GEO Competitive Intelligence: How to Compare Your AI Visibility With Competitors

GEO competitive intelligence measures mentions, citations and sentiment in ChatGPT to compare your visibility with your competitors’.

How do you track your ranking on ChatGPT? The direct answer

To track your ranking on ChatGPT, you need to measure how often your brand is cited in generated answers, because there is no fixed ranking and no equivalent of Google Search Console. Answers vary with phrasing, timing and context, so tracking has to rely on sampling.

The 5-step method

1. Build a panel of 25 to 50 prompts representative of your category (comparisons, recommendations, usage questions).

2. Run each prompt several times (at least 3 to 5), because ChatGPT doesn’t always give the same answer.

3. Record whether the brand appears, its position in the list, the sentiment and the sources cited.

4. Calculate the visibility rate: prompts with a mention divided by prompts tested.

5. Repeat weekly or monthly to detect changes.

For a detailed step-by-step protocol, see our guide on how to measure AI visibility in ChatGPT.

The key metrics

Key takeaway Manual tracking works for an initial test. Beyond a few dozen prompts, specialized tools such as VizibAI’s AI visibility platform automate data collection and competitor comparison.

Why competitive intelligence becomes central with GEO

In answer engines such as ChatGPT, Perplexity or Google AI Overviews, a query no longer returns ten links but one answer that usually cites 3 to 5 brands. This scarcity changes the nature of competition: a brand missing from the answer simply isn’t part of the purchase decision, and every spot a competitor takes is a spot you lose.

GEO competitive intelligence addresses three needs that classic SEO covers poorly:

1. Measuring AI share of voice: the share of answers in a category that mention your brand compared with its competitors.

2. Understanding the gap: identifying the sources, content and topics that lead a model to recommend one competitor over another.

3. Detecting movements: answers vary from one model and one week to the next, which reveals emerging competitors.

Key takeaway In GEO, visibility is relative: an isolated score means nothing. Only a comparison with the category leader, on the same prompts, reveals the real gap. Tools like VizibAI are built for this benchmark.

The metrics to compare two or more brands in AI

Comparing two or more brands in AI relies on five metrics measured on the same prompt panel, the same engine (ChatGPT, Perplexity, Gemini, Google AI Overviews) and the same period.

Measuring the gap with the leader

The gap is calculated in share-of-voice points: if the leader gets 38% and your brand 14%, the distance is 24 points. Tracking this gap monthly shows whether you’re catching up or falling behind.

Tip Run each prompt several times (at least 5 to 10 repetitions), because LLM answers vary from one run to the next. A single query is not a reliable measurement.

GEO tracking tools such as VizibAI, Profound or Semrush AI Toolkit automate this calculation across several brands. Crossing mention rate with sentiment avoids a common bias: a brand that’s frequently cited but associated with criticism isn’t worth the same as a less visible brand that gets recommended.

Measuring your share of voice against competitors

AI share of voice is calculated by dividing your brand’s mentions by the total mentions of all brands on the same prompt panel, expressed as a percentage. Example: if 5 brands total 200 mentions across 50 prompts and yours has 30, your share of voice is 15%.

1. Set an identical prompt panel for all brands.

2. Run each prompt several times (at least 3 to 5) to smooth out answer variability.

3. Count mentions per brand, then divide by the total.

4. Compare the point gap with the leader.

Platforms like VizibAI automate this calculation and repeat it at regular intervals to track changes over time.

Comparing reputation and sentiment between brands

Comparing the AI reputation of two brands means measuring the sentiment (positive, neutral, negative) of the answers that mention them, on the same prompt panel. Calculate the share of positive mentions for each brand and note recurring attributes (price, reliability, support). A difference in tone on a specific attribute reveals a lever for action. Platforms like VizibAI automate this tracking across several models.

Identifying dominant competitors, categories and topics

To identify the dominant brands in a category, query AI engines with 20 to 50 prompts covering the category (“best CRM software for SMBs”, “alternatives to X”), then count how often each brand appears across several runs. Brands cited in more than 50% of answers form the dominant core. Results vary between runs, so each prompt should be repeated at least 5 times per model (ChatGPT, Perplexity, Gemini).

Spotting emerging competitors

An emerging competitor rarely appears in generic answers but gains ground on niche or comparison prompts. A reliable signal: a brand absent a month ago that reaches a 10–15% mention rate on a specific segment. “Alternatives to” and “for startups” prompts reveal them earliest.

