---
title: Brand analysis
description: Use Brand analysis to understand the topics, competitors, and answer patterns behind your brand’s visibility in AI responses.
---

Brand analysis helps you move from **“How often does AI mention us?”** to **“In which customer topics are we strong, where does another brand lead, and what should we investigate next?”** It brings together topic gaps, the role and tone of brand mentions, and a review of potentially risky wording in AI answers.

Analysis follows the market selected in Project Settings. For a project set to **Global**, the analysis uses the market label **Global** and writes generated findings in English. For a country-specific project, generated findings use that country's official language.

Use it alongside [Visibility](/help/visibility/). Visibility shows measured presence and citations across prompts and models. Brand analysis gives you a deeper, periodically refreshed view of the themes in those answers. It is an analytical aid: use its signals to choose what to inspect or improve, not as proof that every customer sees the same answer.

## Run an analysis when the evidence is ready

Brand analysis is a separate run that saves a snapshot. It does not run by itself or update with each monitoring result; a new analysis starts only when someone with edit access selects **Run analysis**. PromptEye then uses the latest monitoring results for the project’s active prompts, from all monitored models. Archived prompts are not included, and the model and period selected on Visibility do not affect the run. The page updates when the run finishes, and the new run appears in Analysis history.

The estimate below the button is a guide based on the number of active prompts, not a completion guarantee. While a run is in progress, you cannot start another one. When the project has no active prompts, has no monitoring results yet, or the latest run already includes the current results, the button is unavailable. The last case shows **Up to date**: you can run a new analysis after the prompts' next checks have produced new results (once a day on most plans, every two days on some). If a run stays **In progress** for more than about 15 minutes, you can start a new one.

Each run uses part of the Brand analysis allowance your workspace gets for each billing period. The allowance is counted in prompts: a run uses as many units as the project has active prompts, and the plan includes a set number of runs per billing period for its prompt limit. A project with more active prompts therefore uses more of the allowance per run. The allowance is shared by all projects in the workspace, so runs in other projects reduce what is left. Failed and invalid-response runs do not count. If the limit prevents a run, wait for the next billing period or change the plan.

Runs can show **In progress**, **Ready**, **Error**, or **Invalid response**. Ready means the saved analysis can be reviewed. Error means the run failed; Invalid response means the returned result could not be accepted. For either failure, use **Retry** when it appears. A retry starts another run and uses the plan allowance if it succeeds. If a run remains in progress, give it time to finish; the estimate is only approximate.

The timeline shows the ten most recent runs. Select a point to review that run’s saved results. Runs cannot be deleted, and the page has no export. A saved run does not change later: if you change brand names or competitor groups, only the next run reflects it. If you have not run an analysis, the cards and topic table have no completed analysis to show. A new run is most useful after the project has accumulated fresh responses, or after you have made changes and want to compare a later sample with the previous one.

## Start with the summary, then choose a topic to investigate

The summary cards show detected topics, distinct leaders, and context gaps in the selected ready run. **Previous** is the value from the preceding ready run, when one exists. In the current view, **Detected topics** and **Context gaps** count the same topic rows; they are not two independent scores. The counts help you notice whether the analysis found a different set of themes or leaders, but do not by themselves say whether the business improved.

The Context gap table groups related monitored prompts into customer topics. For example, several questions about onboarding, integrations, and team permissions might appear under one software-evaluation topic. “Grouped from N monitored prompts” points to the linked prompt results used as evidence for that topic. PromptEye keeps only a small set of these links in each row, so N is not necessarily the total number of related prompts. A topic label is a useful summary, not a new prompt you need to monitor.

**Best brand** is the leading brand identified for that topic in the analyzed answers. The evidence beneath it shows how many model observations included that brand and the average position where position data was available. For example, “3/5 models, avg pos 2” means the brand appeared in three of five usable observations and averaged position two among the observations where its position could be determined. A missing position does not mean the brand was absent; it means there was no position value to show.

**Gap** is an analysis score from 0 to 100 that summarizes the distance between your brand and the topic leader. Zero represents no meaningful gap in the analysis; 100 represents complete absence of your brand while a competitor leads. It is not a literal percentage of missing content or a direct count of pages you need to create. A higher score is a signal to investigate the topic, not a forecast of lost revenue. The app groups scores as Critical (about 66 and above), Moderate (33–66), and Low (below 33). Scores near a boundary can change category with a small change in the analyzed sample.

