ChatGPT answers, unedited

Yes. I’d treat this as AI answer share-of-voice (AI SOV) monitoring: repeatedly ask the same realistic buyer questions across ChatGPT and other assistants, capture the answers, identify every brand mentioned, and compare your percentage against competitors.

Current tools already support this model, but they differ substantially in breadth and methodology. Slate Siftly

My recommendation

Start with a dedicated AI-visibility platform rather than building the collection infrastructure yourself. For a serious competitor-monitoring program, I’d shortlist:

• siftly.ai — particularly good fit if the core requirement is competitor percentage share. It explicitly calculates AI SOV, supports platform/topic breakdowns, head-to-head competitor comparisons, and time trends. Siftly
• chatbenchmark.com — attractive if you want a broader dashboard across ChatGPT, Perplexity, Google AI Overview/AI Mode and other engines, with competitor share and filtering. ChatBenchmark
• zumihq.com — worth evaluating for an enterprise-oriented view combining mention rate, SOV, average position and citation share. Zumi
• answermonk.ai — interesting if you want a relatively focused measurement system where competitors can be discovered from the actual answers rather than only from a predefined list. AnswerMonk

The metric I'd put on the executive dashboard

Don't use just "number of mentions." I'd track:

The distinction between mention rate and SOV is important. A brand can appear in 50% of answers while still losing badly to a competitor that appears more frequently when both are mentioned. Zumi AnswerMonk

How I'd calculate it

Build a fixed panel of, say, 100–500 buyer prompts:

"What are the best [category] tools for a mid-sized company?"
"Compare [your company] vs [competitor]."
"What should I consider when choosing a [category]?"
"Which [category] vendors are best for [use case]?"

Run those prompts repeatedly across ChatGPT, Gemini, Claude, Perplexity, and relevant Google AI experiences. This matters because the engines don't necessarily recommend the same companies. G2 Learn Hub Verity Score

Then calculate:

AI SOV = your brand mentions ÷ total tracked-brand mentions × 100

For example:

I'd actually report two versions:

1. Answer SOV: percentage of answers containing each brand.
2. Mention SOV: percentage of all brand mentions belonging to each brand.

That prevents a competitor that gets mentioned repeatedly in a few unusually long answers from distorting your view.

One important methodological point

Don't take one ChatGPT answer as representative. AI answers are probabilistic and can change between runs; current AI-visibility research specifically recommends multiple runs per prompt and reporting ranges rather than treating a single response as definitive. Verity Score

So I'd run each prompt 3–5 times per engine, then report weekly/monthly aggregates.

If I were implementing this for a company

I'd make the primary KPI:

"What percentage of relevant AI recommendations does our brand own, versus each competitor?"

Then have a dashboard that lets you drill from:

Overall SOV → AI engine → buyer journey → topic → prompt → actual answer → cited sources

That last step is crucial. Knowing that a competitor has 32% vs your 18% is useful; knowing that the competitor wins because ChatGPT repeatedly cites three review sites and two comparison articles that never mention you is actionable. Current AI-visibility products increasingly expose this citation/source layer for exactly that reason. AI Visibility Software Zumi

If you tell me your industry/company and 3–5 competitors, I can also propose the exact prompt taxonomy, SOV formula, dashboard structure, and a recommended tool/vendor shortlist for your use case.

That prevents a competitor that gets mentioned repeatedly in a few unusually long answers from distorting your view.

One important methodological point

Don't take one ChatGPT answer as representative. AI answers are probabilistic and can change between runs; current AI-visibility research specifically recommends multiple runs per prompt and reporting ranges rather than treating a single response as definitive. Verity Score

So I'd run each prompt 3–5 times per engine, then report weekly/monthly aggregates.

If I were implementing this for a company

I'd make the primary KPI:

"What percentage of relevant AI recommendations does our brand own, versus each competitor?"

Then have a dashboard that lets you drill from:

Overall SOV → AI engine → buyer journey → topic → prompt → actual answer → cited sources

That last step is crucial. Knowing that a competitor has 32% vs your 18% is useful; knowing that the competitor wins because ChatGPT repeatedly cites three review sites and two comparison articles that never mention you is actionable. Current AI-visibility products increasingly expose this citation/source layer for exactly that reason. AI Visibility Software Zumi

If you tell me your industry/company and 3–5 competitors, I can also propose the exact prompt taxonomy, SOV formula, dashboard structure, and a recommended tool/vendor shortlist for your use case.

If your goal is specifically to monitor how often competitors appear in ChatGPT answers and compare percentage share, I’d recommend an AI-search visibility platform rather than traditional SEO rank tracking.

My recommendation: Profound

tryprofound.com is the strongest fit for this use case.

It can:

• Track a defined set of prompts daily and measure whether your brand and competitors appear in the resulting AI answers. Profound Profound Knowledge Base
• Calculate Share of Voice (SoV) — essentially, your brand's mentions divided by total tracked-brand mentions. Profound Knowledge Base
• Compare your brand against competitors prompt-by-prompt, topic-by-topic, and platform-by-platform. Profound
• Track average position, so you can distinguish "mentioned" from "mentioned first." Profound
• Show which domains/pages are being cited when competitors win an answer, which makes the data actionable rather than just a scoreboard. Profound
• Monitor ChatGPT alongside Gemini, Perplexity, Claude, Google AI Overviews and other answer engines. Profound

The dashboard I'd build

For each competitor, I'd track:

The key distinction is visibility vs. share of voice. If your company appears in 40% of answers but competitors generate substantially more mentions within those answers, you can have decent visibility but poor competitive share. Profound explicitly separates these measures. Profound Knowledge Base

A good alternative: Peec AI

peec.ai is probably the one I'd evaluate alongside Profound. It has particularly clear visibility, position, sentiment and Share of Voice metrics, plus competitor comparisons at the prompt and engine level. Peec

It also has useful competitor surge alerts, including workflows that can notify Slack when a competitor's SoV jumps materially. Peec

How I'd choose

• Profound → best if you want a comprehensive enterprise competitive-intelligence/AEO platform.
• Peec AI → attractive if your primary requirement is a straightforward AI visibility + competitor SoV dashboard.
• Build your own → only worthwhile if you need proprietary prompts, internal data integration, or very specific scoring methodology.

One important caveat: don't treat a single percentage as "the market share of ChatGPT." These systems should be measured against a fixed, representative prompt set, because AI answers vary from query to query and over time. Profound, for example, runs tracked prompts daily specifically because answers aren't deterministic. Profound

If you tell me your industry + 5–10 competitors + whether you care only about ChatGPT or also Gemini/Perplexity, I can propose the exact prompt taxonomy and Share-of-Voice methodology I'd use.

If you tell me your industry + 5–10 competitors + whether you care only about ChatGPT or also Gemini/Perplexity, I can propose the exact prompt taxonomy and Share-of-Voice methodology I'd use.

If your goal is specifically “How often does my brand appear in AI assistant answers versus competitors, and is my share increasing?”, I’d recommend Profound as the strongest overall solution, with Peec AI as the simpler alternative.

My recommendation: Profound

tryprofound.com is purpose-built for AI-search competitive intelligence. It monitors prompts across ChatGPT, Gemini, Perplexity, Google AI Overviews and other answer engines, then compares your brand with competitors on visibility, share of voice, position, sentiment, and citation share. Profound Profound

The particularly useful metric for your use case is Share of Voice (SoV):

SoV = your brand mentions ÷ total competitor/brand mentions × 100

So, for example, if a set of 1,000 relevant AI answers contains:

You can then track that same metric weekly/monthly and see whether you're gaining or losing AI shelf space.

Profound also lets you drill down to the exact prompts where competitors beat you, rather than just giving you an aggregate percentage. That makes it much more actionable: “Competitor A owns 42% SoV for these 18 commercial prompts, while we have 17%.” Profound Profound Knowledge Base

Strong alternative: Peec AI

peec.ai is probably the best choice if you want a more straightforward AI visibility + competitor dashboard.