Detecting dominated topics and categories

Grouping prompts by theme (pricing, integrations, use cases, industry) lets you calculate each competitor’s share of voice per topic.

Illustrative example.

Key takeaway Opportunities lie where the overall leader is weak: a topic where no brand exceeds 30% share of voice is still open. A competitor’s weaknesses also show in negative sentiment or in answers that don’t cite its website.

Platforms like VizibAI automate this breakdown by category, topic and model, where a spreadsheet requires manual entry of every answer.

Analyzing competitors’ sources and GEO strategy

Analyzing a competitor’s GEO strategy starts with its sources: the web pages AI engines cite or consult to justify a recommendation. On ChatGPT, Perplexity or Google AI Overviews, a brand is rarely recommended at random. It’s recommended because third-party sites, product pages or editorial content feed the answer.

Identifying the content that influences AI

1. Run the same prompt panel across several models and record every cited URL.

2. Group them by domain and type: competitor website, comparison articles, specialized media, forums (Reddit), reviews (G2, Trustpilot), Wikipedia.

3. Spot the domains that appear most often behind the best-ranked brands.

Understanding the gap with a competitor

To find out why a competitor gets recommended more, compare its sources with yours: a domain that cites them but not you is a direct citation opportunity. The most reused content is usually factual, up to date and cited by several independent sites.

Detecting strategy changes

Rerun the panel every week or two and compare new sources, prompts won or lost, and changes in tone. A sudden appearance in several media outlets signals a campaign. Platforms like VizibAI automate this collection and keep a history of sources.

GEO competitive intelligence tools and building a benchmark

A GEO competitive intelligence tool runs a prompt panel across several AI engines (ChatGPT, Perplexity, Gemini, Google AI Overviews, Claude), then compares the mentions, citations and sources of several brands. Four approaches coexist, with very different levels of reliability and cost.

For a detailed comparison of the platforms on the market, read our review of the best GEO tools in 2026.

Building a GEO competitive benchmark

1. Define the scope: 3 to 7 direct competitors, plus 1 or 2 emerging players.

2. Build a panel of 30 to 100 prompts covering discovery, comparison and recommendation, with varied phrasing.

3. Run each prompt several times (at least 3 to 5), because AI answers vary from one run to the next.

4. Record, for each answer, the brands cited, their order, the sentiment and the source URLs.

5. Calculate metrics per brand and per model, then set a baseline.

6. Repeat on a fixed schedule: weekly for fast-moving markets, monthly at minimum.

Selection checklist Check model coverage, run frequency, raw data export, cited-source analysis and the ability to customize prompts. A tool that only shows aggregated scores limits causal analysis.

Manual tracking suits an initial test, spreadsheets suit a technical team, and a dedicated platform makes sense once the panel exceeds a few dozen prompts.

Conclusion

Tracking visibility in AI comes down to three lessons: measure, compare, act.

1. Measure regularly: ChatGPT, Perplexity and Google AI answers vary from one run to the next. A stable prompt panel run at fixed intervals (weekly, for example) gives a reliable trend, where a single query only gives a snapshot.

2. Compare yourself with competitors: a mention only makes sense relative to other brands’ share of voice, because each answer recommends only a few of them.

3. Act on sources: the pages, reviews and media AI cites explain its recommendations. Identifying them tells you which content to create or strengthen.

Platforms like VizibAI automate this cycle, but the rigor of the protocol matters more than the tool you choose. To see where you stand against your competitors, start with a free AI audit of your brand.

Frequently asked questions

How many times should you run a prompt to get a reliable measure of your visibility in ChatGPT?

Run each prompt at least 3 to 5 times, and ideally 5 to 10 times when comparing brands, because model answers vary from one run to the next. A single query is only a snapshot. The mention rate is then calculated across all runs of a panel of at least 25 to 50 prompts.

How do you calculate your AI share of voice against competitors?

AI share of voice is your brand’s mentions divided by the total mentions of all tracked brands on the same prompt panel, expressed as a percentage. For example, 30 mentions out of 200 total gives a 15% share of voice. The gap with the leader is then measured in points: 38% vs. 14% is a 24-point gap.

When should you switch from manual tracking to a GEO competitive intelligence tool like VizibAI?

Manual tracking suits an initial test, and a spreadsheet with API scripts suits a technical team. A dedicated platform like VizibAI becomes relevant once your panel exceeds a few dozen prompts, because it automates query repetition, brand comparison and the history of cited sources. Before choosing, check model coverage, run frequency and raw data export. For a first assessment, a free AI audit is enough.