movement early without drowning you in noise, and they line up with most content and reporting cycles. Some teams run daily for high-stakes categories where a competitor launch can shift answers within days, and monthly can work for slow, stable topics. Whatever cadence you pick, hold it steady and keep the prompt set and engine list constant, because changing the inputs breaks comparability and turns a real trend into guesswork. Set a standing calendar reminder or assign an owner so the rerun happens. The point of a cadence is a clean comparison from one period to the next, so consistency matters more than raw frequency.
Your topic-level visibility varies across engines because each one retrieves and cites sources differently. ChatGPT, Perplexity, and Gemini run on different underlying models with different citation rules. The same prompt can surface a different set of brands on each. Answers also shift from run to run on the same engine: a 2026 SparkToro study found under a 1-in-100 chance that ChatGPT or Google's AI Search repeats its brand list across two runs of the same prompt, and AirOps research found only 30% of brands stay visible from one AI answer to the next. Perplexity leans on live web results and shows its sources, which favors brands with strong, current third-party coverage. A model answering from training data with no retrieval may favor brands it saw often during training. This variance is why a single-engine, single-run score misleads. Measure across the engines your buyers use on a fixed cadence, then read each one on its own before you average them.
Yes, you can influence your topic-level visibility, though you shape the inputs while the engine still writes the final answer. The levers are the same ones that drive AI citations generally, applied at the topic level. Publish clear, well-structured content that answers the specific questions in your prompt cluster, and make each page easy for an engine to parse and quote. Earn third-party coverage on the same subject, because engines lean on external sources when they decide who to name for a category. Keep your entity information consistent across the web so a model reliably associates your brand with that topic. What you cannot do is force a mention or buy your way to the top of an organic answer, and results arrive on the engine's schedule, so expect changes over weeks. Treat the topic score as a feedback loop: change the evidence, rerun the prompts, and watch which subjects respond. The subjects that move are where your effort compounds.
Good topic-level visibility depends on your category, so it is relative to the competitors on your prompts. There is no universal number that means success, because a crowded category caps everyone's share of voice lower than a niche with two vendors. Start by benchmarking against the competitors who appear on your topic, then aim to move your share of voice and mention rate up relative to them over successive runs. A useful early goal is presence: being mentioned at all on the majority of prompts in a cluster, before you chase position. From there, aim to be named among the first brands, since earlier mentions carry more weight with buyers. A lead is worth chasing because it tends to hold: Semrush and Kevin Indig found that clear category owners held first place month over month in 90.4% of comparisons across those 1,094 US ChatGPT categories. Track the direction of travel more than any single figure. A topic score climbing quarter over quarter against a named competitor set beats hitting an arbitrary percentage someone quoted online.