ChatGPT answers, unedited

If your goal is to monitor how often competitors appear in AI assistant answers (ChatGPT, Gemini, Claude, Perplexity, etc.) and calculate percentage share, you’re looking for an AI visibility / Generative Engine Optimization (GEO) monitoring platform, not traditional SEO rank tracking. These tools run a set of buyer-relevant prompts, capture AI answers, detect brand mentions, and calculate competitive share of voice. Semrush Search Engine Land

Recommended approach

1. Use an AI answer monitoring platform

Shortlist:

2. Define your measurement framework

I would track these metrics:

AI Share of Voice (primary metric)

Formula:

Competitor AI Share % =
(Number of tracked answers where competitor appears)
/
(Total tracked answers)
× 100

Example:

You monitor 1,000 prompts:

• Your brand appears in 420 answers → 42%
• Competitor A appears in 610 answers → 61%
• Competitor B appears in 280 answers → 28%

Dashboard:

Add weighting so every mention is not equal

A brand appearing as the first recommendation should count more than a passing mention.

Example weighted score:

AI Visibility Score =
Mention frequency × Position weight × Sentiment weight

Possible weights:

• First recommendation: 3x
• Included in shortlist: 2x
• Mentioned casually: 1x
• Negative/inaccurate mention: adjust downward

3. Build the prompt universe

Do not only track your company name. Track the questions buyers actually ask:

Category prompts

• "Best [category] software"
• "Top [category] vendors"
• "Alternatives to [competitor]"
• "Compare [brand A] vs [brand B]"
• "Who are the leaders in [market]?"

Use-case prompts

• "Best tool for enterprise teams"
• "Best option for startups"
• "Most affordable solution"

Geographic prompts

• "Best [service] providers in [region]"

A typical program might monitor:

• 200–1,000 prompts
• Weekly or daily runs
• Multiple AI engines
• 5–15 competitors

4. Track the reasons behind competitor wins

The most useful output is not just:

"Competitor X appears 15% more often."

You want:

• Which prompts they win
• Which sources AI assistants cite
• Which comparison pages influence answers
• Whether they appear earlier in responses
• Whether they are recommended more often

Several AI visibility platforms now include citation/source analysis because AI answers often depend on the sources models retrieve or rely on. MentionScout Prism

Suggested implementation

For an enterprise program:

Data collection

• Profound or Scrunch AI
• Daily/weekly prompt runs
• Capture raw answers + citations

Analytics layer

• Export to:

• Tableau
• Power BI
• Looker

Executive dashboard
Track:

• AI Share of Voice %
• Competitor gap
• Prompt categories won/lost
• Citation share
• First-position recommendation rate
• Trend over time

A practical starting point would be 100–200 high-value prompts, 10 competitors, weekly measurement for 90 days. That gives enough signal to see whether your brand presence in AI answers is improving or declining without creating excessive monitoring noise.

Yes. I’d treat this as an AI answer share-of-voice (SOV) monitoring system, rather than conventional SEO rank tracking. Several current platforms already measure this across ChatGPT, Claude, Gemini, and Perplexity, including per-engine visibility, competitor mentions, citations, and historical trends. ScaleCraft AI Viali AI Seerly

Recommended solution

Build a repeatable prompt → answer → entity extraction → SOV dashboard pipeline.

1. Create a fixed prompt library

Start with roughly 50–100 buyer-intent prompts, grouped into:

• Category discovery: “What are the best X tools?”
• Problem/solution: “How should a company solve X?”
• Comparison: “X vs Y”
• Alternatives: “What are alternatives to X?”
• Use-case: “What tools are best for X use case?”
• Decision-stage: “Which X should a mid-market company choose?”

Avoid mostly branded queries because they artificially inflate your visibility. A structured prompt set is also what current AI-visibility methodologies recommend. Viali AI Sona

2. Run every prompt against each assistant

For example:

Run the same prompts on a recurring schedule and preserve the full answer, timestamp, model/engine, market, and citations.

This matters because the same prompt can produce materially different competitor visibility by engine. Viali AI Foglift

3. Extract competitor presence

For every answer, capture:

• Your brand: mentioned? cited?
• Competitor A: mentioned? cited?
• Competitor B: mentioned? cited?
• Position in answer
• Number of mentions
• Cited domain(s)
• Sentiment/context
• Recommendation vs merely mentioned

I'd keep mention rate and citation rate separate. A brand can be named without its website being cited, which represents a different type of visibility. Kitbase

4. Use two complementary percentages

Presence rate

Brand presence = answers mentioning brand / total answers

Example: if you appear in 62 of 100 relevant answers, your presence rate is 62%.

Competitive SOV

Brand SOV = brand appearances / total tracked brand appearances

Suppose 100 answers produce:

• You: 80 appearances
• Competitor A: 60
• Competitor B: 40
• Competitor C: 20

Then competitive SOV is based on the 200 total tracked appearances, giving you 40% SOV.

Keeping the denominator consistent is critical; changing prompts, competitors, engines, or measurement windows can make SOV comparisons misleading. Foglift

5. Dashboard I would build

The executive view should look roughly like:

Then provide drill-downs for:

• SOV by assistant — ChatGPT vs Claude vs Gemini vs Perplexity
• SOV by topic/prompt cluster
• SOV over time
• Competitor displacement — prompts where competitors appear and you don't
• Citation share — which domains/models are sourcing competitors
• Position share — how often each brand is #1, #2, #3, etc.
• New competitor alerts — previously absent competitors entering answers

This is broadly consistent with how current AI visibility products structure their monitoring. RankZero Visoryn

My preferred architecture

If you're building this internally, I'd use:

Prompt database → scheduled model runs → raw answer store → LLM/entity parser → metrics warehouse → dashboard → alerts

The important design choice is to store every raw answer, rather than just the calculated percentage. That lets you answer why SOV changed—for example, “Competitor A gained 8 points because ChatGPT started citing three new domains.”

I'd also report per-engine SOV before the blended number. A single aggregate can hide major differences between assistants. Viali AI

If you want to buy rather than build

There is now a fairly crowded market of AI-visibility/GEO monitoring tools. Examples include rankzero.io, seerly.app, and foglift.io, all of which specifically expose competitor visibility/share-of-voice functionality. RankZero Seerly Foglift

For a serious competitive-intelligence program, I'd choose based on whether you need raw-answer/API access, geographic/model control, historical data, and custom prompt sets—not simply the headline SOV score.

If you tell me your industry, number of competitors, and whether you want to build or buy, I can lay out a concrete solution—including the data schema, SOV formula, dashboard, and recommended vendors.

For a serious competitive-intelligence program, I'd choose based on whether you need raw-answer/API access, geographic/model control, historical data, and custom prompt sets—not simply the headline SOV score.

If you tell me your industry, number of competitors, and whether you want to build or buy, I can lay out a concrete solution—including the data schema, SOV formula, dashboard, and recommended vendors.

Yes. I’d recommend treating this as an AI Share of Voice / Answer Presence monitoring system rather than simply tracking whether your brand is mentioned.

Current AI-visibility tools and benchmarks generally measure brand mentions, competitor mentions, citations, position, and share of voice across repeated prompts and multiple answer engines. The important methodological point is that AI answers are variable, so you want repeated observations rather than a single query per prompt. Foglift Verity Score

Recommended solution

Build a recurring Competitor Answer Presence Monitor with five layers:

1. Prompt library

• 100–500 buyer-style prompts for your category.
• Group them by intent: discovery, comparison, alternatives, use case, pricing, implementation, etc.
• Include geographic/market variants where relevant.
• Answer collection

• Run each prompt across the assistants that matter to your customers: ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews/AI Mode, etc.
• Run each prompt multiple times per measurement period because answers can change between runs. Verity Score
• Store the complete answer, timestamp, model/engine, prompt, market, and cited URLs.
• Entity extraction

For every answer, identify:

• Your brand: mentioned Y/N
• Each competitor: mentioned Y/N
• Number of mentions
• Position/order in the answer
• Whether the brand was actually recommended vs merely mentioned
• Sentiment/characterization
• Cited sources/domains

Keep "mentioned" separate from "recommended." A brand can appear in an answer while being explicitly criticized; current research specifically cautions that naming frequency isn't the same as recommendation. Cleanlist Synthesis Insights

4. Share calculation

I'd use two complementary metrics:

Answer Presence %
Presencei=answers mentioning brand ieligible answers×100Presence_i=\frac{\text{answers mentioning brand }i}{\text{eligible answers}}\times100

Competitive Share of Voice %
SOVi=brand i mentions∑all tracked-brand mentions×100SOV_i=\frac{\text{brand }i\text{ mentions}}{\sum \text{all tracked-brand mentions}}\times100

The first tells you "How often do we show up?"
The second tells you "How much of the competitive conversation do we own?"

Also calculate these separately by assistant, prompt category, geography, and time period.

5. Competitive dashboard

Your executive view could look like:

Those numbers are illustrative, not market data.

The part I'd prioritize

Don't make the dashboard just a leaderboard. The really useful question is:

"Why did Competitor A appear when we didn't?"

For every competitor win, capture the sources behind the answer. This lets you build a second layer showing:

Prompt → AI answer → competitors mentioned → citations → source domains → missing/weak information about your brand

That matters because recent benchmarks suggest the sources AI systems rely on can be as important as the brand itself; one 2026 benchmark found substantial citation activity coming from publishers and other off-vendor sources. Foglift

My recommended architecture

Prompt DB → Scheduler → AI APIs/browser collection → Answer archive → Entity/competitor extraction → SOV calculation → Dashboard → Alerts

I'd retain the raw answer permanently so that every percentage can be traced back to the underlying response.

For alerts, I'd use things like:

• Competitor enters ≥10% of previously uncovered prompts
• Your presence falls ≥5 percentage points WoW
• Competitor overtakes you on a high-value prompt cluster
• Your average recommendation position deteriorates
• A new domain repeatedly appears as a source for competitors
• Your brand is mentioned but increasingly in a negative/qualified context

Buy vs. build

If this is an internal marketing/GEO capability, I would buy the collection/monitoring layer and build the analysis layer unless you have unusual requirements.

There are already products specifically offering cross-engine visibility and competitor SOV tracking—for example, Depra describes tracking ChatGPT, Gemini, Perplexity and Google AI Overviews, while other platforms focus on broader multi-engine visibility. Depra AI AirOps

The differentiation I'd build internally is the measurement methodology and competitive intelligence, particularly:

• your proprietary prompt set
• your competitor/entity taxonomy
• weighted business-value prompts
• recommendation-vs-mention classification
• source/citation gap analysis
• historical answer archive
• automated explanations of SOV movement

That produces a much more useful KPI than a generic "AI visibility = 37%" number.

If you tell me your industry/category and roughly how many competitors you want to track, I can sketch the exact data model, scoring methodology, dashboard, and recommended tooling for an MVP.

Yes. I’d recommend treating this as an AI-answer competitive intelligence / GEO monitoring problem rather than traditional SEO rank tracking.

Recommended solution

For a serious marketing team, I’d start with tryprofound.com or peec.ai and build your reporting around a fixed prompt universe.

Current AI-visibility platforms can monitor brand mentions, competitors, citations, rankings, and share of voice across ChatGPT and other answer engines. TechnologyAdvice The Rank Masters

The metric I would use

Define AI Share of Voice (SOV) as:

Your brand mentions ÷ total tracked competitor mentions × 100

For example, if across 1,000 relevant AI answers you get 180 mentions, while competitors get 420 + 250 + 150:

• Your brand: 18% SOV
• Competitor A: 42%
• Competitor B: 25%
• Competitor C: 15%

This is preferable to simply counting whether you're mentioned, because it tells you who is winning the recommendation space. Several current platforms use essentially this approach. AnswerMonk Siftly

What I'd monitor

Don't just run generic prompts like "best X software." Build a representative prompt panel:

• Category discovery: "What are the best X tools?"
• Comparison: "X vs Y"
• Problem/solution: "How do I solve [customer problem]?"
• Use case: "Best X for [industry/use case]"
• Buying intent: "What should I consider when buying X?"
• Alternative intent: "Alternatives to [competitor]"
• Brand-specific: "[Your brand] reviews / pricing / alternatives"
• Negative/problem prompts: "What are the drawbacks of X?"

Then run the same prompts repeatedly across ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews/AI Mode, rather than treating one ChatGPT response as representative. AI answers are nondeterministic, so repeated sampling and fixed prompt sets are important. GeoEcho SourceWatch

Dashboard I'd build

Your executive dashboard should have:

The citation gap is particularly actionable: if ChatGPT repeatedly recommends Competitor A because it cites three review sites and comparison articles that never mention you, you have a concrete content/PR target. Current platforms increasingly expose this underlying citation data rather than just the headline score. RankZero Foglift

My preferred architecture

If you want something more sophisticated than buying a dashboard:

Prompt database → AI engine runner → response archive → entity/brand extraction → ranking + sentiment → citation extraction → SOV calculation → dashboard/alerts

I'd store every observation as:

date | engine | prompt | response | brand | mentioned | position | sentiment | cited_source | competitor_set

That gives you an auditable dataset rather than a mysterious "AI visibility score."