It explicitly tracks:

• Visibility — percentage of AI responses mentioning you
• Position — where you appear in the answer
• Sentiment
• Share of Voice versus competitors
• Prompt-level performance
• Which third-party sources AI systems cite
• Competitor gaps in those sources Peec AI Peec AI

Peec's definition is especially useful because it distinguishes visibility from share of voice. A brand could appear in 40% of answers but have only 25% SoV if competitors receive more total mentions. Peec AI

What I'd implement

Don't just monitor one generic number. I'd build an AI Competitive Share-of-Voice dashboard around four metrics:

1. Answer visibility — % of tracked prompts where each brand appears.
2. Share of voice — % of all brand mentions captured by each competitor.
3. Mention position — how prominently the brand is presented.
4. Citation share — % of supporting citations going to your domain versus competitors.

Then segment everything by:

• AI engine: ChatGPT, Gemini, Perplexity, Google AI
• Intent: informational, commercial, comparison, “best X,” alternatives, etc.
• Topic/category
• Geography
• Time period

This matters because an overall 20% SoV can conceal something strategically important—for example, 35% in high-intent buying prompts but only 8% in comparison prompts.

One important caveat

I'd treat SoV as a trend/benchmarking metric rather than an absolute market-share measurement. AI answers are probabilistic and vary with prompt wording, model, personalization, geography and time. Recent reporting has shown substantial changes in which sources ChatGPT cites over relatively short periods. Axios

So the best methodology is to maintain a fixed, representative prompt set, run it consistently, and compare competitors under the same prompts and engines. That gives you a defensible “AI share of voice” KPI.

Bottom line:
Profound if this is an enterprise competitive-intelligence program and you want deep prompt/citation analysis; Peec AI if you primarily want a clean, marketer-friendly visibility and SoV dashboard.

A good solution is to build an AI Answer Share of Voice (AI SOV) monitoring system: a recurring measurement program that runs the same buyer prompts across chat assistants, captures responses, identifies competitor mentions, and calculates each brand’s share of presence.

Several tools now specialize in this category, including platforms that track brand mentions, citations, position, and competitor share across AI engines. www.answermonk.aiscrunch.comwww.zumihq.com

Recommended approach

1. Create a competitor prompt universe

Build a library of prompts that represent how customers discover products:

Category discovery

• “What are the best [category] platforms?”
• “Which companies offer [solution]?”
• “Compare [brand A] vs [brand B]”

Purchase intent

• “What is the best [product] for enterprise teams?”
• “Which vendors should I shortlist for [use case]?”

Problem-solving

• “How do I solve [customer problem]?”
• “What tools help with [workflow]?”

Segment prompts by:

• Customer segment
• Industry
• Use case
• Geography
• Funnel stage

A mature program usually tracks hundreds to thousands of prompts.

2. Run prompts regularly across assistants

Monitor:

• ChatGPT
• Claude
• Gemini
• Perplexity
• Microsoft Copilot
• Google AI search experiences (where relevant)

Capture:

• Full answer text
• Date/time
• Model/platform
• Prompt
• Citations/sources used
• Brands mentioned
• Mention order

3. Calculate competitive share

A simple metric:

\[
AI\ Share\ of\ Voice = \frac{\text{Your brand mentions}}{\text{All tracked brand mentions}} \times 100
\]

Example:

Also track:

Mention rate

• % of prompts where a brand appears

Recommendation rate

• % of prompts where AI recommends the brand

Average position

• Whether the brand appears first, second, etc.

Citation share

• Which websites/sources cause AI systems to recommend competitors

These additional metrics help explain why a competitor is winning, not just that they are winning. www.answermonk.aiscrunch.comwww.zumihq.com

Tool options

Enterprise / marketing intelligence

Consider:

• Profound — enterprise-oriented AI visibility tracking
• Scrunch AI — competitive presence, prompts, and citation analysis scrunch.com
• Zumi — tracks mention rate, share of voice, position, and citation share www.answermonk.aiscrunch.comwww.zumihq.com

Lightweight / startup-friendly

• AnswerMonk — focuses on AI share of voice and competitor rankings across assistants www.answermonk.ai
• Visibility.so — tracks prompts, competitors, citations, and share of voice visibility.so

Dashboard I would recommend

A weekly executive dashboard:

AI Market Presence

• Overall AI Share of Voice
• Change vs previous month
• Rank among competitors

Platform Breakdown

Prompt Gap Analysis

Questions where competitors appear and you do not:

Citation Gap

Track:

• Which review sites mention competitors
• Which comparison pages AI cites
• Which authoritative sources are missing your brand

If building internally

A simple architecture:

1. Prompt database
   • PostgreSQL/Airtable

2. Scheduler
   • Daily/weekly prompt execution

3. AI response collector
   • API access where available
   • Browser automation where permitted

4. Entity extraction
   • Detect brands, products, sentiment, ranking

5. Analytics layer
   • Tableau / Looker / Power BI

6. Alerts
   • “Competitor overtook you for 20 high-value prompts”
   • “Your mention rate dropped >10%”

For most companies, I would start with 500–1,000 high-value prompts, run them weekly, and report AI Share of Voice + citation gaps + competitor takeover opportunities rather than trying to measure every possible query. This gives a useful competitive signal without creating a large monitoring burden.

Yes. If your goal is specifically “how often do AI assistants mention/recommend us vs. competitors?”, I’d use an AI visibility / share-of-voice monitoring platform, rather than traditional SEO rank tracking.

My recommendation

Start with a dedicated AI-visibility tracker such as citedSpy or citepower.

Both are designed around the metric you care about: running a consistent set of prompts through multiple AI engines, detecting brand/competitor mentions, and calculating competitive share of voice. citepower, for example, tracks ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews and exposes visibility, competitor SOV, sentiment and citations. citepower.com

CitedSpy similarly tracks ChatGPT, Claude, Perplexity, Gemini, Grok and Copilot and provides competitor SOV, rankings and prompt-level results. www.citedspy.com

What I would measure

Build a fixed prompt universe around your actual buying journeys:

A particularly useful definition is SOV at the answer level: percentage of tested AI answers in which each brand appears. This is already being used in current AI-visibility benchmarking. www.citedspy.comfoglift.iosupport.birdeye.com

Example executive dashboard

Suppose you run 1,000 prompts across ChatGPT, Gemini, Perplexity and Google AI answers:

genui{"chart":{"chartType":"bar","meta":{"title":"AI answer share of voice","description":"Illustrative competitive share of AI answers across a fixed prompt set.","footer":"Example data — replace with your measured results."},"xKey":"brand","series":[{"dataKey":"sov","label":"Share of voice","valueSuffix":"%"}],"data":[{"brand":"Your brand","sov":34},{"brand":"Competitor A","sov":28},{"brand":"Competitor B","sov":21},{"brand":"Competitor C","sov":12},{"brand":"Other","sov":5}]}}

I'd then break that down by AI engine × customer intent × competitor. For example:

• Overall SOV: 34%
• ChatGPT: 41%
• Gemini: 36%
• Perplexity: 29%
• Google AI: 31%
• “Best X” prompts: 45%
• “X alternatives” prompts: 22%
• “X vs Y” prompts: 38%

That tells you much more than a single visibility score.

One important methodological point

Don't just ask each model a handful of questions once. AI answers can vary between runs, so use a large, fixed prompt set and repeated measurements, then trend the results weekly/monthly. Current AI-share-of-voice methodologies explicitly recommend multiple prompts and several runs per prompt because answers are non-deterministic. verityscore.io

If I were choosing today: I'd shortlist CitedSpy, citepower, and Birdeye Search AI. Birdeye is particularly interesting if you already use it because its current Visibility Reports explicitly include competitor tables, average visibility, SOV, platform filtering and historical trends. www.citedspy.comfoglift.iosupport.birdeye.com

If you tell me your industry/company and roughly how many competitors you want to track, I can recommend the best tool and design the prompt taxonomy + SOV calculation I'd use.