The gap is not simply the percentage of answers that omit your brand. It compares your brand with the topic leader, taking into account how often and how high each brand appears and which sources the answers use. A brand can be mentioned but still have a substantial gap if the competitor appears more often or ranks higher. Conversely, a low gap does not prove that your brand owns every part of a topic.

Use **Why the leader wins** and **Why we are missing it** as hypotheses to check against the examples in Visibility. They are generated explanations based on the analyzed responses. They are not a verified audit of your website or a statement of the competitor’s actual strategy. When the evidence is thin, treat the explanation as a prompt for further review rather than a confirmed cause.

For example, if a competitor leads on “compliance reporting” and the explanation points to clearer coverage of audit workflows, inspect the source answers and your own product pages. You might decide to clarify the workflow in your content, improve the prompt set, or conclude that the topic is not commercially relevant. The analysis does not make that business decision for you.

## Read the trend as a sequence of snapshots

**Competitive gaps over time** groups the topics in each completed run into the same three gap bands. It helps answer whether the balance of detected gaps is moving in a useful direction across runs. **Analysis history** lets you select a prior run and inspect its saved table and cards.

The sentiment trend connects available scores across runs. If a run has no sentiment result, it is left out of the line rather than shown as zero; with fewer than two usable scores, the chart can show a dot without a line.

These are snapshots of separate analyses, not a continuous daily trend. The underlying answers can vary between runs even if you have not changed your site. Compare several runs and look for a repeated pattern before treating a one-run increase or decrease as a business change. If a topic appears in one run and not another, that can reflect changes in the available answers or how related prompts were grouped; it does not by itself prove that a competitor changed strategy.

## See the role and tone of brand mentions

The **Citation quality** and **Brand mention sentiment** cards are shown in the [Visibility overview](/help/visibility/), alongside Visibility results. They are connected to Brand analysis because their classifications are saved with an analysis run. Citation quality describes the role your brand plays in responses where it appears: a recommendation, an expert source, a comparison element, one of several listed sources, an incidental mention, or an anti-recommendation. This is different from Visibility: Visibility asks whether the brand appears; Citation quality asks how the answer uses the mention.

The distribution is calculated only from responses where your brand appeared, and the card’s footnote names the analysis run it belongs to. If it says the result was computed from only part of the mentions, some mentions could not be classified; read the percentages with that smaller classified sample in mind. The Visibility overview shows the latest ready analysis for these cards, so changing the page’s model or date filters does not turn them into a daily or date-filtered trend. If the readable answers contain no brand mentions, the card says so. If there is no completed result to display, it asks you to run an analysis.

These role labels are classifications, not endorsements or a measure of whether a recommendation is commercially justified. For example, “recommendation” means the response explicitly favors the brand; it does not establish that the recommendation is accurate or that the user acted on it. Open the underlying answer in Visibility before deciding what to change.

**Brand mention sentiment** is a separate measure of whether the language about your brand is positive, neutral, or negative. It applies only to responses that mention your brand. Review the examples and attributes behind the summary: a neutral factual description is not a negative review, and a positive classification is not evidence of customer satisfaction. Changes versus the previous run compare analysis snapshots, not customer sentiment over a continuous period.

## Use the compliance review as a prompt to inspect wording

The **Compliance audit** highlights language in AI responses that may overpromise, make unrealistic claims, or sound unprofessional when attributed to your brand. The score runs from 1 (severe violations) to 10 (fully professional), and the flagged phrases table shows the phrase, risk type, risk level, and answer context.

This is a review of language found in the analyzed AI responses. It is not a legal compliance audit, a review of every page on your website, or a conclusion that your company itself made the claim. A flagged phrase is a reason to inspect the original answer and its source. If the card reports no flagged phrases, that means none were identified in this run’s reviewed sample; it is not a guarantee that no problematic wording exists elsewhere.

## A practical workflow

1. Make sure the project has active prompts and their first monitoring results.
2. Run Brand analysis and wait for the status to become Ready.
3. Start with the largest gaps and check whether the topic matters to your customers.
4. Open the relevant answers in Visibility to see the actual wording, brands, and sources behind the signal.
5. Turn a supported finding into a concrete action, such as clarifying a product capability or creating a useful explanation for a missing customer question.
6. Run another analysis after new monitoring results are available. Compare repeated patterns across runs rather than relying on one score.

If a chart or card has no data, first check whether the selected run is Ready and whether that metric had a usable sample. For example, Citation quality and brand sentiment need responses that mention your brand; topic gaps need readable monitoring results. An empty card can therefore mean “no qualifying evidence in this run,” not necessarily “the feature failed.” Use the status and the card’s own note to distinguish these cases.