One important measurement rule

Keep the denominator fixed.

If you change your prompt set, competitors, engines, or measurement period, your SOV can move even though your actual visibility hasn't. This is a major issue with AI-answer measurement, and current monitoring vendors explicitly emphasize fixed prompt sets and comparable measurement windows. Foglift

So I'd report:

AI SOV = 27.4% (+4.2pp MoM)

rather than just:

AI visibility = 74

That makes the metric much easier to benchmark against competitors and manage like a conventional market-share KPI.

Which solution I'd choose

• Enterprise / serious GEO program: Profound
• Focused AI-search monitoring: Peec AI
• Need a simpler SOV-oriented product: Siftly, AnswerMonk, or SourceWatch
• Want to build proprietary competitive intelligence: API-driven prompt collection + your own warehouse/dashboard

There are now enough purpose-built products that I wouldn't build the entire collection layer from scratch unless your competitive intelligence requirements are unusual. Current market comparisons include Profound, Peec, OtterlyAI, Scrunch, Ahrefs Brand Radar, Semrush and others. TechnologyAdvice

If you tell me your industry + 3–10 competitors + which AI assistants matter, I can design the exact prompt taxonomy, SOV formula, dashboard KPIs, and weekly reporting framework I'd use.

A good solution is to build an AI Answer Share-of-Voice (SOV) monitoring system that continuously samples buyer questions, runs them through major chat assistants, extracts brand mentions, and calculates competitor share. This is often called AI visibility monitoring, LLM visibility tracking, or Generative Engine Optimization (GEO) analytics. Several platforms now provide versions of this capability, including tools that measure mentions, ranking position, sentiment, and competitor comparisons. BrandReply Zumi

Recommended approach

1. Create a prompt intelligence library

Track the questions your customers actually ask AI assistants.

Examples:

• “What are the best CRM platforms for startups?”
• “Compare [your brand] vs [competitor]”
• “Who are the leading vendors for enterprise security?”
• “What software should a company use for [use case]?”

Organize prompts by:

• Buying stage (awareness, evaluation, purchase)
• Product category
• Customer segment
• Geography
• Competitor set

A good initial benchmark is 100–500 prompts.

2. Monitor multiple AI engines

Run the same prompt set across:

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

The goal is not a single answer snapshot because AI responses vary; measure trends over many runs. Some monitoring tools explicitly track multiple engines and report differences by platform. GeoEcho Siftly

3. Extract structured signals from every answer

For each AI response, capture:

4. Calculate AI Share of Voice

A simple formula:

AISOV=Your brand mentionsTotal competitor brand mentions×100AI\ SOV = \frac{\text{Your brand mentions}}{\text{Total competitor brand mentions}} \times 100

Example:

Across 1,000 AI answers:

• Your brand: 180 mentions
• Competitor A: 300 mentions
• Competitor B: 220 mentions
• Competitor C: 100 mentions
• Others: 200 mentions

Your AI SOV:

180/1000=18%180 / 1000 = 18\%

But I would also track weighted SOV:

WeightedSOV=MentionShare×PositionScore×SentimentScoreWeighted\ SOV = Mention\ Share \times Position\ Score \times Sentiment\ Score

Because being the first recommendation is much more valuable than appearing fifth in a list.

Dashboard I would build

Executive view

Competitive battlecard view

Example:

Prompt: “Best enterprise workflow automation tools”

Gap analysis

Identify:

• Prompts where competitors appear and you do not
• Topics where competitors are recommended first
• Sources AI cites for competitors but not you
• New competitors entering answers

Several AI visibility tools now emphasize this type of competitor gap analysis and source/citation tracking. RankZero Visibility.so

Tool options

Buy a platform (fastest)

Examples:

• Profound — enterprise-focused AI visibility monitoring
• quickseo.ai — tracks brand mention share across AI prompts
• brandreply.ai — competitor comparison and sentiment tracking
• geoecho.ai — multi-engine monitoring
• visibility.so — visibility, citations, and competitor gaps

Build internally (more control)

Architecture:

Prompt Database
      |
      v
LLM Query Runner
(ChatGPT API / Claude API / Gemini API)
      |
      v
Answer Parser
(Entity extraction + sentiment + ranking)
      |
      v
Metrics Database
(Postgres / BigQuery)
      |
      v
Dashboard
(Tableau / Looker / PowerBI)

Recommended data model:

AI_Response
- date
- engine
- model_version
- prompt_id
- response_text

Brand_Mention
- response_id
- brand
- position
- sentiment
- recommendation_score

SOV_Report
- date
- brand
- competitor
- mention_share
- weighted_share

Metrics I would report to leadership

1. AI Mention Share

• “How often does AI mention us?”
• AI Recommendation Share

• “How often does AI recommend us as a choice?”
• Competitive Win Rate

• “When we appear with competitors, how often are we ranked higher?”
• Citation Share

• “Which sources influence AI answers?”
• Prompt Ownership

• “Which customer questions do we dominate?”

For most companies, I would start with a 90-day AI visibility benchmark: 200–300 high-intent prompts, 5–10 competitors, weekly tracking, and an SOV dashboard. That produces a much clearer picture than manually asking chat assistants a handful of questions.

1. AI Mention Share

• “How often does AI mention us?”
• AI Recommendation Share

• “How often does AI recommend us as a choice?”
• Competitive Win Rate

• “When we appear with competitors, how often are we ranked higher?”
• Citation Share

• “Which sources influence AI answers?”
• Prompt Ownership

• “Which customer questions do we dominate?”

For most companies, I would start with a 90-day AI visibility benchmark: 200–300 high-intent prompts, 5–10 competitors, weekly tracking, and an SOV dashboard. That produces a much clearer picture than manually asking chat assistants a handful of questions.

• Prompts where competitors appear and you do not
• Topics where competitors are recommended first
• Sources AI cites for competitors but not you
• New competitors entering answers

Several AI visibility tools now emphasize this type of competitor gap analysis and source/citation tracking.

Tool options

Buy a platform (fastest)

Examples:

A good solution is to build an AI Answer Share-of-Voice (SOV) monitoring system that continuously tests the questions your customers ask, records which brands appear in assistant responses, and calculates competitive presence. This category is often called AI visibility monitoring or Generative Engine Optimization (GEO) tracking. Semrush Search Engine Land

Recommended approach

1. Create a competitor prompt universe

Build a library of prompts grouped by buyer intent:

Include:

• 100–1,000 high-value prompts
• Multiple personas
• Regions/languages if relevant
• Different assistant platforms

2. Run prompts on a schedule

For each prompt, capture:

• Assistant used (ChatGPT, Gemini, Claude, Perplexity, etc.)
• Date/time
• Full answer text
• Brands mentioned
• Mention position/ranking
• Sentiment
• Citations/sources used
• Product recommendations

Existing AI visibility platforms follow this model by running controlled prompts and measuring mentions, citations, and competitor gaps. VisiMatrix MentionScout

3. Calculate competitor share of voice

A simple formula:

AIShareofVoice=Competitor mentionsTotal brand mentions across answers×100AI Share\ of\ Voice = \frac{\text{Competitor mentions}}{\text{Total brand mentions across answers}} \times 100

Example:

Also track:

Presence rate

“How often does the brand appear?”

Presence=Answers containing brandTotal answersPresence = \frac{\text{Answers containing brand}}{\text{Total answers}}

Recommendation win rate

“How often does the assistant recommend us first?”

Win Rate = \frac{\text{# answers where brand ranks #1}}{\text{Total relevant answers}}

Citation share

“Which brands are backed by cited sources?”

CitationShare=Brand-linked citationsAll brand citationsCitation Share = \frac{\text{Brand-linked citations}}{\text{All brand citations}}

4. Build a dashboard

Recommended views:

Executive view

• AI visibility score
• Share of voice trend
• Competitor movement
• Lost/won prompts

Marketing view

• Prompts where competitors appear and you do not
• Sources assistants cite most
• Content gaps

Product view

• Feature comparisons
• Incorrect claims/hallucinations
• Competitive positioning

5. Tool options

Buy instead of build

For a marketing team, evaluate:

• Semrush — useful if you already use SEO/content workflows and want AI visibility combined with search data. Semrush
• Profound — enterprise-focused AI answer analytics, competitor tracking, and citation insights. TechnologyAdvice
• Peec AI — focused on AI answer monitoring across major assistants. Search Engine Land
• VisiMatrix — tracks mentions, sentiment, citations, and competitor share of voice across assistants. VisiMatrix

If building internally

A lightweight architecture:

Prompt Database
      |
      v
Scheduler (daily/weekly)
      |
      v
AI Assistant APIs / browser testing
      |
      v
Response Collector
      |
      v
Brand Extraction Model
      |
      v
SOV Calculation Engine
      |
      v
Dashboard (Looker / Tableau / Grafana)

Data model:

Prompt
- prompt_id
- category
- intent

Response
- assistant
- timestamp
- answer_text

BrandMention
- brand
- position
- sentiment
- citation_found
- recommended (yes/no)

My recommendation by company size

• Startup / SMB: use an AI visibility SaaS tool and track 100–300 prompts weekly.
• Mid-market: combine a tool with your own prompt library and BI dashboard.
• Enterprise: build a proprietary benchmark dataset because your competitive prompts and customer language become strategic IP.

The most valuable metric is usually not raw mentions — it is “competitive displacement rate”:

“For the questions where buyers are evaluating us, how often does an AI assistant recommend a competitor instead?”

That directly connects AI presence to lost pipeline risk.

CitationShare=Brand-linked citationsAll brand citationsCitation Share = \frac{\text{Brand-linked citations}}{\text{All brand citations}}

4. Build a dashboard

Recommended views:

Executive view

• AI visibility score
• Share of voice trend
• Competitor movement
• Lost/won prompts

Marketing view

• Prompts where competitors appear and you do not
• Sources assistants cite most
• Content gaps

Product view

• Feature comparisons
• Incorrect claims/hallucinations
• Competitive positioning

5. Tool options

Buy instead of build

For a marketing team, evaluate:

If your goal is specifically “How often does ChatGPT mention/recommend us vs. competitors, and how is that percentage changing?”, I’d treat this as an AI Share of Voice (AI SoV) monitoring problem rather than conventional SEO rank tracking.

My recommendation

Start with a dedicated AI-visibility platform rather than building the measurement stack yourself.

My shortlist:

For your stated use case, I'd start with AnswerMonk if ChatGPT is the priority. Its methodology is particularly aligned with the metric you're asking for: repeatedly run a fixed prompt panel, record which brands appear, and calculate each brand's share of the competitive mentions. www.answermonk.aigeoecho.ai

The metric I would actually put on the dashboard

Don't use just “mentions.”

Track:

1. Presence rate

% of tracked prompts where your brand appears

2. AI Share of Voice

Your brand appearances ÷ total competitor-brand appearances

For example:

genui{"chart":{"chartType":"bar","meta":{"title":"Illustrative AI share of voice","description":"Example competitive mix across a fixed prompt set; replace with measured data."},"xKey":"brand","series":[{"dataKey":"sov","label":"Share of voice","valueSuffix":"%"}],"data":[{"brand":"Your brand","sov":28},{"brand":"Competitor A","sov":25},{"brand":"Competitor B","sov":21},{"brand":"Competitor C","sov":15},{"brand":"Others","sov":11}]}}

3. Recommendation rate

A brand being casually mentioned isn't equivalent to being recommended. I'd separately track:

• Mentioned
• Recommended
• #1 recommendation
• Top-3 position
• Negative/neutral/positive framing

4. Citation share

Which domains/pages are causing the assistant to mention competitors? This is often the most actionable metric because it tells your content team what sources you need to influence. Current AI-visibility products increasingly expose this at the prompt/source level. www.genwolf.aiwww.ansvisor.com

Most important: make the prompt set statistically consistent

I'd create a panel of perhaps 100–500 high-value prompts, organized into:

• Category discovery — “best X software”
• Comparison — “X vs Y”
• Alternatives — “alternatives to X”
• Use case — “best X for [use case]”
• Buyer intent — “X for enterprise”
• Problem-oriented — “how do I solve [problem]?”
• Brand-specific
• Competitor-specific

Then run the same prompts on a fixed cadence.