If your goal is specifically “How often does an AI assistant mention us vs. competitors?”, I’d recommend an AI-search visibility monitoring platform rather than traditional SEO rank tracking.

My pick: Scrunch

Scrunch AI visibility monitoring

Scrunch is particularly well aligned with your use case because it measures competitive presence directly from actual AI responses. It can track prompts across ChatGPT and other answer engines, identify brand/competitor mentions, and calculate the percentage of responses in which each brand appears. scrunch.comscrunch.com

A useful dashboard would look conceptually like:

genui{"chart":{"chartType":"bar","meta":{"title":"Illustrative AI answer share","description":"Example of how to compare the percentage of monitored AI answers mentioning each brand.","footer":"Illustrative data only — not measured market data."},"xKey":"brand","series":[{"dataKey":"share","label":"AI answer presence","axisLabel":"Share of monitored answers","valueSuffix":"%"}],"data":[{"brand":"Your brand","share":42},{"brand":"Competitor A","share":37},{"brand":"Competitor B","share":29},{"brand":"Competitor C","share":18}]}}

The metrics I'd track

Scrunch supports filtering these measurements by AI platform, topic, prompt, persona, funnel stage, geography, and branded/non-branded queries, which is important because an aggregate number can hide major differences between use cases. scrunch.com

One important measurement caveat

Don't simply add competitor percentages together. If 100 answers are analyzed and Competitor A appears in 30 while B appears in 25, some of those answers may mention both. Scrunch explicitly distinguishes individual competitor presence from aggregate “at least one competitor appeared” presence. helpcenter.scrunchai.com

I'd therefore report:

Your brand: 42% presence
Competitor A: 37%
Competitor B: 29%
Competitor C: 18%

…and separately calculate a normalized share of voice if you want a true “we own X% of competitive visibility” metric.

Strong alternative: Profound

Profound AI search competitive benchmarking

Profound is probably the better choice if you want a more sophisticated enterprise competitive-intelligence/AEO workflow. It explicitly provides visibility rank, citation share, share of voice, sentiment, position, and competitor benchmarking across ChatGPT, Gemini, Perplexity, Google AI Overviews and other platforms. www.tryprofound.com

My recommendation:

• Scrunch → best fit if your primary KPI is “What percentage of AI answers mention us vs. competitors?”
• Profound → better if you also want deeper competitive/citation intelligence and an enterprise AEO program.

For either solution, I'd build the measurement around 100–500 high-value prompts, weighted toward non-branded, purchase-intent questions, and report share of voice by competitor × topic × AI platform × month rather than relying on one overall percentage. Scrunch itself recommends organizing prompts by topic, persona and funnel stage. scrunch.com

A good solution is to treat this as AI answer share-of-voice (AI SOV) monitoring: build a recurring system that asks the same buyer-intent questions across chat assistants, captures the answers, detects brand mentions, and calculates competitor presence. Several tools now specialize in this category, tracking prompts, mentions, citations, sentiment, and competitor share across assistants such as ChatGPT, Claude, Gemini, and Perplexity. www.scalecraft.aivisibility.so

Recommended approach

1. Create a “prompt universe”

Do not monitor random questions. Build a library of prompts your customers actually ask:

Examples:

• “What are the best [category] platforms for enterprise?”
• “Compare [your brand] vs [competitor A]”
• “What tools should a [persona] use for [problem]?”
• “Alternatives to [competitor]”
• “Best vendors for [use case]”

Group them by:

• Buying stage (awareness, evaluation, selection)
• Product category
• Customer segment
• Geography/language
• Competitor

A typical starting point:

• 100–500 prompts
• 5–10 competitors
• Weekly or daily monitoring

2. Track every AI response

For each prompt, store:

Tools such as Profound, Visibility.so, and similar AI visibility platforms provide versions of this workflow. visibility.so

3. Calculate competitive share

The simplest metric:

AI Share of Voice (%)

\[
\text{AI SOV} =
\frac{\text{Brand mentions}}
{\text{Total competitor + brand mentions}}
\times 100
\]

Example:

Across 1,000 tracked answers:

Add weighting because appearing first matters more:

Example:

• Position #1 mention = 3 points
• Position #2 = 2 points
• Position #3+ = 1 point

This creates a weighted AI visibility score, not just a mention count.

4. Track the gaps that matter

The most actionable report is:

“Competitor appears, you do not.”

Example:

This tells marketing teams what content, PR, reviews, documentation, or partnerships may improve AI visibility.

5. Recommended tool categories

Enterprise monitoring

Best when you need executive dashboards, many prompts, and multiple AI engines:

• Profound
• Evertune

Mid-market / marketing teams

Good balance of monitoring and reporting:

• Visibility.so
• LLM Monitor

Build-your-own stack

For companies with data teams:

• Prompt scheduler:
  • API automation
  • Browser automation where needed
• Storage:
  • BigQuery / Snowflake / Postgres
• NLP layer:
  • Brand entity extraction
  • Competitor matching
  • Sentiment classification
• Dashboard:
  • Looker / Tableau / Power BI

Dashboard KPIs I would track

Executive view

• AI Share of Voice %
• Visibility trend (30/90 days)
• Competitor ranking
• Top lost prompts

Marketing view

• Prompts where competitors win
• Missing citations
• Content/source gaps
• Sentiment changes

Product view

• Feature comparisons where competitors dominate
• Incorrect AI descriptions
• Category positioning issues

My recommended starting setup

For most companies:

1. Track 200 high-value prompts
2. Run them weekly across ChatGPT, Claude, Gemini, and Perplexity
3. Measure:
   • mention rate
   • weighted position
   • citations
   • sentiment
   • competitor overlap
4. Build a leaderboard:
   • Your brand: 35%
   • Competitor A: 28%
   • Competitor B: 22%
   • Others: 15%

This gives you a practical “SEO rank tracker equivalent” for AI assistants. AI visibility tools increasingly provide exactly this type of competitor benchmarking and share-of-voice reporting. www.doishowup.com

If your goal is specifically “How often does the AI assistant mention my competitors, and what percentage of the answer market do we each own?”, I’d use an AI visibility / GEO share-of-voice platform, rather than traditional SEO monitoring.

My recommendation

Start with Profound or Peec AI for a serious enterprise program; use a lighter tool such as DoIShowUp if you mainly need straightforward monitoring.

The key is to measure two different percentages:

1. Mention/visibility rate

brand appears in answers ÷ total prompts tested

2. Share of voice (SOV)

brand mentions ÷ total competitor + brand mentions

SOV is the better competitive metric because it tells you whether you're gaining or losing the recommendation space to competitors. Current tools in this category explicitly support competitor SOV, prompt-level results, engine-level breakdowns, and trends. www.ai-visibility-software.com

What I'd build the monitoring program around

Run a fixed panel of 50–200 buyer prompts every week, such as:

• “What are the best [category] platforms?”
• “What should a mid-market company use for [job]?”
• “Compare [your brand] vs [competitor].”
• “What are alternatives to [competitor]?”
• “Which [category] vendors are best for [industry/use case]?”

Run them across ChatGPT, Claude, Gemini, Perplexity and Google AI experiences, rather than treating ChatGPT as the entire market. Current visibility platforms support increasingly broad multi-engine monitoring. www.viaudit.com

For every answer, capture:

The dashboard I'd want

genui{"chart":{"chartType":"bar","meta":{"title":"Example AI share of voice","description":"Illustrative competitive share across a fixed buyer-prompt set.","footer":"Illustrative data — replace with measured results from your prompt monitoring."},"xKey":"company","series":[{"dataKey":"share","label":"Share of voice","axisLabel":"Share of voice","valueSuffix":"%"}],"data":[{"company":"Your brand","share":34},{"company":"Competitor A","share":27},{"company":"Competitor B","share":21},{"company":"Competitor C","share":12},{"company":"Others","share":6}]}}

Then trend that number weekly:

Your SOV: 34% → 37% → 41%
Competitor A: 27% → 29% → 24%

That's much more actionable than simply saying “we received 127 mentions.”