That matters because AI answers are probabilistic. A single ChatGPT answer can give you a very misleading SoV number. Repeated sampling of the same prompt set gives you a much more defensible trend. This is also how current AI-visibility vendors describe their measurement methodologies. geoecho.ai

The executive dashboard I'd build

I'd reduce the entire system to five numbers:

AI Visibility

42% of tracked conversations

AI Share of Voice

28% — #2 in category

Recommendation Share

24% — +6 pp MoM

Competitor Gap

Competitor A: +11 pp

Citation Share

Your domains: 18% vs competitor A: 31%

Then allow drilling down:

Category → Prompt → AI engine → Answer → Competitors mentioned → Citations → Recommended action

That turns this from a vanity “AI visibility score” into something your marketing/content team can actually act on.

If you're deciding between buying a platform vs. building this internally, I'd lean buy first, build later. The difficult part isn't calculating the percentage; it's reliably sampling assistants, preserving answer history, handling answer variability, identifying entities/competitors, and keeping the methodology consistent.

If your goal is “When buyers ask ChatGPT/other assistants about our category, how often do they mention us vs. competitors?”, I’d treat this as an AI visibility / LLM share-of-voice problem rather than traditional SEO rank tracking.

My recommendation

Start with Profound if this is an enterprise/strategic initiative; start with Peec AI or Otterly if you want a lighter-weight monitoring setup.

The important capability is not merely “does ChatGPT mention my brand?” You want a competitive dataset that captures:

• Prompt-level presence: whether your brand and each competitor appear in each answer.
• Share of voice (SOV): percentage of total brand mentions going to each competitor.
• Recommendation position: who is mentioned first, second, etc.
• Citation/source share: which websites are causing the assistant to recommend a competitor.
• Sentiment/framing: recommended vs. neutral vs. negative.
• Engine/model splits: ChatGPT vs. Gemini vs. Perplexity, etc.
• Trend over time: weekly/monthly SOV and changes after marketing/content initiatives.

Several current platforms explicitly provide competitive SOV rather than just your own visibility. LLM Pulse, for example, calculates SOV as your mentions divided by total mentions across the configured competitor set, and supports filtering by model, country, language, and prompt collection. LLM Pulse

How I'd structure the measurement

Build a fixed prompt universe of perhaps 100–500 buyer questions, divided into categories such as:

1. Category discovery — “What are the best X platforms?”
2. Purchase intent — “What X should a 500-person company use?”
3. Comparisons — “A vs B vs C”
4. Problem/solution — “How can I solve X?”
5. Use-case specific — “Best X for [industry/use case]”
6. Competitor prompts — “Alternatives to Competitor A”
7. Brand prompts — “Is [your company] good for X?”

Then run the same prompt set on a fixed cadence and calculate:

Brand SOV=mentions of brandmentions of all tracked brands×100\text{Brand SOV} = \frac{\text{mentions of brand}} {\text{mentions of all tracked brands}} \times 100

This denominator is important: changing the prompt set or competitor set can artificially move your percentage even if your actual visibility hasn't changed. Foglift

The dashboard I'd want

For example, your executive dashboard could look like:

Then trend SOV by month rather than obsessing over an individual answer, because LLM responses can vary.

Which solution I'd shortlist

• Profound — my choice for a serious enterprise program where you want deep AI visibility analytics and competitive intelligence. Current comparisons describe it as particularly strong for deeper dashboards/governance. Acromatico Loudmink
• Peec AI — strong option if competitive share-of-voice is the central requirement rather than broader enterprise governance.
• Otterly AI — good lower-cost starting point if you primarily need automated monitoring and competitor comparisons. Acromatico
• LLM Pulse — particularly interesting if you want explicit SOV calculations plus segmentation by engine, geography, language, and prompt collection. LLM Pulse
• Build internally — worthwhile if this is going to become a core proprietary measurement system. Store every prompt, response, detected brand, position, sentiment, citation/source, model, timestamp, and geography in a database and calculate the metrics yourself.

One useful market signal: a recent benchmark that tested 13 buyer-intent questions across five AI engines found substantial differences in how frequently competing AI-visibility vendors themselves appeared, illustrating why relative competitor measurement is more useful than a single “AI visibility score.” Foglift

The key metric I'd put in front of leadership

Don't report just:

“Our AI visibility is 62%.”

Report:

“We own 34% of AI recommendations in our category, versus 31% for our largest competitor. We gained 6 percentage points in the last 90 days, primarily from product-comparison prompts.”

That turns AI monitoring from a vanity metric into a competitive market-share metric.

If you tell me your industry + 3–5 competitors + whether this is for ChatGPT only or all major AI assistants, I can design the exact prompt taxonomy, SOV formula, dashboard, and recommended tooling stack.

“Our AI visibility is 62%.”

Report:

“We own 34% of AI recommendations in our category, versus 31% for our largest competitor. We gained 6 percentage points in the last 90 days, primarily from product-comparison prompts.”

That turns AI monitoring from a vanity metric into a competitive market-share metric.

If you tell me your industry + 3–5 competitors + whether this is for ChatGPT only or all major AI assistants, I can design the exact prompt taxonomy, SOV formula, dashboard, and recommended tooling stack.

A good solution is to build an AI Answer Share-of-Voice (SoV) monitoring system that continuously tests the same buyer questions across chat assistants, extracts brand mentions, and benchmarks you against competitors. This is becoming a distinct category often called AI visibility monitoring or GEO (Generative Engine Optimization) tracking. Zumi Unite.AI

Recommended approach

1. Create a competitor prompt library

Build a dataset of 100–1,000 prompts that represent real buying journeys:

Examples:

• “What are the best CRM platforms for mid-market companies?”
• “Compare [Your Brand] vs [Competitor A]”
• “What tools should a SaaS startup use for customer support?”
• “Who are the market leaders in [category]?”

Group prompts by:

• Product category
• Customer segment
• Geography
• Buying stage
• Use case

This matters because AI answers are highly prompt-dependent.

2. Run prompts across major AI assistants

Monitor the same prompts regularly across:

• ChatGPT
• Claude
• Gemini
• Perplexity
• Microsoft Copilot
• Google AI search experiences

Track:

• Whether your brand appears
• Which competitors appear
• Mention order/rank
• Recommendation language
• Sentiment
• Citations/sources used

Several AI visibility platforms now measure these dimensions, including mention rate, position, competitor presence, and citation share. Zumi Siftly

3. Calculate AI Share of Voice

A simple formula:

AIShareofVoice=Your brand mentionsTotal brand mentions across tracked answers×100AI\ Share\ of\ Voice = \frac{\text{Your brand mentions}}{\text{Total brand mentions across tracked answers}} \times 100

Example:

Across 500 tracked answers:

Also calculate:

Mention rate

Answers containing your brandTotal answers\frac{\text{Answers containing your brand}}{\text{Total answers}}

Average position

Example:

• #1 recommendation = 1 point
• #2 = 2 points
• Not mentioned = null

Citation share

Which domains and pages are causing AI models to recommend competitors instead of you.

Tool options

Enterprise platforms

Good for marketing teams that need dashboards and ongoing tracking:

• Profound — enterprise AI visibility monitoring.
• Siftly — tracks AI mentions, competitors, sentiment, and visibility trends. Siftly
• Zumi — focuses on mention rate, share of voice, position, and citation signals. Zumi

Lighter-weight options

• Lettertrace — useful if you want more control and self-hosting. Lettertrace
• Visibility.so — emphasizes prompt-level answers, citations, and competitor comparisons. Visibility.so

Recommended dashboard

A weekly executive dashboard should show:

AI Visibility Scorecard

Competitor battlecard

Opportunity report

Examples:

• “Competitor A appears in 72% of security-related answers; you appear in 31%.”
• “AI models cite competitor review pages 4× more often.”
• “Your product documentation is missing from frequently cited sources.”

If building internally

A simple architecture:

Prompt database
        ↓
AI assistant query runner
        ↓
Response storage
        ↓
Brand/entity extraction
        ↓
Competitor classification
        ↓
SoV calculation engine
        ↓
Dashboard + alerts

Technology stack:

• Database: PostgreSQL
• Scheduler: Airflow / Temporal
• LLM extraction: GPT-based classifier
• Dashboard: Looker / Tableau / Metabase
• Alerts: Slack/email

My recommended starting point

For most companies:

1. Start with 200–500 high-intent prompts
2. Track weekly
3. Measure: - Mention rate
4. AI Share of Voice
5. Recommendation position
6. Citation sources
7. Identify the 20 prompts where competitors beat you
8. Improve the content sources AI systems rely on

The key metric is not simply “does AI mention us?” — it is “when buyers ask AI what to choose, what percentage of the answer space do we own versus competitors?” saidly.ai

Yes. I’d treat this as an AI Answer Share of Voice (SOV) monitoring program rather than conventional SEO rank tracking.

Current tools already support this model: they run a standardized prompt set across ChatGPT and other answer engines, capture responses, identify brand/competitor mentions and citations, and calculate SOV over time. Viali AI DemandSphere

Recommended solution

I’d build a competitive AI visibility dashboard with four layers:

1. Prompt universe

• 100–500 real buyer questions.
• Group by intent: discovery, comparison, recommendation, problem-solving, alternatives, pricing, etc.
• Avoid prompts containing your brand name, because those artificially inflate visibility. Viali AI
• AI answer monitoring

• Run the same prompts against ChatGPT, Claude, Gemini, Perplexity and, where relevant, Google AI Overviews/AI Mode.
• Capture the complete answer, citations/URLs, model/engine, date, geography and prompt.
• Run repeatedly because AI answers are not perfectly deterministic. Verity Score
• Competitive scoring

Track at least:

• Mention rate = % of answers mentioning the brand
• Recommendation rate = % explicitly recommending it
• Citation rate = % of answers citing your domain
• Average position = where you appear in the recommendation set
• Share of Voice = your brand mentions ÷ all tracked competitor mentions
• Competitor gap = your SOV − competitor SOV
• Win/loss rate = prompts where you appear and a competitor doesn't, versus the reverse
• Sentiment/framing = recommended / neutral / negative / caveated

A key point is to keep mention SOV and citation SOV separate. Being named isn't necessarily the same as being supported by a source.

4. Trend + alerting

• Weekly baseline monitoring.
• Daily monitoring around major content/product launches.
• Alert when a competitor's SOV jumps materially, you disappear from an important prompt cluster, or a new competitor starts appearing. Weekly monitoring is also the cadence recommended by current AI-monitoring guidance. RankScope

The dashboard I'd want

Then drill down:

The most valuable view isn't actually the leaderboard. It's the prompt-level competitor gap:

“For which questions is Competitor A consistently appearing while we aren't—and which sources are causing that?”

That connects the monitoring system to an actionable GEO/content program. Current platforms increasingly expose exactly this kind of prompt-level win/loss and citation-gap analysis. MeasureLLM Foglift

Buy vs. build

I'd recommend buying the monitoring infrastructure rather than building the whole thing from scratch unless you have a very large analytics/data-science team.

A few current options worth evaluating:

• tryprofound.com — strong enterprise-oriented AI visibility monitoring.
• demandsphere.com — particularly interesting if you already use traditional SEO/SERP analytics; it combines AI visibility with broader search data. DemandSphere
• modelmention.io — focused on visibility, position, SOV, sentiment and citations across major AI engines. ModelMention
• foglift.io — interesting if auditability matters; it retains the prompt, answer, competitors, citations and measurement window behind the SOV score. Foglift

My preferred architecture

AI engines → standardized prompt runner → raw answer store → entity/brand extraction → citation extraction → SOV calculation → dashboard/alerts → content recommendations

The critical design decision is to store every raw answer. Don't just store "Brand X = 34% SOV." That makes the metric impossible to audit and explains why the number moved.

I'd also define a fixed measurement cohort—same prompts, competitors, engines, geography and time window—because changing the denominator can make SOV rise without actual visibility improving. Foglift

If you're evaluating this for a specific company/category, I can also design the actual SOV methodology, KPI definitions, dashboard schema, and recommended tool/vendor shortlist for your use case.

Yes. I’d treat this as an AI answer visibility / share-of-voice monitoring problem rather than traditional SEO rank tracking.

Several current platforms already do this by running a standardized set of buyer prompts against ChatGPT, Gemini, Claude, Perplexity, and other answer engines, then measuring mentions, recommendations, position, citations, and competitor share. OnPagePilot Zumi

Recommended solution

Build a recurring “AI Competitive Share of Voice” monitor around a fixed prompt library.

1. Create a representative prompt set

Organize ~100–500 prompts into intent groups:

• Category: “What are the best [category] tools?”
• Comparison: “[Your brand] vs [competitor]”
• Recommendation: “What should I use for [job to be done]?”
• Problem/solution: “How can I solve [customer problem]?”
• Use case: “Best [category] for [industry/use case]”
• Alternative: “What are alternatives to [competitor]?”
• Buying: “Which [category] is best for a [company size]?”

Keep the prompt set stable over time. This is critical because changing the denominator can artificially change your share percentage. Foglift

2. Run every prompt across the major assistants

At minimum:

• ChatGPT
• Gemini
• Claude
• Perplexity
• Google AI Overviews / AI Mode

Some existing platforms already support this cross-engine approach. OnPagePilot Zumi

Run the same prompts on a recurring schedule—daily for high-value categories or weekly for most businesses.

3. Capture the answer, not just a score

For every prompt × assistant × run, store:

This evidence layer is important because you want to know why a competitor is winning, not merely that they're winning. Current tools increasingly expose the underlying cited URLs and sources for this reason. Apify Zumi

The core percentage

I'd report two related metrics.

Mention share of voice

SOVi=answers mentioning brand itotal brand mentions across tracked answers×100SOV_i = \frac{\text{answers mentioning brand }i} {\text{total brand mentions across tracked answers}} \times 100

For example, if your 500 tracked answers contain:

• Your brand: 180 mentions
• Competitor A: 240
• Competitor B: 150
• Competitor C: 90

then the competitive SOV is calculated from those mentions.

But I'd also track:

Answer penetration

Penetrationi=answers mentioning brand itotal eligible answers×100Penetration_i = \frac{\text{answers mentioning brand }i} {\text{total eligible answers}} \times100

This prevents an important distinction from getting lost. A brand can have high SOV among answers where someone is mentioned while still being absent from many buyer questions.

I'd also add first-place rate:

FirstPlace_i = \frac{\text{answers where brand is the #1 recommendation}} {\text{answers where any tracked competitor is recommended}} \times100

That's arguably the most actionable metric for competitive monitoring.

Dashboard I'd recommend

I'd have five headline KPIs:

1. Your AI visibility: 28%
2. #1 competitor: 25%
3. Competitive gap: +3 pts
4. First-place rate: 31%
5. Citation share: 22%

Then drill down by assistant → intent → prompt → competitor → cited source.

Buy vs. build

If you want something operational quickly, I'd shortlist existing AI-visibility platforms rather than building the model collection infrastructure yourself.

• OnPagePilot — particularly relevant if you want daily share-of-voice history and competitor/citation comparisons across seven AI surfaces. OnPagePilot
• Zumi — strong fit for a marketing dashboard because it explicitly exposes mention rate, SOV, average position, competitors, and citation sources. Zumi Zumi
• Chatobserver — interesting if stakeholder reporting is important; it emphasizes recurring reports and competitive visibility. Chatobserver
• RankZero — worth evaluating if competitor gap monitoring and alerts are the priority. RankZero
• Apify's LLM Visibility Tracker — attractive if you want to build your own pipeline/dashboard because it exposes prompt-level evidence and can be incorporated into an automated workflow. Apify

My recommendation

For a serious program, I'd buy the data collection layer and build your own reporting/analytics layer.

The architecture would be:

Prompt library → AI assistants → answer capture → entity/competitor extraction → SOV calculation → citation/source analysis → dashboard → alerts

The key strategic insight is to avoid reporting a single meaningless “AI visibility score.” The useful question is:

“For the questions our customers actually ask, how often does AI recommend us versus each competitor, on which assistants, and what sources are causing the difference?”

That gives marketing/content/PR teams a concrete action list rather than another vanity metric.

My recommendation

For a serious program, I'd buy the data collection layer and build your own reporting/analytics layer.

The architecture would be:

Prompt library → AI assistants → answer capture → entity/competitor extraction → SOV calculation → citation/source analysis → dashboard → alerts

The key strategic insight is to avoid reporting a single meaningless “AI visibility score.” The useful question is:

“For the questions our customers actually ask, how often does AI recommend us versus each competitor, on which assistants, and what sources are causing the difference?”

That gives marketing/content/PR teams a concrete action list rather than another vanity metric.

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

There are now tools specifically designed for this: for example, llmmonitor.io tracks mentions and competitor share across ChatGPT, Gemini, and Claude, while citedspy.com tracks multiple assistants and reports share of voice, sentiment, citations, and trends. CitedSpy LLMMonitor

My recommended approach

1. Build a fixed “buyer prompt” panel

Create ~50–200 prompts that represent how customers actually use AI assistants:

• Category: “What are the best [category] tools?”
• Recommendation: “What would you recommend for [use case]?”
• Comparison: “Compare A, B, and C.”
• Alternative: “What are alternatives to A?”
• Problem-oriented: “How should a [customer type] solve X?”
• Purchase intent: “What should I look for when choosing X?”
• Brand-specific: “What is A best at?”
• Competitor-specific: “Is A better than B?”

Keep this prompt set stable. Otherwise your percentage can change simply because you changed the questions.

2. Query several assistants

At minimum I'd monitor:

• ChatGPT
• Gemini
• Claude
• Perplexity
• Google AI Overviews / AI Mode

This matters because different engines can produce substantially different recommendations and citations. Clear Cited RankScope

Run each prompt multiple times because AI answers aren't deterministic. A useful methodology is to report ranges/confidence intervals rather than treating one run as definitive. Verity Score Clear Cited

3. Calculate competitive share of voice

I'd use two complementary metrics.

Mention rate

MentionRatebrand=answers mentioning brandtotal answers×100Mention\ Rate_{brand} = \frac{\text{answers mentioning brand}} {\text{total answers}} \times100

Competitive AI Share of Voice

SOVbrand=brand mentionsall tracked competitor/brand mentions×100SOV_{brand} = \frac{\text{brand mentions}} {\text{all tracked competitor/brand mentions}} \times100

The second metric answers the more useful question: “When AI recommends companies in our category, how much of the recommendation space do we own?”

For example:

One important nuance: don't mix the denominators. Keep the same prompt set, competitors, engines, geography, and measurement period when comparing percentages. Foglift

4. Don't stop at “mentioned”

I'd score every appearance:

• 0 — absent
• 1 — mentioned
• 2 — listed/recommended
• 3 — strongly recommended
• 4 — recommended first / clear winner

Then calculate a Weighted AI Visibility Score.

That prevents a brand that gets casually mentioned 50 times from looking better than a competitor that gets recommended first 30 times.

Also capture:

• Position in answer
• Sentiment/framing
• Whether the assistant recommends you
• Competitors appearing alongside you
• Citations/URLs
• Which domains are being cited
• Which competitors are cited but you aren't
• Changes versus previous measurement

Citation ownership is particularly valuable because it tells you why a competitor is appearing. Current AI-visibility platforms increasingly expose this layer rather than only giving a headline SOV number. AI Visibility Software Ansvisor

5. Turn it into an executive dashboard

I'd make the top-level dashboard look roughly like:

AI Market Visibility — September 2026

Then trend SOV weekly:

What I'd actually buy

For a marketing/GEO team that wants an off-the-shelf solution, I'd shortlist:

• LLMMonitor — simpler monitoring/benchmarking approach across ChatGPT, Gemini and Claude. LLMMonitor
• CitedSpy — broader engine coverage and explicit competitor SOV, sentiment and citation tracking. CitedSpy
• Profound / enterprise-grade platforms — worth evaluating if you need large-scale prompt monitoring, reporting and governance. Current 2026 comparisons put Profound among the stronger enterprise options. AI Visibility Software
• Build your own measurement layer — best if this is strategically important and you want complete control over prompts, sampling, scoring and historical data.

My preference

If this is for a serious competitive-intelligence program, I would not rely on a single vendor's proprietary “AI visibility score.” Use a standardized prompt corpus and store the raw AI answers yourself.

The architecture I'd recommend is:

Prompt library → multi-LLM querying → raw answer archive → entity/competitor extraction → citation extraction → scoring → SOV calculation → historical database → dashboard/alerts

That gives you an auditable answer to questions like:

“Competitor X gained 8 percentage points of AI share this month. Which 17 prompts caused the change, which assistants changed, and which sources are driving their appearances?”

That's considerably more actionable than simply knowing that your “AI visibility score” went from 62 to 67.

Prompt library → multi-LLM querying → raw answer archive → entity/competitor extraction → citation extraction → scoring → SOV calculation → historical database → dashboard/alerts

That gives you an auditable answer to questions like:

“Competitor X gained 8 percentage points of AI share this month. Which 17 prompts caused the change, which assistants changed, and which sources are driving their appearances?”

That's considerably more actionable than simply knowing that your “AI visibility score” went from 62 to 67.

Yes. I’d treat this as an AI-answer “share of voice” monitoring system, rather than traditional SEO rank tracking. The key is to repeatedly run a fixed panel of buyer questions through the assistants your customers use, capture the answers, identify every competitor mentioned, and calculate both presence and relative share. This is increasingly how AI-visibility platforms measure competitive visibility. Answer Engines Optimization Cituna

Recommended solution

1. Build a representative prompt panel

Create ~100–500 prompts organized into categories:

• Category: “What are the best [category] solutions?”
• Comparison: “[Your brand] vs [competitor]”
• Alternative: “What are alternatives to [competitor]?”
• Problem/need: “How can I solve [customer problem]?”
• Use case: “Best [category] for [specific use case]”
• Buying: “What should I consider when choosing [category]?”
• Recommendation: “Which [category] vendors would you recommend?”

Keep the panel fixed over time so week-over-week changes are meaningful.

2. Monitor multiple assistants

At minimum:

• ChatGPT
• Google AI Overviews / AI Mode
• Gemini
• Claude
• Perplexity

Measure each separately because an assistant can have a very different competitive landscape from another. AnswerMonk Cituna

3. Store the complete answer, not just a score

For every prompt/run, capture:

This is important because being mentioned isn't equivalent to being recommended. A competitor could be mentioned 60% of the time but recommended first much more often.

4. Use three complementary metrics

I wouldn't use a single “share” percentage.

Presence rate

% of answers mentioning your brand

For example:
You: 42% vs Competitor A: 61%

Share of voice

Your brand mentions ÷ total competitor mentions × 100

For example:

Weighted recommendation share

Give more weight to prominence:

• #1 recommendation = 5 points
• #2 = 4
• #3 = 3
• Mentioned but not ranked = 1
• Not present = 0

This prevents a brand buried in a long answer from looking equivalent to the brand the assistant recommends first.

5. Add the really valuable layer: “Why are we losing?”

The dashboard should let you click:

Competitor A has 34% share vs our 28%

and see:

• Prompts where Competitor A appears and you don't
• Prompts where both appear but A ranks higher
• Sources/citations supporting A
• Publications/reviews that mention A
• Product attributes the assistant associates with A
• Specific wording the assistant uses to recommend A
• Changes since the previous measurement

That turns monitoring into an actionable competitive intelligence system, rather than another dashboard.

A particularly useful metric is the citation gap: sources that appear in competitor answers but not yours. Current AI-visibility products are increasingly using this approach. AI Visibility Software RankZero

Build vs. buy

If this is for a marketing/SEO team, I'd buy rather than build the entire infrastructure unless you have unusual requirements.

Current products worth evaluating include:

• tryprofound.com — enterprise-oriented AI visibility/answer monitoring.
• otterly.ai — AI search visibility and competitor tracking.
• peec.ai — AI visibility analytics and competitive monitoring.
• answermonk.ai — particularly focused on AI share of voice and ChatGPT monitoring. AnswerMonk
• visibility.so — combines share of voice, historical trends, prompt exploration, sources and competitor monitoring. Visibility.so

There are also newer lower-cost products, so I wouldn't automatically jump to an enterprise platform. AI Visibility Software PromptScout

My recommended architecture

If you're building this internally, I'd use:

Prompt database → assistant/API execution → answer archive → entity/competitor extraction → recommendation/position classifier → citation extraction → metrics warehouse → dashboard + alerts

Then have a weekly report like:

AI Competitive Visibility — Week of Sep 4

Your share: 28% ↑ 3.2pp
Competitor A: 34% ↑ 5.1pp
Competitor B: 22% ↓ 1.4pp

Biggest threat: Competitor A gained 11pp on “best [category] for enterprise” prompts.

Why: 7 newly detected third-party sources now cited by ChatGPT.

Opportunity: 23 prompts where competitors are cited but your site isn't.

Priority: Create/update content addressing those 23 gaps.

The important design decision: don't define “share” simply as percentage of answers mentioning us. Track presence → prominence → recommendation → citation, separately. That gives executives one simple competitive percentage while giving the marketing team enough evidence to actually improve it. Answer Engines Optimization Cituna

If you tell me your industry + 3–5 competitors + which assistants matter, I can also lay out a concrete prompt taxonomy, scoring formula, dashboard schema, and recommended vendor/build approach for your use case.