One important methodological point

Don't take a single ChatGPT answer as representative. AI responses are non-deterministic, so the more defensible methodology is multiple runs per prompt, across engines and markets, with confidence/range reporting. verityscore.io

For example, I'd report:

AI Share of Voice: 34% ± 3 pp
1,000 prompt/engine runs
125 buyer prompts
5 AI engines
30-day rolling average

That gives your marketing team something they can actually benchmark month over month.

Which solution I'd choose

• Enterprise / strategic competitive intelligence: Profound — best fit if this becomes an executive-level measurement program.
• Strong SOV + prompt-level competitive analysis: Peec AI / Otterly — good if the primary requirement is competitive visibility measurement.
• Simple, inexpensive monitoring: DoIShowUp — its current offering explicitly provides competitor SOV, prompt monitoring, screenshots and change alerts. www.ai-visibility-software.comverityscore.iowww.doishowup.com
• Build your own: use the APIs for the target assistants, maintain a controlled prompt corpus, store every response, and calculate SOV yourself. This gives you the most methodological control but substantially more engineering/maintenance.

If this is for a brand/marketing team, my preferred setup would be Profound + a standardized prompt taxonomy + weekly SOV reporting, with the dashboard broken down by competitor × AI engine × customer intent × geography. That turns “what does ChatGPT say about us?” into a measurable competitive KPI.

Yes. I’d treat this as an AI-answer share-of-voice (SOV) monitoring problem rather than conventional SEO rank tracking.

Recommended solution

For most teams, I’d shortlist Visibility.so as the starting point. It tracks ChatGPT, Claude, Gemini and Perplexity, lets you monitor defined prompts, compares competitor SOV, shows historical movement, and exposes the sources cited in answers. visibility.so

If your priority is specifically deep competitor benchmarking, Otterly.AI is also worth evaluating; an independent 2026 comparison rated it particularly strongly for competitive SOV and source-stack intelligence. proofmap.com

What I would measure

Build a fixed prompt universe of roughly 50–200 buyer questions, grouped by intent:

The important distinction is mention rate vs. SOV. For example, if you appear in 40% of prompts, that doesn't necessarily mean you own 40% of the competitive recommendation space. SOV should normalize your presence against the brands appearing in the same answers. This is the approach used by current AI-visibility platforms. viali.ai

The dashboard I'd build

genui{"chart":{"chartType":"bar","meta":{"title":"AI answer share of voice","description":"Illustrative competitive benchmark showing each brand's share of tracked AI-answer mentions.","footer":"Illustrative data — replace with your monitored results."},"xKey":"brand","series":[{"dataKey":"sov","label":"Share of voice","valueSuffix":"%"}],"data":[{"brand":"Your brand","sov":32},{"brand":"Competitor A","sov":27},{"brand":"Competitor B","sov":21},{"brand":"Competitor C","sov":12},{"brand":"Other","sov":8}]}}

Then add four views:

1. Executive: Your SOV vs. competitors, trend over time, biggest gains/losses.
2. Prompt-level: Every question → answer → brands mentioned → positions.
3. Competitor gap: “Competitor X appears in 63 prompts where we don't.”
4. Citation intelligence: Sources repeatedly cited for competitors but not for you.

One important methodological point

Don't rely on a single run of each prompt. AI answers are nondeterministic, so run prompts repeatedly and report aggregated rates/ranges rather than treating one ChatGPT answer as ground truth. verityscore.io

I'd also track the exact answer and citations, not just whether your brand was mentioned. That turns the system from a vanity dashboard into something your SEO/content team can act on.

My practical recommendation: start with Visibility.so if you want an off-the-shelf solution; use Otterly.AI if competitive SOV/source analysis is the primary requirement. If you tell me your industry, number of competitors, and whether you need ChatGPT only or ChatGPT + Gemini/Claude/Perplexity, I can recommend the most appropriate setup and KPI formula.

Yes. What you’re describing is usually called AI Search / Generative Engine Visibility monitoring: systematically run a fixed set of buyer prompts through ChatGPT and other assistants, capture the answers, detect brand mentions, and calculate competitive share of voice.

My recommendation: Profound

Profound is the strongest fit if this is for a serious marketing/competitive-intelligence program. Its Answer Engine Insights product tracks responses from ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, Claude, Copilot, Grok and others, and provides visibility, share of voice, position, sentiment, citations, and competitor rankings. www.tryprofound.com

Profound — Answer Engine Insights

The key advantage is that you can define a prompt universe such as:

• “best [category] software”
• “alternatives to [category leader]”
• “best [product type] for enterprise”
• “[problem] software for a 500-person company”
• “compare A vs B vs C”
• “[category] vendors with [specific capability]”

Then monitor you + competitors on exactly the same prompts.

The dashboard I'd build

For example, suppose 1,000 relevant answers produce:

• You: 240 mentions
• Competitor A: 400
• Competitor B: 220
• Competitor C: 140

Your mention share of voice = 24%, while Competitor A has 40%.

That's more useful than simply saying “we appeared in 24% of answers,” because visibility and competitive share are different metrics. Both Profound and Peec explicitly distinguish these concepts. help.tryprofound.comwww.tryprofound.com

A good alternative: Peec AI

Peec AI is particularly attractive if you want a simpler marketing-team workflow. It explicitly provides visibility, position, sentiment and share of voice, lets you add competitors, and tracks prompts across AI models. peec.ai

Peec AI

I'd shortlist it alongside Profound rather than build the monitoring system yourself.

If you want to build it internally

The architecture is relatively straightforward:

Prompt library → scheduled assistant queries → response storage → brand/entity extraction → position/sentiment classification → competitor aggregation → dashboard

I'd use 1,000–5,000 carefully designed prompts, rather than thousands of random prompts. Segment them by:

1. Category discovery
2. Problem/need
3. Product comparison
4. Alternatives
5. Use case
6. Industry
7. Buyer persona
8. Geography

Then calculate two separate KPIs:

Visibility

% of prompts whose answer mentions your brand

Competitive SOV

Your brand mentions ÷ total mentions of tracked brands

And trend both weekly and monthly.

One important methodological point: don't treat a single model's answers as “the market.” AI responses vary substantially by platform, prompt wording, location, personalization, and time. A robust measurement system therefore fixes the prompt set, samples repeatedly, and reports confidence/range and trend, rather than overinterpreting a single percentage.

If the goal is specifically “I want to know what percentage of ChatGPT answers recommend us vs. Competitor A/B/C, and see that number over time,” I'd start with Profound, with Peec AI as the main alternative. Profound also has a particularly useful competitive-citation layer for explaining why competitors are winning. www.tryprofound.com

Yes. What you want is generally called AI Search / GEO (Generative Engine Optimization) share-of-voice monitoring: repeatedly ask a controlled set of buyer prompts to ChatGPT and other assistants, capture the answers, identify which brands appear, and calculate each brand’s share. Current platforms increasingly offer exactly this workflow. segeo.iovisibility.so

My recommendation

Start with a dedicated AI-visibility platform rather than building the monitoring infrastructure yourself. I’d shortlist:

If your primary KPI is specifically "what % of AI answers mention us vs. competitors," I'd start by evaluating seGEO and Visibility.so. If you need seven-engine coverage, look at ViAudit as well.

The metric I'd use

Don't measure just "number of mentions." Build a Competitor AI Share of Voice metric around a fixed prompt universe.