• Prompts where Competitor A appears and you don't
• Prompts where both appear but A ranks higher
• Sources/citations supporting A
• Publications/reviews that mention A
• Product attributes the assistant associates with A
• Specific wording the assistant uses to recommend A
• Changes since the previous measurement

That turns monitoring into an actionable competitive intelligence system, rather than another dashboard.

A particularly useful metric is the citation gap: sources that appear in competitor answers but not yours. Current AI-visibility products are increasingly using this approach. AI Visibility Software RankZero

Build vs. buy

If this is for a marketing/SEO team, I'd buy rather than build the entire infrastructure unless you have unusual requirements.

Current products worth evaluating include:

• tryprofound.com — enterprise-oriented AI visibility/answer monitoring.
• otterly.ai — AI search visibility and competitor tracking.
• peec.ai — AI visibility analytics and competitive monitoring.
• answermonk.ai — particularly focused on AI share of voice and ChatGPT monitoring. AnswerMonk
• visibility.so — combines share of voice, historical trends, prompt exploration, sources and competitor monitoring. Visibility.so

There are also newer lower-cost products, so I wouldn't automatically jump to an enterprise platform. AI Visibility Software PromptScout

My recommended architecture

If you're building this internally, I'd use:

Prompt database → assistant/API execution → answer archive → entity/competitor extraction → recommendation/position classifier → citation extraction → metrics warehouse → dashboard + alerts

Then have a weekly report like:

AI Competitive Visibility — Week of Sep 4

Your share: 28% ↑ 3.2pp
Competitor A: 34% ↑ 5.1pp
Competitor B: 22% ↓ 1.4pp

Biggest threat: Competitor A gained 11pp on “best [category] for enterprise” prompts.

Why: 7 newly detected third-party sources now cited by ChatGPT.

Opportunity: 23 prompts where competitors are cited but your site isn't.

Priority: Create/update content addressing those 23 gaps.

The important design decision: don't define “share” simply as percentage of answers mentioning us. Track presence → prominence → recommendation → citation, separately. That gives executives one simple competitive percentage while giving the marketing team enough evidence to actually improve it. Answer Engines Optimization Cituna

If you tell me your industry + 3–5 competitors + which assistants matter, I can also lay out a concrete prompt taxonomy, scoring formula, dashboard schema, and recommended vendor/build approach for your use case.

Yes. I’d treat this as an AI answer “share of voice” monitoring problem rather than traditional SEO rank tracking.

Recommended solution

For most teams, I’d start with Saidly as the monitoring layer. It specifically tracks how often your brand and competitors are mentioned across ChatGPT, Claude, Gemini, and Grok, and provides the underlying answers, sentiment, themes, and cited sources. Saidly

If you need broader enterprise coverage, I’d also evaluate Profound or Otterly; current 2026 benchmarks show these among the more visible tools in the AI-search monitoring category. Foglift AI Visibility Software

The measurement framework I recommend

Build a fixed panel of roughly 100–500 real customer prompts, organized by intent:

• Discovery: “What are the best X tools?”
• Comparison: “X vs Y”
• Problem/solution: “How can I solve X?”
• Category: “Best vendors for X”
• Use case: “What should a company like [persona] use for X?”
• Purchase: “Which X product should I choose?”

Run those prompts on a recurring schedule across the AI assistants that matter to your customers.

For every answer, capture:

A basic SOV calculation is:

Your AI Share of Voice = Your brand mentions ÷ all tracked competitor mentions × 100

The important part is keeping the same prompt set, competitors, models, geography, and measurement window when comparing periods. Otherwise, your percentage can move simply because the sample changed. Citations.io Foglift

What the executive dashboard should look like

I'd make the primary KPI something like:

Then drill down:

Overall → AI platform → prompt category → individual prompt → answer → cited source

That last level is particularly important. A dashboard saying “Competitor A has 34% SOV” tells you what happened; the actual answers and citations tell your content/marketing team why it happened.

The metric I'd use as the north star

I wouldn't use raw mention share alone. I'd create a Weighted AI Visibility Share:

Visibility score = mention × position × sentiment × citation

For example, being recommended first with a positive description and a citation should count considerably more than being mentioned fifth in passing.

I'd report both:

1. Raw SOV — easy for executives to understand.
2. Weighted SOV — better measure of actual competitive presence.

Also report SOV by prompt intent and platform, because an overall number can hide important differences. Current AI-visibility methodologies increasingly recommend per-engine and per-prompt-category reporting for exactly this reason. AnyEO AnswerMonk

My suggested stack

Fastest to implement:
Saidly → fixed prompt library → weekly monitoring → dashboard → alerts when competitor SOV jumps.

Enterprise:
Profound/Otterly → centralized prompt taxonomy → multiple AI engines → API/data warehouse → BI dashboard → automated alerts.

Build it yourself:
AI APIs/browser automation → prompt runner → answer/brand/entity extraction → citation extraction → warehouse → Looker/Tableau/Power BI.

The key is not to build a dashboard that merely counts brand mentions. Build a system that can answer:

“For which questions are competitors winning the AI recommendation, why are they winning, and what evidence are the models using?”

That turns competitor monitoring into an actionable AI-search/GEO intelligence system, rather than another vanity visibility metric. siftly.ai organikpi.com

Fastest to implement:
Saidly → fixed prompt library → weekly monitoring → dashboard → alerts when competitor SOV jumps.

Enterprise:
Profound/Otterly → centralized prompt taxonomy → multiple AI engines → API/data warehouse → BI dashboard → automated alerts.

Build it yourself:
AI APIs/browser automation → prompt runner → answer/brand/entity extraction → citation extraction → warehouse → Looker/Tableau/Power BI.

The key is not to build a dashboard that merely counts brand mentions. Build a system that can answer:

“For which questions are competitors winning the AI recommendation, why are they winning, and what evidence are the models using?”

That turns competitor monitoring into an actionable AI-search/GEO intelligence system, rather than another vanity visibility metric. siftly.ai organikpi.com

Yes. I’d recommend treating this as an AI-answer share-of-voice (SoV) monitoring system, rather than simply a “brand mention tracker.”

The key is to monitor the same buyer prompts across assistants, capture the full answers, identify every competitor mentioned/cited, and calculate relative share over time. This is increasingly how GEO/AEO tools frame the problem. Answer Engines Optimization HubSpot Blog

Recommended solution

Build or buy a prompt-based AI visibility monitoring layer with four components:

The metric I'd make primary

Use:

AI Share of Voice = your brand mentions ÷ total tracked competitor mentions × 100

But don't report that percentage alone. Pair it with mention/coverage rate.

For example, if you have 30% SoV but only appear in 10% of prompts, that's very different from 30% SoV with 70% prompt coverage. Several current measurement frameworks explicitly distinguish presence, citation share, prominence, and competitive SoV. GitHub Cituna

I'd therefore have five core KPIs:

• Mention rate: % of prompts where you appear.
• Share of voice: your mentions relative to competitors.
• Recommendation rate: % where you're actually recommended/shortlisted.
• Citation share: % of citations going to your domains.
• Competitive gap: your SoV minus the leader's SoV.

Most important feature: “Why did we lose?”

The dashboard shouldn't stop at percentages.

For every competitor win, show:

Prompt: “Best CRM for a 200-person SaaS company?”
Winner: Competitor A
Your brand: Not mentioned
Sources cited: G2, competitor comparison page, analyst article
Pattern: Competitor A appears in 4/5 relevant third-party sources
Opportunity: Build/earn coverage on those sources + strengthen comparison content

That turns the product from a measurement dashboard into an optimization system.

Current AI-visibility products are already moving in this direction—tracking the exact answer and sources behind the competitive percentage rather than treating SoV as a black-box score. seGEO Visibility.so

Buy vs. build

If you want this for internal marketing intelligence, I'd initially buy rather than build the entire infrastructure.

Some current products worth evaluating include:

• segeo.io — focused on monitoring brand/competitor visibility across ChatGPT, Perplexity, Gemini and Google AI Overviews. seGEO
• visibility.so — emphasizes prompt-level answers, competitor SoV, historical trends and citation sources. Visibility.so
• answermonk.ai — particularly interesting if the primary requirement is ChatGPT-focused SoV measurement and repeatable prompt panels. AnswerMonk
• foglift.io — emphasizes keeping the underlying prompt, answer, competitors and citations behind the SoV metric. Foglift

My recommendation: if this is a strategic capability you're planning to operationalize across a large organization, I'd build a thin internal data/analytics layer on top of a monitoring provider rather than building the assistant-query infrastructure yourself.

The internal layer should own your prompt taxonomy, competitor definitions, scoring methodology, historical database, dashboards, alerts and business outcomes.

That gives you a defensible metric such as:

“Our target-market AI Share of Voice is 24%, versus 31% for the category leader, down 3 percentage points this month.”

And, crucially:

“We lost 8 points on enterprise prompts because Competitor A is being cited by 3 sources we don't currently appear in.”

That second statement is where the real business value is.

Yes. I’d treat this as an AI/LLM Share-of-Voice (SoV) monitoring problem, rather than conventional SEO rank tracking.

Recommended solution

For a marketing team, my first choice would be peec.ai if you want a relatively straightforward dashboard for competitor benchmarking. It explicitly tracks:

• Visibility: % of AI answers mentioning your brand
• Share of Voice: your brand mentions as a percentage of total tracked brand mentions
• Position: where your brand appears in the answer
• Sentiment
• Competitor comparisons
• Prompt-level performance
• Sources/citations influencing AI answers
• Coverage across ChatGPT, Gemini, Perplexity and other AI search surfaces Peec AI Peec AI

A strong enterprise alternative is tryprofound.com, particularly if you need deeper competitive analysis and reporting. It also provides visibility, competitor SoV, average position and trends over time. Profound

How I would measure competitor presence

Don't use one generic "AI visibility" score. Build a fixed prompt universe representing real customer questions, for example:

• "Best [category] software"
• "[Your product] alternatives"
• "[Competitor] alternatives"
• "Best [category] for enterprise"
• "Compare A vs B"
• "What should I use for [use case]?"
• "[Category] companies with [feature]"
• "Is A better than B?"

Then run the same prompts regularly across the AI assistants that matter to you.

For each prompt/assistant combination, capture:

The key distinction is visibility vs. share of voice. For example, if you appear in 40% of answers, that doesn't tell you much if competitors collectively appear in 90% of them. SoV gives you the competitive context. Peec defines SoV based on your brand mentions relative to total tracked brand mentions. Peec AI

The dashboard I'd build

I'd make the executive view something like:

AI SHARE OF VOICE — SEPTEMBER 2026

Brand              SoV       Δ MoM      Avg Position
-----------------------------------------------------
Your Brand          28%       +4 pp          #2
Competitor A        34%       -2 pp           #1
Competitor B        21%       +1 pp           #3
Competitor C        17%       -3 pp           #4

Then break it down by AI assistant × customer intent:

                         ChatGPT   Gemini   Perplexity
-------------------------------------------------------
Your Brand                 31%       24%       29%
Competitor A               36%       41%       32%
Competitor B               18%       21%       24%
Competitor C               15%       14%       15%

And most importantly, have a "competitor wins" view:

127 prompts where Competitor A appears but we don't
83 prompts where Competitor B is recommended instead of us
41 high-value prompts where neither brand is currently present

That turns monitoring into an actionable GEO/AEO opportunity list, rather than just another marketing dashboard.

One important improvement

I'd also track citation share, separately from mention share.

A competitor can be mentioned frequently because the model knows the brand, while another competitor can be cited as the authoritative source. Those are very different competitive positions. Current AI-visibility platforms increasingly expose the underlying sources/citations for precisely this reason. Peec AI Peec AI

My shortlist

1. Peec AI — best starting point for straightforward AI visibility + competitor SoV.
2. Profound — stronger choice if this becomes an enterprise-grade competitive intelligence program.
3. Build your own tracker — worthwhile if you need proprietary prompts, custom weighting, internal BI integration, or precise control over the SoV methodology.

If this is for a specific company/category, give me the company and 3–5 competitors and I can design the prompt set, SoV formula, dashboard structure, and weekly reporting framework I would use.

The key distinction is visibility vs. share of voice. For example, if you appear in 40% of answers, that doesn't tell you much if competitors collectively appear in 90% of them. SoV gives you the competitive context. Peec defines SoV based on your brand mentions relative to total tracked brand mentions. Peec AI

The dashboard I'd build

I'd make the executive view something like:

AI SHARE OF VOICE — SEPTEMBER 2026

Brand              SoV       Δ MoM      Avg Position
-----------------------------------------------------
Your Brand          28%       +4 pp          #2
Competitor A        34%       -2 pp           #1
Competitor B        21%       +1 pp           #3
Competitor C        17%       -3 pp           #4

Then break it down by AI assistant × customer intent:

                         ChatGPT   Gemini   Perplexity
-------------------------------------------------------
Your Brand                 31%       24%       29%
Competitor A               36%       41%       32%
Competitor B               18%       21%       24%
Competitor C               15%       14%       15%

And most importantly, have a "competitor wins" view:

127 prompts where Competitor A appears but we don't
83 prompts where Competitor B is recommended instead of us
41 high-value prompts where neither brand is currently present

That turns monitoring into an actionable GEO/AEO opportunity list, rather than just another marketing dashboard.

One important improvement

I'd also track citation share, separately from mention share.

A competitor can be mentioned frequently because the model knows the brand, while another competitor can be cited as the authoritative source. Those are very different competitive positions. Current AI-visibility platforms increasingly expose the underlying sources/citations for precisely this reason.

My shortlist

Yes. I’d recommend treating this as an AI answer visibility / Share of Voice (SoV) monitoring program, rather than traditional SEO rank tracking.

Several tools now specifically track brand mentions, competitor mentions, position, citations, and SoV across ChatGPT and other AI engines. For example, LLM Pulse calculates SoV from mentions across the same tracked prompts, while Visibility.so and Scout LLM provide prompt-level and competitor visibility tracking. LLM Pulse Visibility.so Scout LLM

Recommended solution

Build a standardized prompt panel + automated answer collector + competitor scoring dashboard.

1. Create a fixed prompt set

Start with 100–200 high-value prompts, divided into categories:

• Category discovery: “What are the best [category] solutions?”
• Buying intent: “What should I use for [job to be done]?”
• Comparison: “[Your brand] vs [competitor]”
• Alternatives: “What are alternatives to [competitor]?”
• Recommendations: “Which [product/service] would you recommend for [persona]?”
• Problem-based: “How can I solve [customer problem]?”
• Industry-specific: “Best [category] for [industry/use case]”

The important part is to keep the prompt set stable so changes in SoV reflect changes in AI answers rather than changes in your measurement methodology. A 50–100 query test suite is also a commonly recommended starting point. Sona LLM Pulse

2. Run every prompt across the major assistants

At minimum:

• ChatGPT
• Claude
• Gemini
• Perplexity
• Google AI Overviews / AI Mode, where relevant

Don't combine them into one opaque score initially. Keep an individual score for each engine, because the answers and source-selection behavior differ substantially between platforms. LLM Pulse Scout LLM

3. Capture the actual answer

For every prompt/run, store:

This raw-answer layer is critical. You want to be able to go from “our SoV fell 6 points” → “these 17 prompts changed → competitor X replaced us → these sources were cited.”

4. Use three complementary metrics

I wouldn't use percentage share alone.

A. Mention Share of Voice

The simplest metric:

SoV = your brand mentions ÷ total tracked brand mentions × 100

For example:

• Your brand: 120 mentions
• Competitor A: 180
• Competitor B: 100
• Competitor C: 60
• Total: 460

Your SoV = 26.1%.

This is essentially how current AI visibility platforms define mention-based SoV. LLM Pulse Asked Thrice

B. Answer Visibility

Measure:

% of answers containing your brand

If you appear in 64 of 100 prompts, your visibility is 64%.

This is different from SoV. You could appear in lots of answers but still have a small share because competitors are mentioned repeatedly. Asked Thrice LLM Pulse

C. Weighted AI Share

I'd make this your executive metric.

Give more weight to prominent recommendations:

• #1 recommendation = 1.0
• #2 = 0.8
• #3 = 0.6
• Mentioned but not ranked = 0.3
• Negative mention = 0 or negative adjustment

That prevents this problem:

Competitor A is mentioned 100 times, but usually as the fifth option.
You are mentioned 80 times and are usually the first recommendation.

A raw SoV calculation could incorrectly conclude that Competitor A is winning.

Position-weighted visibility is already becoming a feature of commercial AI visibility products. LLM Pulse Scout LLM

5. Your executive dashboard

I'd make the top-level dashboard look something like:

Then trend it weekly:

6. The most valuable report isn't the percentage

I'd add a Competitor Gap report:

Where competitors are winning that you aren't

For every prompt, classify:

• 🟢 You win — you're recommended first
• 🟡 Competitive — you're mentioned but not preferred
• 🔴 Competitor wins — competitor is recommended and you're absent
• ⚫ Category gap — nobody is mentioned
• 🔵 Citation gap — you're mentioned, but competitor owns the supporting sources

Then aggregate this by topic.

For example:

Competitor A is taking 18% of your potential AI visibility because:

• 12 prompts recommend A but not you
• 7 prompts cite A's content
• 5 comparison prompts position A above you
• 3 prompts contain outdated information about your product

That turns monitoring into an actionable GEO/AEO program, rather than another analytics dashboard.

Buy vs. build

For most teams, I'd buy rather than build the entire infrastructure.

A few current options worth evaluating:

• llmpulse.ai — particularly relevant if competitive SoV is the primary requirement; it supports model, locale and prompt-level breakdowns. LLM Pulse
• visibility.so — useful if you want prompt-level visibility, competitor tracking and citation/source analysis. Visibility.so
• scoutllm.ai — emphasizes visibility, SoV, average position and per-engine tracking. Scout LLM
• apify.com — interesting if you want a more programmable/custom pipeline; its LLM Visibility Tracker already extracts mentions, rank, SoV, citations, competitors and sentiment. Apify

My preferred architecture

Prompt database → scheduled LLM runs → answer parser → brand/competitor entity extraction → position/sentiment/citation scoring → warehouse → dashboard → alerts

And I'd make the core KPI hierarchy:

AI Visibility → Share of Voice → Weighted Share → Position → Citation Share → Competitor Gap

That gives marketing leadership a single percentage to track, while giving the GEO/SEO/content team enough underlying evidence to explain why that percentage moved.

One important methodological point: don't calculate SoV from a single run of each prompt. AI answers are nondeterministic, so repeated runs are needed to distinguish a real competitive shift from answer-to-answer randomness. Asked Thrice

My preferred architecture

Prompt database → scheduled LLM runs → answer parser → brand/competitor entity extraction → position/sentiment/citation scoring → warehouse → dashboard → alerts

And I'd make the core KPI hierarchy:

AI Visibility → Share of Voice → Weighted Share → Position → Citation Share → Competitor Gap

That gives marketing leadership a single percentage to track, while giving the GEO/SEO/content team enough underlying evidence to explain why that percentage moved.

One important methodological point: don't calculate SoV from a single run of each prompt. AI answers are nondeterministic, so repeated runs are needed to distinguish a real competitive shift from answer-to-answer randomness. Asked Thrice

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

The market is already converging on this approach: platforms such as HubSpot AEO, Searchpanel, AnswerMonk, and others measure brand mentions/recommendations across a fixed set of prompts and compare them with competitors. HubSpot Searchpanel AnswerMonk

Recommended solution

Build or buy a monitoring layer that repeatedly runs a controlled prompt set against the major assistants, captures the full answers, extracts competitors, and calculates share of voice.

1. Create a fixed “buyer prompt” library

Start with perhaps 100–500 prompts representing how customers actually use assistants:

• “What are the best [category] tools?”
• “Compare [your brand] vs [competitor]”
• “What should I use for [use case]?”
• “Best alternatives to [competitor]”
• “Who are the leading providers of [category]?”
• “[category] for enterprise”
• “[category] for small businesses”

Group them by intent, product, industry, funnel stage, and geography.

The important thing is to keep the prompt panel relatively stable. That lets you distinguish a real change in AI visibility from simply changing the questions being asked. This fixed-panel approach is also used by current AI-visibility products. AnswerMonk Sona

2. Monitor multiple answer engines

At minimum:

• ChatGPT
• Gemini
• Perplexity
• Claude
• Google AI Overviews/AI Mode, where available

Don't combine them immediately into one number. Keep an engine-level view, because a brand can perform very differently across assistants. Saidly Seerly

3. Capture more than “mentioned / not mentioned”

For every response, store something like:

This matters because a brand merely appearing in an answer isn't equivalent to being recommended first. Current AEO measurement frameworks increasingly distinguish mentions, citations, recommendations, and supporting evidence. Answer Engines Optimization

4. Calculate competitor share of voice

I'd make this your primary KPI:

AI Share of Voice = Your brand mentions ÷ Total tracked brand mentions × 100

For example, suppose 1,000 analyzed answers contain 2,500 total competitor mentions:

• You: 500 → 20% SOV
• Competitor A: 750 → 30%
• Competitor B: 625 → 25%
• Competitor C: 375 → 15%
• Others: 250 → 10%

This is preferable to simply saying “we appeared in 50% of answers,” because it tells you whether competitors are taking more of the recommendation space. Citations HubSpot

5. Add the metrics that explain the SOV

I'd use a dashboard with five headline metrics:

AI Visibility

% of prompts where your brand appears

Share of Voice

% of total competitor mentions belonging to you

Recommendation Share

% of answers where you are actually recommended

Average Position

Average position when you appear in a ranked/list answer

Citation Share

% of relevant cited sources/domains associated with you

Then add the most useful competitive diagnostic:

“Where is Competitor X winning that we aren't?”

For example:

That turns monitoring into an actionable competitive intelligence system, rather than another vanity dashboard.

6. Most important feature: citation-gap analysis

When a competitor appears and you don't, identify what information the assistant relied on.

For example:

Competitor wins 63% of “best enterprise CRM” prompts.

Then:

• 38% of winning answers cite G2
• 27% cite competitor comparison pages
• 19% cite analyst/review sites
• 11% cite Reddit
• 5% other

That tells your marketing team why the competitor is winning and where to focus. Citation/source analysis is becoming a core component of current AI-visibility platforms. Searchpanel RankZero

Buy vs. build

If you want something running quickly: I'd evaluate HubSpot AEO, Searchpanel, AnswerMonk, Sona, or similar dedicated AEO/GEO platforms. HubSpot, for example, currently combines prompt tracking, competitor SOV, citation analysis, and recommendations across ChatGPT, Gemini, and Perplexity. HubSpot

If this is strategically important or you have unusual requirements: build your own measurement layer. The core architecture is relatively straightforward:

Prompt library → AI engines → Response capture → Entity extraction → Position/recommendation classifier → Citation extraction → SOV database → Dashboard/alerts

The hard part isn't calculating the percentage. The hard part is creating a statistically defensible, repeatable measurement methodology.

My preferred KPI hierarchy

I'd report this to executives as:

1. AI Share of Voice: 24% → 31% ↑
2. Recommendation Share: 18% → 27% ↑
3. #1 Recommendation Rate: 9% → 16% ↑
4. Competitor gaining fastest: Competitor B +8 pp
5. Biggest lost topic: “Enterprise solutions” −14 pp

And allow the user to drill all the way down to:

Prompt → exact AI answer → competitor → citation → source/content gap.

That gives you a much more useful answer to “Are we losing ground to competitors in AI assistants, and why?” than a generic AI visibility score.

Yes. I’d treat this as AI/LLM Share of Voice (SoV) monitoring rather than traditional SEO rank tracking.

Recommended solution

For a serious competitor-monitoring program, I’d shortlist Profound, LLM Pulse, or a similar dedicated AI-visibility platform rather than building the collection layer yourself. Current tools can track the same prompt set repeatedly, identify brand/competitor mentions, capture citations, and calculate SoV. LLM Pulse SE Ranking

The measurement framework I’d use is:

1. Build a fixed prompt universe

• 100–500 prompts representing your buyers' questions.
• Split into discovery, comparison, category, problem/solution, and high-intent prompts.
• Keep the exact prompt set stable so the denominator doesn't move.
• Run prompts against ChatGPT

• Ideally track other assistants too, but keep ChatGPT as its own score.
• Record the complete answer, model, date, locale, and whether browsing/citations were used.
• Extract competitors

For every answer, capture:

• Your brand mentioned? Yes/No
• Competitor(s) mentioned
• Position/order in the answer
• Positive/neutral/negative framing
• Citation/link to your site
• Citation/link to competitor
• Which third-party sources are driving the recommendation
• Calculate two different percentages

Mention rate

Answers mentioning brand ÷ total relevant answers × 100

Competitive Share of Voice

Brand mentions ÷ all tracked competitor-brand mentions × 100

Keeping these separate is important. A brand can have a 30% mention rate but only 10% SoV if competitors are being mentioned much more frequently. Current AI-visibility platforms use essentially this distinction. LLM Pulse Foglift

5. Add position weighting

I would not treat a #1 recommendation and a #7 mention equally. For example:

• #1 recommendation = 1.0
• #2 = 0.8
• #3 = 0.6
• #4–5 = 0.4
• Other mention = 0.2

Then report both raw SoV and weighted SoV.