For example, suppose you track 500 prompts:

• 300 answers mention your company
• 240 mention Competitor A
• 150 mention Competitor B
• 110 mention Competitor C

You can report:

Brand mention rate: 60%
Competitor A: 48%
Competitor B: 30%
Competitor C: 22%

But I'd add a second metric: weighted answer share. A brand appearing as the #1 recommendation should count more than a brand buried in a list of 10. This gives you a more meaningful competitive picture than raw mentions.

A useful executive dashboard would therefore have:

genui{"chart":{"content":{"chartType":"bar","meta":{"title":"Illustrative AI answer share","description":"Example competitor share of AI answers across a fixed prompt set; values are illustrative, not measured data."},"xKey":"company","series":[{"dataKey":"share","label":"Answer share","axisLabel":"Share of answers","valueSuffix":"%"}],"data":[{"company":"Your brand","share":42},{"company":"Competitor A","share":31},{"company":"Competitor B","share":18},{"company":"Competitor C","share":9}]}}}

More importantly: capture why competitors win

I'd make the monitoring dataset granular enough to answer:

1. Which prompts mention us?
2. Which prompts mention competitors but not us?
3. Who gets recommended #1?
4. Which competitors are gaining/losing share week over week?
5. Which sources are being cited when competitors appear?
6. What claims does the assistant make about each company?
7. Does the answer characterize us positively, neutrally, or negatively?

This matters because AI visibility isn't simply a ranking problem. Recent research suggests AI-answer visibility has multiple stages—discoverability, citation, and how much a cited source actually influences the generated answer—and outputs can vary substantially between runs. arxiv.org

The measurement design I'd recommend

Use roughly 100–500 high-value prompts, segmented into:

• Category questions — "best X software"
• Comparison questions — "X vs Y"
• Buying questions — "best X for a mid-market company"
• Problem/solution questions — "how do I solve X?"
• Use-case questions
• Competitor-specific questions
• Persona/industry/location variants

Run each prompt repeatedly and keep the exact answer, model, date, prompt, cited URLs and brand positions. This is important because AI answers aren't deterministic; a single query/run isn't a reliable market-share measurement. arxiv.org

Then calculate:

AI SoV = your brand's weighted appearances ÷ total weighted brand appearances

I'd report it by engine + prompt category + competitor + time, rather than one giant number.

One important caveat

Don't equate AI share of voice with actual market share or revenue. It's a visibility/recommendation metric. Ideally connect it downstream to AI referral traffic, leads and conversions. Research on ChatGPT referral traffic also shows why it's important to separate platform-wide growth from the actual effect of optimization work. arxiv.org

If you're choosing one solution today: I'd pilot seGEO against Visibility.so using the same 50–100 prompts and your 3–5 biggest competitors. Compare prompt coverage, repeatability, competitor SoV, answer-level evidence, citations, alerts and API/export capability before committing.

A good solution is to treat this as AI Share of Voice (AI SOV) / Generative Engine Optimization (GEO) monitoring: systematically query AI assistants with buyer-intent prompts, capture answers, detect competitor mentions, and calculate each brand’s percentage of visibility. Several emerging platforms do exactly this. www.frictionai.cosiftly.ai

Recommended approach

1. Build a fixed “prompt universe”

Create 100–500 prompts that represent how customers discover products:

Examples:

• “What are the best [category] platforms for enterprise teams?”
• “Compare [your brand] vs [competitor A]”
• “What tools should a startup use for [problem]?”
• “Who are the market leaders in [category]?”

Track the same prompts every week/month so changes are measurable.

2. Monitor multiple AI assistants

Track answers from:

• ChatGPT
• Claude
• Gemini
• Perplexity
• Google AI Overviews / AI search experiences

The goal is not just whether your brand appears, but:

• position in answer (1st recommendation vs buried mention)
• sentiment
• whether competitors appear
• citations/sources used
• recommendation strength

3. Calculate competitor share

A simple metric:

AI Share of Voice (%)

\[
\text{Brand SOV} =
\frac{\text{Brand mentions}}
{\text{Total tracked brand mentions}}
\times 100
\]

Example:

Across 1,000 AI answers:

You can also add weighted scoring:

• #1 recommendation = 3 points
• Mentioned in top 3 = 2 points
• Mentioned anywhere = 1 point

This avoids treating a buried mention the same as a recommendation.

4. Use an AI visibility platform

Options worth evaluating:

These types of tools generally automate prompt runs, collect AI responses, identify brand mentions, and report share-of-voice trends. siftly.ai

5. Create an executive dashboard

Track monthly:

Visibility

• AI mention rate %
• AI SOV %
• competitor gap

Recommendation quality

• average ranking position
• % of answers where you are the recommended choice
• sentiment score

Competitive movement

• competitors gaining share
• prompts where competitors win
• new citations/sources driving competitor visibility

Example KPI:

“Increase AI recommendation share from 18% → 30% across 250 buyer-intent prompts in 6 months.”

My recommended stack by company size

Startup / SMB

• Peec AI, Siftly, or similar AI visibility tracker
• Google Sheets/Looker Studio dashboard

Mid-market

• Semrush or Ahrefs + dedicated AI visibility tool
• Weekly automated reporting

Enterprise

• Profound-class platform
• Custom prompt library
• CRM attribution layer to connect AI visibility → pipeline

A key point: do not measure only “mentions.” A competitor appearing as the first recommended option is much more valuable than being mentioned alongside five alternatives. The strongest programs combine mention share + recommendation rank + citation authority + conversion impact. siftly.ai

Yes. I’d treat this as an AI-answer “share of voice” measurement system, rather than traditional SEO rank tracking.

Recommended solution

For most teams, I’d start with a dedicated GEO/AI-visibility platform such as DeepSmith, Seerly, or Ranksmith, rather than building the monitoring infrastructure yourself. These tools already track competitor mentions, prompt-level answers, citations, and share of voice across multiple AI engines. deepsmith.ai

My preferred setup:

1. Define 100–500 buyer prompts
   • “Best [category] for…”
   • “[Category] alternatives”
   • “[Competitor] vs [your brand]”
   • “Which [product/service] should I choose?”
   • Problem/use-case queries.
   • Include different customer segments and geographies.

2. Run them repeatedly across
   • ChatGPT
   • Gemini
   • Perplexity
   • Claude
   • Google AI Overviews/AI Mode where available.

Repeated sampling matters because AI answers are stochastic and can vary substantially between runs. Recent research specifically recommends repeated measurements and controls rather than treating one answer as a definitive ranking. arxiv.org

3. Capture the complete answer, not just whether your brand appeared:
   • Brand mentioned: yes/no
   • Competitors mentioned
   • Position/order of recommendation
   • Recommendation strength
   • Sentiment/perception
   • Your URL cited
   • Competitor URLs cited
   • Sources used by the assistant
   • Exact prompt + exact response
   • Model/engine + date.

4. Calculate competitive share of voice

A simple metric is:

Brand SOV = your brand's appearances ÷ total brand appearances

For example, across 1,000 answers:

This is preferable to simply counting mentions because it gives you a competitive denominator. Tools such as DeepSmith and Seerly use similar concepts for competitive AI visibility. deepsmith.ai

But I'd add two more metrics

1. Recommendation share

Not all mentions are equal. Being listed as one of five alternatives is different from:

“The best option is Brand X.”

So calculate:

Recommendation Share = answers where brand is recommended / relevant answers

2. Citation share

Measure what percentage of AI answers cite your owned properties versus competitors' properties.

This is particularly useful because being mentioned and being used as an information source are different things. getgeoscan.ai

The dashboard I'd build

genui{"chart":{"content":{"chartType":"bar","meta":{"title":"Illustrative AI answer share of voice","description":"Example competitive share of voice across monitored AI answers; replace with measured data."},"xKey":"company","series":[{"dataKey":"sov","label":"AI share of voice","axisLabel":"Share of voice","valueSuffix":"%","valueFormat":"raw"}],"data":[{"company":"Your brand","sov":35},{"company":"Competitor A","sov":30},{"company":"Competitor B","sov":20},{"company":"Competitor C","sov":15}]}}}

I'd have five core views:

• Overall SOV: You vs every competitor
• Engine SOV: ChatGPT vs Gemini vs Perplexity vs Claude
• Prompt SOV: Which questions are you winning/losing?
• Citation SOV: Which sources are influencing answers?
• Trend: Weekly/monthly movement in visibility

Then add a particularly valuable report:

“Where competitors win and we don't.”

For every prompt where Competitor A appears but you don't, show the exact answer, cited sources, and recurring source domains. That turns monitoring into an actionable GEO program rather than another analytics dashboard.

Buy vs build

Buy if your goal is marketing intelligence. I'd shortlist DeepSmith, Seerly, and Ranksmith based on the capabilities they currently advertise. deepsmith.ai

Build if you need highly customized measurement or have engineering resources. The architecture is straightforward:

Prompt library → AI engine APIs/runs → response archive → entity extraction → competitor classification → SOV calculation → dashboard/alerts

I'd also store the raw response permanently. That's critical because otherwise you can see that SOV changed but can't determine why.

One important caveat: don't report a single “AI visibility %” as if it were an objective market-share number. Treat it as a sample-based measurement, with fixed prompts, repeated runs, confidence/error bands, and consistent engine/model settings. AI-answer behavior is still highly variable across engines and over time. arxiv.org

If you're designing this for an enterprise, I can also lay out a specific measurement framework + database schema + dashboard KPIs for tracking your brand against 5–20 competitors.

Yes. I’d recommend building a “Chat Share of Voice” monitor that treats AI assistants like a new search channel.

Recommended solution

1. Create a fixed prompt set

• 100–500 prompts across your key categories/use cases.
• Include branded, unbranded, comparison, “best X,” and problem-solving prompts.
• Run the same prompts periodically across ChatGPT, Gemini, Claude, Perplexity, etc.

2. Capture every answer
For each response, store:

• Assistant/model
• Prompt
• Date/time
• Competitors mentioned
• Your brand mentioned/not mentioned
• Position/rank of each brand
• Recommendation sentiment
• Citation/source, when available

3. Calculate AI Share of Voice

The basic metric should be:

Brand SOV = competitor/brand mentions ÷ total brand mentions

But I’d actually track three metrics:

You can also weight position—for example, a #1 recommendation counts more than a #7 mention.

genui{"chart":{"chartType":"bar","meta":{"title":"Illustrative AI share of voice","description":"Example comparison of competitor presence in assistant answers; replace with measured data."},"xKey":"brand","series":[{"dataKey":"share","label":"Mention share","axisLabel":"Share of answers","valueSuffix":"%"}],"data":[{"brand":"Your brand","share":32},{"brand":"Competitor A","share":27},{"brand":"Competitor B","share":21},{"brand":"Competitor C","share":12},{"brand":"Others","share":8}]}}

4. Make the dashboard actionable

I’d have four core views:

Executive:
Your SOV vs. competitors, trend over time, and biggest gains/losses.

Competitive:
Which prompts competitors win that you lose.

Category:
SOV by product/category, customer segment, geography, and intent.

Content opportunities:
Questions where competitors are consistently cited/recommended but your brand isn't.

5. Add an “AI visibility score”

A useful composite could be:

AI Visibility = 40% Mention Share + 40% Recommendation Share + 20% Top-3 Share

Track it weekly/monthly and break it down by assistant and prompt category.

What I'd build

If this is for a company rather than a one-off analysis, I’d use:

Prompt repository → scheduled assistant runs → answer parser → brand/entity extraction → SOV calculation → dashboard → alerts

The important part is not just counting mentions. An assistant saying “Brand X is an alternative, but Brand Y is the better choice” shouldn't be treated the same as recommending Brand X first.

If you tell me your industry/company and 5–10 competitors, I can design the exact measurement framework, dashboard schema, and scoring methodology for it.

A good solution is to build a “Chat Assistant Share of Voice (SOV) Monitoring Platform” that continuously measures how often your brand vs. competitors appear in AI-generated answers across assistants such as ChatGPT, Claude, Gemini, Perplexity, Copilot, and industry-specific AI tools.

Recommended approach

1. Create a competitor prompt monitoring engine

Build a library of representative user prompts:

Example category: Project management software

• “What is the best project management tool for startups?”
• “Compare Asana vs Monday vs ClickUp”
• “Best alternatives to Jira”
• “Which tool is easiest for remote teams?”

For each prompt, run it periodically against selected AI assistants and capture:

• Full answer text
• Brands mentioned
• Position/rank of each brand
• Sentiment/context
• Recommendation strength
• Citations/links included
• Model, date, region, language

2. Calculate AI Share of Voice

Use metrics similar to SEO visibility, but adapted for AI answers.

Core metric: AI Mention Share

\[
\text{AI Share of Voice} =
\frac{\text{Your brand mentions}}{\text{Total competitor brand mentions}}
\times 100
\]

Example:

3. Add weighted visibility scoring

A simple mention count is not enough. A brand appearing first in an answer is more valuable.

Example scoring:

Formula:

\[
\text{AI Visibility Score} =
\frac{\sum(\text{weighted mentions})}{\text{total opportunities}}
\]

4. Track competitive intelligence dashboards

Recommended dashboard views:

Market Share Dashboard

• AI Share of Voice %
• Trend over time
• Competitor ranking changes

Prompt Gap Analysis

Shows prompts where competitors win:

Recommendation Quality

Track:

• “Recommended as best”
• “Good alternative”
• “Mentioned only”
• “Not recommended”

5. Suggested technical architecture

Data collection layer

Options:

• Automated API access where available
• Browser automation for assistants without APIs
• Human QA sampling

Store:

• Prompt
• Response
• Timestamp
• Model/version
• Geography
• Competitor entities detected

AI analysis layer

Use an LLM classifier to extract:

{
  "brand": "Competitor A",
  "mentioned": true,
  "position": 1,
  "recommendation_strength": "high",
  "sentiment": "positive"
}

Database

Good choices:

• PostgreSQL for structured analytics
• Elasticsearch/OpenSearch for answer search
• Vector database for similarity analysis

Dashboard

Tools:

• Tableau
• Power BI
• Custom React dashboard

6. Existing platforms to evaluate

Some vendors are moving into AI visibility tracking:

• Profound — focuses on tracking brand presence in AI-generated answers.
• Peec AI — tracks visibility in AI search results.
• Semrush — has expanded into AI search visibility features.
• Ahrefs — useful for related search visibility analysis.

7. Recommended operating cadence

Daily

• Run top 100–500 priority prompts
• Detect major competitor changes

Weekly

• Review:
  • Lost recommendations
  • New competitor mentions
  • Emerging prompts

Monthly

• Executive report:
  • AI market share %
  • Competitor movement
  • Content/product gaps

MVP recommendation (8–12 weeks)

Start with:

1. 200–500 industry prompts
2. 5–10 competitors
3. 3 AI assistants
4. Daily collection
5. Automated entity extraction
6. Share-of-voice dashboard

The key KPI should be:

“What percentage of relevant AI answers recommend us compared with competitors?”

This is the AI-era equivalent of SEO visibility tracking.

A good solution is to build a Generative AI Share of Voice (SoV) monitoring platform that continuously tests competitor visibility inside AI assistant responses (ChatGPT, Claude, Gemini, Perplexity, Copilot, etc.) and reports mention share, ranking position, sentiment, and recommendation share.

Recommended solution architecture

1. Prompt intelligence layer

Create a tracked prompt library representing how customers discover products.

Examples:

• “Best CRM software for startups”
• “Alternatives to Salesforce”
• “Top accounting tools for small businesses”
• “Which [category] platform should I choose?”

Organize prompts by:

• Buyer journey stage
• Industry
• Geography
• Customer segment
• Product category

Run these prompts on a schedule (daily/weekly).

2. AI answer collection engine

Automate querying of major assistants:

Capture:

• Full response text
• Model/version
• Date/time
• Prompt used
• Sources cited
• Position of each brand mentioned

3. Brand extraction + scoring engine

Use NLP/LLM extraction to identify:

Presence metrics

• Was the brand mentioned?
• How many times?
• Was it in the top recommendations?

Position metrics
Example scoring:

4. Calculate AI Share of Voice

A simple metric:

\[
AI\ Share\ of\ Voice = \frac{Brand\ Score}{Total\ Competitor\ Scores} \times 100
\]

Example:

Track changes over time:

• “Company A gained +8 pts this month”
• “Competitor B overtook us for enterprise prompts”
• “Brand absent from 65% of high-intent queries”

5. Dashboard

Recommended views:

Executive dashboard

• AI Share of Voice %
• Trend vs competitors
• Top winning prompts
• Lost opportunities

Marketing dashboard

• Prompts where competitors appear but you don't
• Competitor positioning themes
• Sentiment comparison

SEO/content dashboard

• Missing topics
• Frequently cited sources
• Content opportunities

Existing tools worth evaluating

For a faster deployment, look at AI visibility monitoring platforms such as:

• Profound AI Search Visibility Platform — focused on tracking brand presence in AI answers.
• Scrunch AI — monitors AI search visibility and brand representation.
• Peec AI — tracks brand visibility across AI search engines.
• Otterly AI — tracks AI search rankings and mentions.
• Semrush AI Visibility tools — extends existing SEO workflows into AI visibility monitoring.

If building internally

A typical stack:

Data collection

• API access where available
• Browser automation for unsupported assistants
• Prompt scheduler

Processing

• LLM extraction pipeline
• Entity recognition
• Sentiment classifier
• Citation parser

Storage

• PostgreSQL for results
• Vector database for answer similarity

Visualization

• Tableau
• Microsoft Power BI
• Custom React dashboard

Metrics I would prioritize

1. AI Share of Voice (%)

“How often are we recommended vs competitors?”

2. Recommendation Share (%)

“How often are we in the top 3 answers?”

3. Prompt Coverage (%)

“What percentage of relevant queries mention us?”

4. Competitive Gap Score

“Where do competitors appear and we don't?”

5. Citation Share (%)

“Which brands/content sources are AI systems relying on?”

6. Sentiment Score

“Are AI assistants describing us positively?”

For most companies, I would start with a 30–1000 prompt benchmark, run it weekly across 3–5 AI assistants, and build a competitor share-of-voice dashboard before investing in a full platform.

If your goal is to measure how often your brand versus competitors appears in AI assistant answers (ChatGPT, Gemini, Claude, Perplexity, Copilot, etc.) and compare share over time, you're looking for an AI visibility / AI search analytics platform rather than traditional SEO software.

Here are the strongest options:

My recommendation

If you're evaluating vendors today:

Enterprise (large brands)

• Profound
• Scrunch AI

These have the most mature competitive intelligence capabilities.

Best value

• Peec AI

It offers a good balance of:

• prompt tracking
• competitor monitoring
• share of voice
• historical trends
• dashboarding

Metrics to monitor

I would recommend measuring more than simple mentions.

1. AI Share of Voice (primary KPI)

Your mentions / Total brand mentions

Example:

AI Share of Voice

• You: 42%
• Competitor A: 31%
• Competitor B: 18%
• Competitor C: 9%

2. Prompt coverage

"What percentage of tracked prompts mention us?"

3. Position

• First recommendation
• Top 3
• Mentioned only

4. Citation share

How often each brand is cited as a source.

5. Sentiment

Positive, neutral, or negative framing.

6. Model comparison

Compare performance across:

• ChatGPT
• Gemini
• Claude
• Perplexity
• Microsoft Copilot

Often you'll find a brand is dominant on one model but underrepresented on another.

A recommended reporting dashboard

A weekly dashboard might include:

• Overall AI Share of Voice (%)
• Share by competitor
• Share by AI model
• Share by product category
• Top gaining competitors
• Top lost prompts
• Citation sources
• Month-over-month change

For example:

This kind of breakdown makes it easy to spot where competitive positioning differs across assistants.

If you have engineering resources, another option is to build a custom monitoring system: maintain a fixed library of prompts, query multiple AI assistants on a schedule, use an LLM or rules to extract mentioned brands, calculate share-of-voice metrics, and store historical results in a data warehouse for dashboards in tools like Power BI or Looker. This offers maximum flexibility but requires ongoing maintenance as AI models and APIs evolve.

If your goal is to measure how often your brand appears in AI assistant answers versus competitors, you're looking for what many teams now call AI Search Share of Voice (AI SOV) or LLM Visibility Monitoring.

A good solution should do four things:

1. Maintain a library of prompts relevant to your industry.
2. Query multiple AI assistants (ChatGPT, Gemini, Claude, Perplexity, Copilot, etc.) on a schedule.
3. Detect which brands are mentioned, their rank/order, sentiment, and recommendation strength.
4. Calculate percentage share and trends over time.

Commercial platforms

DIY architecture

If you want complete control, build your own monitoring system.

Workflow

Prompt Library
       │
       ▼
Multiple LLM APIs
(ChatGPT, Claude, Gemini, Perplexity...)
       │
       ▼
Response Storage
(Postgres/S3)
       │
       ▼
NER + Brand Detection
       │
       ▼
Share-of-Voice Calculation
       │
       ▼
Dashboard (Power BI, Tableau, Looker)

Metrics to calculate

For every prompt:

• Was the brand mentioned? (Yes/No)
• Position in the answer
• Number of mentions
• Positive/Neutral/Negative recommendation
• Included in "Top 5" or "Top 10"
• Citation frequency (where applicable)
• Answer length devoted to each brand

Example after 1,000 prompts:

Weighted Share of Voice

A more informative metric weights prominent mentions more heavily.

Example weights:

• First recommendation = 5 points
• Second = 4
• Third = 3
• Mention in body = 1
• Mention in comparison table = 2

Then compute:

Weighted SOV =
Brand Score /
Total Scores

This better reflects how users experience recommendations.

Example dashboard

Track metrics such as:

• Overall AI Share of Voice
• Share by assistant
• Share by prompt category
• Weekly/monthly trends
• Emerging competitors
• Brand sentiment
• Citation sources
• Geographic differences
• Changes after website updates or PR campaigns

Recommended evaluation cadence

• 500–5,000 prompts
• Daily or weekly execution
• 5–10 AI assistants
• Historical storage of every response
• Automatic alerts when visibility changes significantly

Recommendation by organization size

• Small team: Use a commercial AI visibility platform to get started quickly with minimal engineering effort.
• Mid-size company: Combine a commercial platform with a BI dashboard for custom reporting.
• Enterprise: Build an internal monitoring pipeline that queries multiple LLM APIs, stores every response, applies consistent brand extraction and scoring, and computes custom weighted AI Share of Voice metrics aligned with your business.

This approach gives you a reliable percentage share for each competitor while also showing where and how strongly each brand is being recommended, rather than simply counting mentions.

A good solution is to build an AI Answer Share-of-Voice (SOV) monitoring platform that continuously tests competitor visibility across major chat assistants and calculates how often each brand appears, is recommended, or is cited.

A practical architecture:

1. Prompt intelligence layer

Create a monitored prompt library representing real customer journeys.

Examples:

• “Best CRM for startups”
• “Alternatives to [your product]”
• “Which tools should a marketing team use?”
• “Compare [Brand A] vs [Brand B]”
• “Best vendors for enterprise security”

Organize prompts by:

• Buyer stage (awareness, evaluation, purchase)
• Industry
• Geography
• Use case
• Product category

Refresh prompts monthly using:

• Search query data
• Customer questions
• Sales call transcripts
• SEO keyword research
• Community discussions

2. Multi-model answer collection

Run the same prompts across:

• ChatGPT
• Claude
• Gemini
• Perplexity
• Microsoft Copilot
• Other relevant assistants

Capture:

• Full answer text
• Date/time
• Model/version
• Prompt
• Citations/sources
• Recommended brands
• Ranking/order of mentions

Store every response as an audit trail.

3. Brand extraction engine

Use an LLM or NLP pipeline to identify:

Presence metrics

• Was the brand mentioned? (yes/no)
• Position in answer (1st, 2nd, etc.)
• Number of mentions
• Sentiment/context
• Recommendation strength

Example:

Prompt:

“Best project management tools for startups”

Assistant answer:

1. Asana
2. Monday.com
3. ClickUp

Score:

4. Share-of-Voice calculation

A simple metric:

genui{"fractions_ratios_percentages_learning_block":{"type_id":"PERCENT_PART_WHOLE_PROPORTION"}}

AI Answer Share of Voice (%)

\[
\text{SOV}=\frac{\text{Brand appearances}}{\text{Total competitor appearances}}\times100
\]

Example:

Across 1,000 tracked answers:

5. Add visibility quality scoring

Raw mentions are misleading. Weight by impact:

Suggested score:

This gives:

AI Visibility Score = weighted mentions / total opportunities

6. Dashboard views

A useful executive dashboard:

Market view

“Who owns AI recommendations?”

• Brand SOV %
• Trend over time
• Competitor movement

Prompt view

“Where are we losing?”

Example:

Model view

“Where do assistants differ?”

7. Tools you could build with

Enterprise/custom stack

• Prompt scheduler: Airflow / Temporal
• LLM calls: model APIs
• Extraction: GPT-based classifier
• Database: PostgreSQL / BigQuery
• Dashboard: Looker / Tableau / Power BI

Existing categories of tools to evaluate

• AI search monitoring platforms
• SEO rank trackers expanding into AI visibility
• Brand monitoring platforms
• Custom LLM evaluation pipelines

Recommended MVP (4–6 weeks)

1. Track 500–1,000 high-value prompts
2. Run daily against 3 assistants
3. Extract brand mentions automatically
4. Calculate:
   • AI SOV %
   • recommendation rank
   • citation share
   • sentiment
5. Build a dashboard with competitor benchmarking

The key metric I would report to leadership is:

“AI Recommendation Share” = percentage of relevant assistant answers where our brand appears among the recommended options.

It is the closest equivalent to traditional search market share in the AI assistant era.

If your goal is to measure how often competitors appear in AI assistant responses (ChatGPT, Gemini, Claude, Perplexity, Copilot, etc.) and compare market visibility over time, you're looking for an AI Share of Voice (AI SoV) or LLM brand monitoring platform.

Here are the strongest options:

What you should measure

Rather than simply counting mentions, track multiple metrics:

• AI Share of Voice
  • % of prompts where your brand appears
  • % of prompts where each competitor appears

Example:

• Rank Position
  • Mentioned first
  • Mentioned in top 3
  • Mentioned at all

Being the first recommendation is usually much more valuable than appearing fifth.

• Sentiment
  • Positive
  • Neutral
  • Negative

• Citation Share
  • Which websites the assistant cites
  • How often your domain is referenced versus competitors

• Category Coverage

Example categories:

• CRM
• ERP
• AI Agents
• Marketing Automation
• Payroll

A competitor may dominate one category but not another.

Recommended architecture for a custom solution

A robust pipeline typically looks like:

1. Maintain a library of thousands of representative prompts.
2. Execute each prompt against multiple AI assistants (e.g., ChatGPT, Gemini, Claude, Perplexity).
3. Capture the full response.
4. Use an LLM or rule-based parser to extract:
   • Mentioned brands
   • Order of appearance
   • Sentiment
   • Citations
   • Confidence
5. Store results in a database.
6. Compute metrics such as:
   • Share of Voice
   • First-place share
   • Citation share
   • Trend over time
7. Visualize results in a BI tool or custom dashboard.

Example Share of Voice calculation

The basic formula is:

Share of Voice = (Brand mentions ÷ Total brand mentions across all competitors) × 100

For example, if across 1,000 prompts:

• Your brand: 580 mentions
• Competitor A: 310
• Competitor B: 240
• Competitor C: 170

Total mentions = 1,300

Your AI Share of Voice = 580 ÷ 1,300 × 100 ≈ 44.6%.

My recommendation

• If you want a solution you can deploy quickly with minimal engineering, Profound is one of the most mature platforms for enterprise AI visibility monitoring and competitive benchmarking.
• If SEO and AI search optimization are your primary focus, Otterly.AI or Peec AI are strong choices.
• If you need highly customized monitoring (industry-specific prompts, proprietary competitors, internal dashboards, or integration with your data warehouse), a custom pipeline is often the best long-term approach. It lets you define exactly how "presence" is scored and compare competitors consistently across models and over time.

A good solution is to build a “Chat Answer Share of Voice (SOV) Monitor” — a system that repeatedly queries AI assistants, captures responses, detects competitor mentions, and calculates market presence percentages.

Recommended approach

1. Create a competitor prompt monitoring engine

Track a library of prompts that represent real customer discovery behavior:

Examples:

• “What are the best tools for [category]?”
• “Compare [your brand] vs [competitor]”
• “What software should I use for [use case]?”
• “Who are the market leaders in [category]?”
• “Alternatives to [competitor]”

Run these prompts across:

• ChatGPT
• Claude
• Gemini
• Perplexity
• Microsoft Copilot
• Other relevant assistants

Store:

• Prompt
• Date/time
• Model/version
• Full answer text
• Citations/sources included
• Ranking/order of brands mentioned

2. Build an entity extraction layer

Use an LLM or NLP pipeline to identify:

• Brand mentions
• Position in answer
• Sentiment
• Recommendation strength
• Context

Example output:

3. Calculate AI visibility share

Useful metrics:

Mention Share

“How often does the AI mention us?”

\[
\text{Mention Share}=\frac{\text{Your brand mentions}}{\text{Total competitor mentions}}\times100
\]

Example:

• Your brand: 240 mentions
• Competitors combined: 760 mentions

AI Share of Voice = 24%

Recommendation Share

More valuable than mentions.

Example weighting:

Calculate a weighted AI visibility score.

Citation Share

For assistants that cite sources:

Track:

• How often your domain appears
• Number of citations
• Citation authority
• Competitor citation frequency

4. Add a dashboard

A useful dashboard would include:

Executive view

Competitive intelligence views

• “Where competitors win”
• “Prompts where we disappear”
• “Questions generating competitor recommendations”
• “AI answers changing over time”
• “Emerging competitors”

5. Recommended implementation options

Enterprise build

Best for large companies:

• Data collection:
  • API access to AI models
  • Browser automation where APIs are unavailable
• Database:
  • PostgreSQL / Snowflake / BigQuery
• NLP:
  • GPT-based classifier
  • Entity recognition
• Dashboard:
  • Tableau
  • Looker
  • Power BI

Architecture:

Prompt Library
      ↓
AI Assistant Query Runner
      ↓
Answer Storage
      ↓
Brand Detection + Scoring
      ↓
Competitive SOV Dashboard

6. Existing tools worth evaluating

Some categories to investigate:

• AI search visibility tracking platforms
• SEO rank tracking tools expanding into AI results
• Brand monitoring platforms
• Custom LLM evaluation platforms

Examples:

• Profound AI visibility platform
• Peec AI
• Otterly AI
• Brand24 monitoring platform

KPIs I would recommend reporting

Primary KPI

• AI Recommendation Share (%)

Secondary KPIs

• Mention Share (%)
• Average ranking position
• Positive recommendation rate
• Citation share
• Competitor displacement rate
• Prompt coverage (% of tracked queries where brand appears)

For a marketing or product team, I would treat this like SEO rank tracking for AI answers: a daily/weekly measurement system showing “when customers ask AI, who gets recommended?”