The dashboard I'd want

I would make the core dashboard show:

Then drill down to “where are we losing?”:

Prompt: “Best [category] for enterprise companies”
ChatGPT: Competitor A, Competitor B, Your Brand
Loss: Competitor A appears ahead of you
Source gap: Competitor A is repeatedly supported by 3 industry publications that don't mention you.

That last layer is particularly valuable because it turns a percentage into an actionable marketing/SEO/GEO task. AI-visibility platforms increasingly expose the underlying prompts and cited URLs for exactly this reason. SE Ranking Visibility.so

My recommendation

If you're buying: start by evaluating Profound + LLM Pulse + one lower-cost alternative against a sample of your actual prompts. LLM Pulse, for example, explicitly supports competitor SoV, model/locale/prompt segmentation, and historical tracking. LLM Pulse

If you're building: use an LLM/API collection layer + structured response parser + warehouse + dashboard. The key isn't the dashboard; it's maintaining a statistically consistent prompt panel and storing the raw answers so every percentage can be audited.

One important caveat: don't report a single “AI visibility %” without showing the denominator. Changes in prompts, competitors, models, geography, or measurement windows can make SoV move without actual competitive improvement. Foglift

If you tell me your industry, approximate number of competitors, and whether you want a SaaS tool or an in-house solution, I can lay out a concrete architecture and recommend the best 2–3 options.

Yes. I’d treat this as an AI/LLM Share of Voice monitoring problem rather than conventional SEO rank tracking.

Several platforms now specifically measure brand presence, competitor mentions, position, and share of voice across AI answers. For example, Semrush describes SOV as the percentage of AI answers mentioning your brand versus competitors, while other platforms track prompt-level results and historical changes. Semrush Scrunch

Recommended solution

I’d build or buy a system with this pipeline:

1. Define a fixed prompt universe

Create 100–1,000 prompts representing actual customer questions, grouped by:

• Category discovery — “What are the best X platforms?”
• Comparison — “X vs Y”
• Recommendation — “Which X should I choose?”
• Use case — “Best X for a mid-market company”
• Pricing/value — “Most affordable X”
• Problem/solution — “How do I solve X?”
• Brand-specific prompts

Keep the core panel constant over time so week-over-week changes are meaningful. Prompt-level monitoring is particularly important because an aggregate SOV number can hide the questions where a competitor is beating you. GrackerAI Scrunch

2. Run every prompt repeatedly

Query the relevant assistants—at minimum ChatGPT, Gemini, Claude, Perplexity and Google AI results—and store the complete response, timestamp, model/engine, country, language and search mode where applicable.

This matters because LLM responses are non-deterministic. A single answer shouldn't be treated as a definitive measurement; repeated sampling and smoothing produce a much more reliable benchmark. Semrush GrackerAI

3. Extract structured competitor data

For every response, extract:

Then calculate three separate metrics rather than collapsing everything into one score:

Mention rate

% of answers containing your brand

420 / 1,000 answers = 42% mention rate

This answers: “Does the assistant know/include us?”

Share of voice

For each prompt/answer, count the competitive brands mentioned and allocate the answer's competitive visibility among them.

For example, if 100 answers contain:

• Your brand: 60
• Competitor A: 45
• Competitor B: 30
• Competitor C: 15

Your raw mention share is:

60 / (60 + 45 + 30 + 15) = 40%

This is preferable to simply saying “we appeared in 60% of answers,” because it tells you how much of the competitive conversation you actually own. LLM Visibility AnswerMonk

Position / recommendation share

Track whether you're #1, #2, #3, etc.

I'd particularly report:

• % of answers where you're #1
• Average position
• % of answers where competitor is #1
• Win rate in head-to-head prompts

A brand mentioned 50% of the time but ranked #5 is strategically very different from one mentioned 50% of the time and ranked #1. Zumi Scout LLM

The dashboard I'd recommend

Your executive dashboard should show:

• AI visibility: 48%
• AI share of voice: 38%
• #1 recommendation rate: 24%
• Average position: 2.7
• Change vs previous period: +6 pp
• Largest competitor: Competitor A, 27%
• Prompts lost to competitors: 83
• New competitor appearances: 4

Then drill down:

Engine → Topic → Prompt → Answer → Competitors → Citations

That last step is crucial. You don't just want to know “Competitor A gained 8 points.” You want to know which questions they gained it on and which sources caused the change. Current AI-visibility products increasingly expose exactly this prompt/source-level analysis. Visibility.so GrackerAI

Buy vs. build

If you want something operational quickly, I'd shortlist:

• semrush.com — good choice if you're already invested in the Semrush ecosystem; its published methodology explicitly covers visibility and SOV. Semrush
• visibility.so — particularly aligned with your requirement: SOV, competitor monitoring, prompt-level answers, historical trends and source rankings. Visibility.so
• scrunch.com — useful if you want deeper prompt/topic/citation segmentation. Scrunch
• gracker.ai — especially focused on competitive prompt-by-prompt monitoring and emerging competitors. GrackerAI

My recommendation: if this is for a serious marketing/competitive-intelligence program, don't optimize around a single “AI visibility score.” Build the measurement around fixed buyer prompts + repeated sampling + mention rate + competitive SOV + position + exact answer/citation evidence. That gives you a defensible percentage-share metric and, importantly, tells the team why that percentage moved.

If you're evaluating this as a product to build internally, I can also lay out the exact data model, SOV formula, sampling methodology, dashboard, and architecture for an MVP.

60 / (60 + 45 + 30 + 15) = 40%

This is preferable to simply saying “we appeared in 60% of answers,” because it tells you how much of the competitive conversation you actually own.

Position / recommendation share

Track whether you're #1, #2, #3, etc.

I'd particularly report:

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

My recommendation: Profound

tryprofound.com is probably the strongest fit if your main question is:

“When prospects ask ChatGPT/other AI assistants about our category, how often are we mentioned compared with competitors?”

Profound’s Answer Engine Insights specifically tracks visibility, competitor rankings, citations, mention position, and share of voice across ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, Claude, Copilot and other answer engines. Profound Profound

It defines SOV essentially as:

Competitor SOV = brand mentions ÷ total brand mentions across the tracked answers × 100

So, for example:

I would monitor 5 metrics

Don't rely on SOV alone. I'd build the dashboard around:

1. Mention rate — % of prompts where each brand appears.
2. Share of voice — your mentions as a percentage of all competitor mentions.
3. Mention position — whether you're the first recommendation, second, fifth, etc.
4. Citation share — how often the AI actually cites your domain versus competitors.
5. Competitive gap — prompts where a competitor appears but you don't.

That last metric is particularly valuable because it turns the dashboard from “we're losing” into “here are the 47 prompts where we're losing.” Profound supports prompt-level competitive analysis and identifies the prompts driving competitor citations. Profound

How I'd set up the monitoring program

Create a representative prompt universe of roughly 100–500 prompts, divided into categories such as:

• Category discovery — “best X for…”
• Problem/solution — “how do I…”
• Product comparison — “X vs Y”
• Alternatives — “alternatives to X”
• Buying intent — “best X for a large enterprise”
• Brand-specific — “Is [your brand] good for…?”
• Competitor-specific — “Is [competitor] better than…?”

Then run the same prompt set continuously across the major assistants.

I'd report:

Overall SOV → SOV by AI platform → SOV by topic → SOV by prompt → competitor gaps → citations driving those answers.

This is important because AI answers are volatile: a brand can have 35% overall SOV while being nearly invisible for a strategically important topic.

One important distinction

I'd keep “answer presence” and “citation presence” separate.

For example:

ChatGPT mentions your company in 40% of relevant answers, but cites your website in only 8%.

That tells you something very different from:

ChatGPT mentions you in 8% and cites you in 8%.

The first suggests you have brand awareness but weak source authority; the second suggests you're simply not entering the consideration set.

Profound explicitly provides both visibility/SOV and citation-share metrics. Profound Knowledge Base Profound Knowledge Base

Alternatives

If you want to evaluate vendors rather than immediately choosing one, I'd shortlist:

• Profound — best fit for deep competitive benchmarking and prompt-level analysis.
• Similarweb — worth considering if you want AI visibility connected to broader competitive intelligence and traffic/revenue data.
• Sona — interesting if you want a lighter-weight approach; its methodology recommends a 50–100-query test suite for SOV benchmarking. Sona
• Peec AI / Otterly — worth benchmarking if you want more specialized AI-visibility monitoring.

The market is moving quickly, so I'd run a 2–4 week bake-off using your own prompt set rather than choosing solely from feature lists.

One other reason I'd prioritize this now: recent research from Profound found a correlation between ChatGPT share of voice and which brands users ultimately chose in product-comparison tasks, although the relationship varies substantially by category. Profound

If you tell me your industry + 3–5 competitors + which assistants matter (ChatGPT only vs. ChatGPT/Perplexity/Gemini/etc.), I can design the exact SOV measurement framework, prompt taxonomy, dashboard KPIs, and scoring formula I'd use.

That last metric is particularly valuable because it turns the dashboard from “we're losing” into “here are the 47 prompts where we're losing.” Profound supports prompt-level competitive analysis and identifies the prompts driving competitor citations. Profound

How I'd set up the monitoring program

Create a representative prompt universe of roughly 100–500 prompts, divided into categories such as:

• Category discovery — “best X for…”
• Problem/solution — “how do I…”
• Product comparison — “X vs Y”
• Alternatives — “alternatives to X”
• Buying intent — “best X for a large enterprise”
• Brand-specific — “Is [your brand] good for…?”
• Competitor-specific — “Is [competitor] better than…?”

Then run the same prompt set continuously across the major assistants.

I'd report:

Overall SOV → SOV by AI platform → SOV by topic → SOV by prompt → competitor gaps → citations driving those answers.

This is important because AI answers are volatile: a brand can have 35% overall SOV while being nearly invisible for a strategically important topic.

One important distinction

I'd keep “answer presence” and “citation presence” separate.

For example:

ChatGPT mentions your company in 40% of relevant answers, but cites your website in only 8%.

That tells you something very different from:

ChatGPT mentions you in 8% and cites you in 8%.

The first suggests you have brand awareness but weak source authority; the second suggests you're simply not entering the consideration set.

Profound explicitly provides both visibility/SOV and citation-share metrics. Profound Knowledge Base Profound Knowledge Base

Alternatives

If you want to evaluate vendors rather than immediately choosing one, I'd shortlist:

• Profound — best fit for deep competitive benchmarking and prompt-level analysis.
• Similarweb — worth considering if you want AI visibility connected to broader competitive intelligence and traffic/revenue data.
• Sona — interesting if you want a lighter-weight approach; its methodology recommends a 50–100-query test suite for SOV benchmarking. Sona
• Peec AI / Otterly — worth benchmarking if you want more specialized AI-visibility monitoring.

The market is moving quickly, so I'd run a 2–4 week bake-off using your own prompt set rather than choosing solely from feature lists.

One other reason I'd prioritize this now: recent research from Profound found a correlation between ChatGPT share of voice and which brands users ultimately chose in product-comparison tasks, although the relationship varies substantially by category. Profound

If you tell me your industry + 3–5 competitors + which assistants matter (ChatGPT only vs. ChatGPT/Perplexity/Gemini/etc.), I can design the exact SOV measurement framework, prompt taxonomy, dashboard KPIs, and scoring formula I'd use.

A good solution is to build an AI Answer Visibility Monitoring system (sometimes called AI visibility tracking, AEO/GEO monitoring) that treats chat assistants like a new search channel. The goal is not just “does the assistant mention us?” but how often competitors capture recommendation share across buyer-intent prompts. Several tools now specialize in this category and track mentions, citations, and competitor share of voice across assistants. Sightivo HubSpot

Recommended approach

1. Create a competitor prompt benchmark set

Build a library of 100–500 prompts that represent real customer questions:

Discovery

• “What are the best [category] tools?”
• “Who are the leaders in [category]?”

Comparison

• “Compare Brand A vs Brand B”
• “Alternatives to Brand A”

Purchase intent

• “What should a mid-market company use for [problem]?”
• “Which vendor has the best pricing/features/support?”

Use-case specific

• “Best [category] for healthcare”
• “Best [category] for startups”

Refresh prompts quarterly as customer language changes.

2. Monitor multiple assistants

Track answers from:

• ChatGPT
• Gemini
• Claude
• Perplexity
• Microsoft Copilot
• Google AI Overviews / AI Mode (where applicable)

Different assistants can recommend different competitors, so measuring only one model can give a misleading picture. Tools in this space commonly compare visibility across multiple engines. ucited.ai Zumi

3. Calculate “AI Share of Voice”

A practical metric:

AI Share of Voice=Your brand mentionsTotal tracked brand mentions×100\text{AI Share of Voice} = \frac{\text{Your brand mentions}}{\text{Total tracked brand mentions}} \times 100

Example:

This tells you who owns the recommendation space.

4. Add weighted scoring

Raw mentions are not enough. Weight answers by importance:

This creates a more realistic AI Market Presence Score.

5. Track the “why” behind competitor wins

For every competitor appearance, capture:

• Which prompt triggered it
• Which sources the AI cited
• Which pages/reviews/community sites influenced the answer
• Position in answer
• Sentiment/context

The most actionable insight is usually:

“Competitor X appears in 65% of answers because AI systems repeatedly cite these 12 third-party sources that do not mention us.”

Citation analysis is a common feature in current AI visibility platforms. MentionScout AI Rank Tracker

Tool options

Enterprise / marketing teams

• HubSpot AI Visibility — useful if you already use HubSpot marketing workflows and want AI visibility integrated with broader marketing analytics. HubSpot
• Profound — aimed at larger organizations needing AI search intelligence. AI Visibility Software

Dedicated AI visibility tracking

• Sightivo — focuses on tracking assistant mentions, cited pages, and competitor recommendations. Sightivo
• Zumi — tracks brand mentions, share of voice, and citation sources across AI engines. Zumi
• MentionScout — emphasizes competitor visibility, answer gaps, and citation analysis. MentionScout

Suggested dashboard

A weekly executive dashboard:

AI Presence

• Overall AI Share of Voice %
• Trend vs previous month
• Visibility by assistant

Competitive Position

• Your rank vs competitors
• Prompts where competitors win
• Lost recommendation opportunities

Source Intelligence

• Top cited domains
• Missing citations
• Review sites/community sources influencing answers

Action Queue

• Create/update comparison pages
• Improve third-party reviews
• Publish missing use-case content
• Build citations from authoritative sources

For most companies, I would start with 200–300 high-intent prompts, 5–10 competitors, weekly tracking, and a Share of Voice dashboard. That gives a reliable baseline before investing in larger-scale automation.

• HubSpot AI Visibility — useful if you already use HubSpot marketing workflows and want AI visibility integrated with broader marketing analytics.
• Profound — aimed at larger organizations needing AI search intelligence.

Dedicated AI visibility tracking

Yes. I’d treat this as an AI-answer Share of Voice (SoV) monitoring problem rather than traditional SEO rank tracking.

My recommendation: start with Profound

tryprofound.com is probably the strongest fit if your goal is specifically “How often does ChatGPT mention us vs. competitors, and how is that changing?”

It tracks prompts across ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode and other answer engines, and provides visibility, Share of Voice, competitor rankings, citations, sentiment, position, and prompt-level analysis. Profound Profound

The key metrics I'd put on an executive dashboard are:

Profound explicitly defines SoV around the frequency of your brand mentions relative to competitors and provides rankings/charts for it. Profound Knowledge Base Profound Knowledge Base

A simple way I'd calculate the headline number

Suppose you run 1,000 relevant prompts and get:

• Your company: 240 mentions
• Competitor A: 300
• Competitor B: 180
• Competitor C: 120
• Others: 160

Your AI Share of Voice = 240 / 1,000 = 24% under a mention-based methodology.

I'd actually maintain two separate metrics, though:

Visibility:
answers mentioning you / total answers

Competitive SoV:
your mentions / all tracked brand mentions

That's important because a brand can have high visibility while still losing competitive share. Peec's documentation makes the same distinction between visibility and SoV. Peec.ai Docs

Profound vs. Peec AI

peec.ai is the other product I'd seriously evaluate. It has particularly clear dashboards for competitor visibility, SoV, position and sentiment, with filtering by model, country, topic and prompt. Peec AI Peec.ai Docs

I'd choose:

• Profound → best if this becomes a serious enterprise competitive-intelligence/AEO program, especially if you care about citations and discovering unexpected competitors.
• Peec AI → attractive if you primarily want a straightforward brand vs. competitor visibility/SoV dashboard.
• Build your own → worthwhile if you have unusual prompt taxonomies, need proprietary scoring, or want to integrate the data into an existing BI/marketing stack.

The dashboard I'd build

Then drill down:

1. Overall SoV — current % and week/month change.
2. SoV by AI engine — ChatGPT vs Gemini vs Perplexity, etc.
3. SoV by topic — product comparisons, “best X,” alternatives, pricing, use cases, etc.
4. Prompt-level battles — “Competitor A appears 78%, we appear 21%.”
5. Citation gap — sites/pages cited for competitors but not you.
6. Trend — SoV over the last 30/90 days.
7. Answer quality — position + sentiment, not just presence.

The last two are important: a 25% SoV isn't necessarily better than 20% if the competitor is consistently recommended #1 and you're mentioned as an afterthought.

One important implementation detail

Don't just track a handful of manually invented prompts. Build a representative prompt universe covering your category, buying funnel, use cases, competitors, alternatives and high-intent questions, then run the same corpus repeatedly. Profound runs tracked prompts daily, while Peec also supports prompt-level and model-level analysis. Peec.ai Docs Profound

If this is for a marketing/brand team, my short answer is: pilot Profound and Peec side-by-side for 2–4 weeks, using the same 100–500 prompts, and compare the resulting SoV numbers before committing. The methodology matters as much as the dashboard.

Yes. I’d recommend treating this as an AI Share of Voice (SOV) / answer visibility monitoring problem rather than traditional SEO rank tracking.

The market is moving toward exactly this model: repeatedly running a fixed set of buyer prompts through ChatGPT, Gemini, Perplexity, Claude, etc., capturing the answers, extracting competitor mentions/citations, and calculating each brand’s share. Spawned AgentGEO

Recommended solution

For an enterprise-grade solution, I’d shortlist Zumi, Profound, and Scrunch AI; for a lighter-weight implementation, build the measurement layer yourself.

zumihq.com is particularly aligned with your requirement because it tracks prompts across multiple AI engines, competitor share of voice, citations, and changes over time. Zumi

The important part is not simply buying a "brand mention" monitor. You want a system that captures the actual assistant answer and lets you distinguish:

• Mention share — how often your brand is named.
• Recommendation share — how often you're actually recommended.
• Citation share — how often your site/content is cited.
• Position/prominence — whether you're first recommendation vs. buried in a list.
• Competitor displacement — prompts where a competitor appears but you don't.
• Sentiment/claim — what the assistant says about each brand.
• Source overlap — which publications/pages are causing competitors to appear.

That distinction matters: AI SOV isn't quite analogous to a Google ranking position; a brand can be mentioned, recommended, or merely cited as supporting evidence. Answer Engines Optimization

The metric I'd use

For every fixed prompt set, calculate:

Brand SOV = brand mentions ÷ total tracked-brand mentions

For example:

I'd actually maintain two related metrics:

1. Answer presence: % of prompts where the brand appears.
2. Competitive SOV: brand mentions / all tracked-brand mentions.

This prevents a misleading result where everyone becomes more visible but your relative position doesn't improve. The industry is increasingly using fixed prompt panels precisely so changes can be compared over time. AnswerMonk Citations

How I'd implement the monitoring program

Create a panel of roughly 50–150 high-value prompts, divided into:

• Category discovery — "best X for..."
• Problem/solution — "how do I solve X?"
• Comparison — "X vs Y"
• Alternatives — "alternatives to Competitor A"
• Buying intent — "best X for enterprise"
• Use-case prompts — "X for [industry/use case]"
• Brand prompts — "Is [your brand] good for X?"

Run the same panel weekly across your priority AI assistants. Multiple runs are useful because AI answers aren't deterministic; recent measurement guidance recommends repeated runs rather than treating one answer as definitive. OrganiKPI Verity Score

Store the raw answer, not just the resulting percentage. That makes every SOV number auditable back to the actual response. AgentGEO

The dashboard I'd want

Then drill down to:

"Where are we losing?"

17 prompts where Competitor A appears and you don't
11 prompts where Competitor B is recommended first
8 prompts where competitors are cited from sources you aren't present in

That last layer is what turns monitoring into an actionable GEO/AEO program rather than another dashboard.

My recommendation

If you're evaluating vendors, I'd start with Zumi + Profound + Scrunch AI and run the same 30–50 prompt benchmark through each before purchasing. If you have engineering resources and need maximum transparency, an API-based approach is even better: fetch the raw answers yourself and calculate SOV deterministically. AgentGEO, for example, explicitly advocates this auditable model. AgentGEO

If you tell me your industry and 3–5 competitors, I can design the exact prompt taxonomy, SOV formula, dashboard KPIs, and vendor shortlist I'd use for your situation.

My recommendation

If you're evaluating vendors, I'd start with Zumi + Profound + Scrunch AI and run the same 30–50 prompt benchmark through each before purchasing. If you have engineering resources and need maximum transparency, an API-based approach is even better: fetch the raw answers yourself and calculate SOV deterministically. AgentGEO, for example, explicitly advocates this auditable model. AgentGEO

If you tell me your industry and 3–5 competitors, I can design the exact prompt taxonomy, SOV formula, dashboard KPIs, and vendor shortlist I'd use for your situation.

If your goal is specifically “How often does an AI assistant recommend us vs. competitors?”, I’d use an AI-search visibility / GEO monitoring platform rather than trying to build this from standard SEO tooling.

My recommendation: evaluate 3 approaches

There are several platforms now explicitly offering this. For example, Zumi tracks brand mentions, competitor share of voice, answer position, and citations across multiple AI engines. www.zumihq.com Seerly similarly runs live prompts across ChatGPT, Perplexity, Claude and others and breaks visibility down by engine and competitor. seerly.app

The metric I'd standardize on

Don't simply count “mentions.” I'd create an AI Share of Voice (SOV) metric:

AI SOV = competitor/brand appearances ÷ total competitor/brand appearances

For example, suppose you run 500 relevant prompts:

• Your brand: 180 appearances
• Competitor A: 150
• Competitor B: 100
• Competitor C: 70

Then your AI SOV is 36%.

I'd actually maintain three related metrics:

1. Mention rate — % of prompts where the brand appears.
2. Share of voice — your presence relative to competitors.
3. Recommendation position — average position when mentioned.

That distinction matters: being mentioned fourth in a list of five isn't equivalent to being the assistant's first recommendation. Some platforms already expose these separately. www.zumihq.comwww.answermonk.ai

Make the monitoring prompt-based

I'd build a fixed panel of perhaps 100–300 prompts, divided into:

• Discovery: “What are the best X tools?”
• Problem: “How can I solve X?”
• Comparison: “X vs Y”
• Category: “Best alternatives to X”
• Purchase: “What should a company use for X?”
• Use-case/persona: questions specific to your target customers.

Then run the same panel on ChatGPT, Gemini, Claude, Perplexity and Google AI Overviews on a recurring basis. A 50–100 query panel is also the methodology recommended by some current AI-visibility monitoring approaches. www.sona.com

The dashboard I'd want

genui{"chart":{"chartType":"bar","meta":{"title":"Illustrative AI share of voice","description":"Example competitive visibility across AI assistant answers; replace with your measured data."},"xKey":"company","series":[{"dataKey":"sov","label":"AI share of voice","valueSuffix":"%"}],"data":[{"company":"Your brand","sov":36},{"company":"Competitor A","sov":30},{"company":"Competitor B","sov":20},{"company":"Competitor C","sov":14}]}}

And underneath:

Overall

• Your SOV: 36%
• Category leader: 30%
• Gap to leader: +6 pts
• Mention rate: 48%
• Avg. recommendation position: 1.9

By assistant

• ChatGPT: 42%
• Perplexity: 31%
• Claude: 38%
• Gemini: 35%

By intent

• Discovery: 44%
• Comparison: 27%
• Purchase: 39%
• Alternatives: 18%

Competitive alerts

Competitor A gained 7 percentage points in ChatGPT over the last 30 days.

Competitor B is now appearing in 63% of comparison prompts where your brand appears in only 29%.

That's much more actionable than a generic “AI visibility score.”

One important caveat

Treat SOV as a leading visibility metric, not revenue or traffic. AI answers can change substantially with prompt wording, model updates, personalization and retrieval. Recent reporting has shown how quickly citation/visibility patterns can move—for example, Axios reported a substantial decline in Reddit's share of ChatGPT citations over a few weeks. www.axios.com

So I'd keep the prompt set fixed, store the actual answers and citations, and report changes over time rather than relying on one-off queries.

If I were implementing this for a company today, I'd shortlist Zumi, Seerly, and AnswerMonk, run the same 100–200 prompts through each, and choose based on raw-answer access, competitor SOV methodology, engine coverage, and API/export capabilities. AnswerMonk is particularly interesting if you want a straightforward SOV-oriented workflow; it explicitly calculates competitor share from recorded AI answers and reports it per engine. www.answermonk.ai

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 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:

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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: