ChatGPT answers, unedited

If you're monitoring multiple client brands across industries, I’d treat this as an agency-level AI visibility measurement problem, not simply a ChatGPT keyword tracker.

My recommendation: use Otterly as the monitoring layer

otterly.ai is the first platform I’d pilot for this use case. It is specifically built around recurring prompts and reports brand mentions, competitive visibility, citations, and share of voice across ChatGPT and other AI engines. Its current product supports multiple brands/projects and lets you segment prompts by intent and geography. Otterly

For an agency, I would build the operating model like this:

The important part: build a standardized prompt framework

Don't let each account manager invent prompts ad hoc. Create a master taxonomy such as:

• Category discovery: "What are the best [category] companies?"
• Best-of: "What are the best [product/service] for [audience]?"
• Comparison: "[Brand A] vs [Brand B]"
• Problem/solution: "What's the best way to solve [problem]?"
• Purchase intent: "Where should I buy [product]?"
• Local: "Who provides [service] in [location]?"
• Attribute-specific: "Which [category] has the best [attribute]?"
• Negative/concern: "What are the drawbacks of [category/brand]?"

Then assign perhaps 50–200 prompts per client, depending on account importance.

The key metric I'd report is:

AI Share of Voice = brand mentions ÷ total relevant brand mentions across the tracked prompt set

I'd keep mention share, citation share, and recommendation/position as separate metrics rather than collapsing everything into one opaque "AI visibility score."

For a larger agency, consider Peec or Profound too

peec.ai is worth testing if your priority is client-level competitive analytics and citation analysis. Current industry comparisons describe it as particularly suited to agencies/multi-project setups. Geodocs Conbersa

tryprofound.com becomes more interesting when clients are enterprise accounts and you need deeper analytics, governance, and broader AI-search monitoring. Current comparisons position it toward the enterprise end of the market. Mentn TechnologyAdvice

What I'd actually implement

Phase 1 — Pilot

Run Otterly + your own reporting layer for 3–5 clients representing very different industries.

For each client:

1. Define 50–100 high-value prompts.
2. Establish 5–10 competitors.
3. Run them on a consistent schedule.
4. Store raw AI responses, not just scores.
5. Calculate SOV by:

• industry/category
• intent
• prompt
• competitor
• geography
• AI engine
• Track cited URLs/domains.
• Flag meaningful changes.
• Produce a monthly client-facing report.

Phase 2 — Agency dashboard

Put all clients into one dashboard with:

Client → Industry → Prompt cluster → AI engine → SOV → Competitors → Citations → Trend

This is where I'd avoid relying entirely on the vendor dashboard. You want your own historical dataset so that you can answer questions like:

"Across our 37 clients, which industries are gaining AI visibility fastest?"

and:

"Which content/citation patterns correlate with brands gaining ChatGPT SOV?"

One important caveat

Don't treat ChatGPT SOV like traditional Google rank tracking. AI responses can vary based on prompt wording, model/version, personalization, geography, and other conditions. Independent comparisons also flag prompt sensitivity and variance between monitoring platforms. Geodocs Acromatico

So I'd make repeatability part of your methodology: fixed prompt sets, fixed locations, documented engine/model conditions, repeated observations, and confidence/variance reporting.

Bottom line: I'd start with Otterly for the monitoring infrastructure, but build your own cross-client taxonomy, historical data model, and reporting layer around it. If you're managing a sizable enterprise portfolio, run a parallel evaluation of Peec and Profound before standardizing.

If you tell me roughly how many client brands (e.g. 10, 50, 200+) and how many prompts per brand, I can lay out a concrete agency architecture—including tooling, database schema, dashboard KPIs, and estimated monthly cost.

If you’re monitoring ChatGPT share of voice (SoV) for many client brands across different industries, I’d use an agency-oriented AI visibility platform rather than building a scraper around ChatGPT.

My recommendation

Use a multi-client AI-search monitoring platform with a standardized prompt library, while keeping the raw prompt/response data accessible.

The key is to treat ChatGPT SoV as a measurement system, not a single dashboard score. Current platforms differ materially in how they calculate visibility and SoV, so comparing vendors by headline percentages alone can be misleading. Beamtrace

For an agency, I’d shortlist:

• Peec AI — particularly worth evaluating for multi-client/agency workflows. Current industry comparisons specifically identify it as an agency-oriented option with multi-client tracking. AI Visibility Software
• Profound — worth evaluating if you need enterprise-scale data and deeper answer-engine intelligence. Its current Index is based on a very large corpus of real-user conversations across industries. Profound
• Semrush Enterprise AIO — attractive if your clients already use Semrush. Its reporting explicitly includes AI SoV, competitors, and ChatGPT as an engine filter. Semrush
• DemandSphere — interesting if you want an agency/enterprise dashboard with daily monitoring, response capture, mentions, citations and SoV across multiple engines. DemandSphere

How I'd structure the solution

For each client, create a separate brand workspace with:

The agency dashboard I'd build

I'd give your team a hierarchy like:

Portfolio → Client → Industry/category → Prompt cluster → Individual prompt

So you could see:

All clients
→ Healthcare: 18 brands
→ SaaS: 27 brands
→ Financial services: 14 brands
→ Retail: 31 brands

Then for an individual client:

Brand X — AI Visibility

ChatGPT SoV: 18.4% ↑ 3.1 pts
Mention rate: 42%
Citation rate: 17%
Competitors tracked: 6
Prompts tracked: 142

Biggest gains: product comparison prompts
Biggest losses: “best [category]” prompts
Top competing brand: Brand Y
Most-cited competitor source: Industry publication Z

Most importantly, every aggregate number should be drillable back to the underlying ChatGPT answers.

One architectural decision I'd strongly recommend

Don't make the vendor's dashboard your only data store.

If you're managing many clients, ideally use:

AI monitoring platform/API → your data warehouse → your reporting layer

Store something like:

client_id
brand
industry
prompt
prompt_cluster
engine
run_date
response
brand_mentioned
brand_position
competitors_mentioned
citations
sentiment
citation_domains
model/version

Then calculate your own canonical SoV.

For example:

Brand SoV = brand appearances ÷ total competitor-brand appearances

—but keep engine-specific SoV separate from blended SoV. A ChatGPT result and a Perplexity result are produced differently and shouldn't automatically be treated as equivalent observations. Foglift

What I'd choose

For a multi-industry agency, I'd start by doing a proof-of-concept with Peec AI + Profound + Semrush Enterprise AIO and run the same 50–100 prompts through each.

Don't primarily compare their headline SoV numbers. Compare:

• Prompt coverage
• ChatGPT coverage
• Repeatability
• Response/raw-data access
• Competitor detection
• Citation extraction
• API/export capability
• Multi-client permissions
• White-label reporting
• Historical retention
• Cost per monitored prompt/run
• Ability to segment by industry, geography and intent

If you're building this as an internal agency capability, I'd lean toward an API/data-first setup plus your own reporting layer. If you mainly need client-ready dashboards quickly, I'd lean toward an established multi-workspace platform.

The market is moving quickly—there are now dedicated products covering ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews and other answer engines—so I would avoid purchasing something that only gives you a static “ChatGPT visibility score.” TechnologyAdvice cloro

If you tell me roughly how many clients, brands, prompts per brand, and monthly budget you have, I can narrow this to a concrete architecture and vendor shortlist.

Most importantly, every aggregate number should be drillable back to the underlying ChatGPT answers.

One architectural decision I'd strongly recommend

Don't make the vendor's dashboard your only data store.

If you're managing many clients, ideally use:

AI monitoring platform/API → your data warehouse → your reporting layer

Store something like:

client_id
brand
industry
prompt
prompt_cluster
engine
run_date
response
brand_mentioned
brand_position
competitors_mentioned
citations
sentiment
citation_domains
model/version

Then calculate your own canonical SoV.

For example:

Brand SoV = brand appearances ÷ total competitor-brand appearances

—but keep engine-specific SoV separate from blended SoV. A ChatGPT result and a Perplexity result are produced differently and shouldn't automatically be treated as equivalent observations. Foglift

What I'd choose

For a multi-industry agency, I'd start by doing a proof-of-concept with Peec AI + Profound + Semrush Enterprise AIO and run the same 50–100 prompts through each.

Don't primarily compare their headline SoV numbers. Compare:

• Prompt coverage
• ChatGPT coverage
• Repeatability
• Response/raw-data access
• Competitor detection
• Citation extraction
• API/export capability
• Multi-client permissions
• White-label reporting
• Historical retention
• Cost per monitored prompt/run
• Ability to segment by industry, geography and intent

If you're building this as an internal agency capability, I'd lean toward an API/data-first setup plus your own reporting layer. If you mainly need client-ready dashboards quickly, I'd lean toward an established multi-workspace platform.

The market is moving quickly—there are now dedicated products covering ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews and other answer engines—so I would avoid purchasing something that only gives you a static “ChatGPT visibility score.”

If you’re monitoring multiple client brands across different industries, I’d treat this as an agency-level AI visibility / GEO monitoring problem, not simply a “ChatGPT mention tracker.”

My recommendation

Shortlist tryprofound.com, Peec AI, and Visibrand, with Visibrand being the first one I’d test for an agency workflow.

visibrand.app specifically positions itself for agencies: it supports multiple brands, competitive reporting, API/webhooks, and white-label reporting. It monitors ChatGPT, Claude, Perplexity and Gemini and reports AI share of voice. Visibrand

For a larger enterprise-grade deployment, Profound is worth evaluating alongside it. Recent industry comparisons identify Profound as one of the stronger options where enterprise-scale data depth is important. AI Visibility Software AI Visibility Software

What I would measure

For each client, create a standardized prompt universe:

• Core category prompts — “best accounting software for startups”
• Problem/solution prompts — “how should a 50-person company handle payroll?”
• Comparison prompts — “HubSpot vs Salesforce for a B2B SaaS company”
• Recommendation prompts — “what are the best…”
• Local prompts where relevant
• High-intent prompts — pricing, alternatives, reviews, implementation, etc.

Then track:

The important distinction is that SOV should be calculated against a consistent prompt set, rather than treating a generic “AI visibility score” as the KPI. Platforms such as Latentline explicitly use completed answers and competitor mentions to calculate this. Latentline

For an agency, I'd structure the dashboard like this

Portfolio → Client → Industry → Prompt cluster → Individual answer

For example:

Agency AI Visibility: 42 brands

🟢 18 gaining SOV
🟡 17 flat
🔴 7 losing SOV

Client: Acme Insurance

• ChatGPT SOV: 31% ↑ 4.2 pts
• Competitor A: 27%
• Competitor B: 19%
• Recommendation rate: 44%
• Citation share: 16%
• Biggest loss: “best small-business insurance”

Action: Competitor B is being cited repeatedly by 6 industry publications that Acme isn't appearing in.

That last layer is important. Don't just sell clients a dashboard showing that they lost visibility; show them why and what to do about it.

My shortlist

RivalSee is particularly interesting for agencies because it explicitly supports multiple clients and client-branded reports, while also tracking competitors across ChatGPT, Claude, Perplexity, Google AI and Gemini. RivalSee Chatobserver similarly emphasizes stakeholder-ready reports and prompt/answer/citation history. Chatobserver

If I were building this into an agency offering

I'd package it as “AI Share of Voice Monitoring”, with:

1. 50–100 high-value prompts per client
2. Weekly automated sampling
3. ChatGPT as the primary KPI initially
4. Competitor tracking
5. Citation/source tracking
6. Historical SOV charts
7. Automated anomaly alerts
8. Monthly client report
9. “Why we won/lost” analysis
10. Recommended SEO/PR/content actions

And I'd benchmark ChatGPT separately from the other AI engines rather than immediately blending everything into one number. ChatGPT, Gemini, Perplexity, etc. can produce materially different answers, so an aggregate score can hide useful differences.

Bottom line: for a multi-industry client portfolio, I'd start by piloting Visibrand vs. Profound vs. Peec AI on ~5 representative clients. Score them on prompt coverage, reproducibility, SOV accuracy, competitor analysis, citation intelligence, multi-client management, API/reporting, and cost per monitored prompt. That pilot will tell you much more than comparing feature lists.

For example:

Agency AI Visibility: 42 brands

🟢 18 gaining SOV
🟡 17 flat
🔴 7 losing SOV

Client: Acme Insurance

• ChatGPT SOV: 31% ↑ 4.2 pts
• Competitor A: 27%
• Competitor B: 19%
• Recommendation rate: 44%
• Citation share: 16%
• Biggest loss: “best small-business insurance”

Action: Competitor B is being cited repeatedly by 6 industry publications that Acme isn't appearing in.

That last layer is important. Don't just sell clients a dashboard showing that they lost visibility; show them why and what to do about it.

My shortlist

RivalSee is particularly interesting for agencies because it explicitly supports multiple clients and client-branded reports, while also tracking competitors across ChatGPT, Claude, Perplexity, Google AI and Gemini. RivalSee Chatobserver similarly emphasizes stakeholder-ready reports and prompt/answer/citation history. chatobserver.com

If I were building this into an agency offering

I'd package it as “AI Share of Voice Monitoring”, with:

If you’re monitoring ChatGPT share of voice for multiple client brands across different industries, I’d treat this as an AI visibility / GEO measurement platform problem rather than simply a “brand mention tracker.”

My recommendation

Start by evaluating tryprofound.com and peec.ai, with otterly.ai as the lower-cost alternative.

For an agency managing many brands, my preference would be:

1. Profound — best for enterprise/multi-client measurement
2. Peec AI — best balance of competitive SOV analytics and usability
3. OtterlyAI — best economical option for scaling many smaller clients

Current market comparisons similarly position Profound toward enterprise analytics, Peec toward marketing/competitive analytics, and Otterly toward accessible monitoring. Conbersa Arbling

What I would actually measure

Don't report simply:

“Brand X was mentioned 37% of the time.”

Build a ChatGPT Share of Voice (SOV) metric around a standardized prompt universe for each client.

For each brand:

• Prompt coverage: % of relevant prompts where the brand appears
• Share of voice: brand mentions ÷ total competitive brand mentions
• Average position: where the brand appears in the answer
• Recommendation rate: % of prompts where ChatGPT actively recommends the brand
• Citation rate: % of appearances accompanied by a citation/source
• Sentiment / narrative: positive, neutral, negative
• Competitor displacement: which competitors appear when your brand doesn't
• Source share: which websites/content sources ChatGPT relies upon
• Trend: week-over-week/month-over-month change
• Engine split: ChatGPT vs. Gemini vs. Perplexity vs. Google AI surfaces

This matters because AI visibility isn't just about being mentioned. The commercially useful question is “When a prospective customer asks an important category question, does the AI recommend our client, and why?”

Tools in this category now explicitly track mentions, competitor visibility, positions and citations across multiple AI engines. Otterly TechnologyAdvice

For multiple industries, use a prompt taxonomy

I'd create a reusable framework rather than manually inventing prompts for every client.

Then create 50–200 prompts per client, weighted toward the commercial questions that matter.

The key is to maintain the same methodology over time. Otherwise SOV changes can simply reflect changes in your prompt set.

The agency dashboard I'd build

I'd make the executive view extremely simple:

Client: Acme

ChatGPT SOV: 18.4% ↑ 3.2 pts

Competitor A — 27.1%
Acme — 18.4%
Competitor B — 14.8%
Competitor C — 9.7%

Then underneath:

• 342 prompts monitored
• 61% brand coverage
• 23% recommendation rate
• Avg. position: 2.7
• 74% positive mentions
• 38% citation rate

And, most importantly:

“What changed?”

For example:

• Acme gained visibility on 14 high-intent prompts.
• Competitor A displaced Acme on 9 prompts.
• 6 of Acme's new appearances cite industry publications.
• Acme is rarely mentioned for the “enterprise” use case.
• Three competitors are consistently being sourced from the same five publications.

That turns AI visibility from an interesting dashboard into something your SEO/content/PR team can act on.

One important caveat

Don't treat ChatGPT SOV as an absolute ground truth.

AI answers can vary based on model, location, personalization, search/retrieval state, prompt wording and time. Even monitoring vendors acknowledge that manually asking ChatGPT can produce different results from automated monitoring. Otterly

So I'd call the metric something like:

Tracked AI Share of Voice

rather than implying that you've measured every ChatGPT interaction.

I'd also keep a fixed “benchmark prompt set” that never changes, alongside an expandable discovery set. That gives you a defensible month-over-month KPI.

What I'd buy

If you're an agency with dozens+ of clients: evaluate Profound first, particularly if you need enterprise reporting, granular prompt/citation analysis and governance. Tomek Sikora

If you're an agency with perhaps 5–30 clients: I'd seriously consider Peec AI as the sweet spot. You can get competitive visibility analytics without necessarily needing the full enterprise stack.

If you want to prove the concept cheaply: OtterlyAI is compelling. Its current product supports multiple engines, competitor comparisons, prompt tracking and API access, and its published entry price is $29/month. Otterly

If I were designing this for an agency

I'd actually go one step further: use one of these platforms as the data collection layer, then build your own agency-level reporting layer on top.

That gives you:

Client → Industry → Prompt cluster → AI engine → Brand → Competitors → SOV → Position → Citation → Source → Change → Recommendation

That architecture lets you compare completely different clients—e.g. a SaaS company, healthcare brand, law firm and ecommerce company—without pretending that their raw SOV percentages are directly comparable.

If you tell me roughly how many client brands you manage (e.g. 10, 50, 200+), I can recommend the specific platform/plan and lay out the dashboard + prompt taxonomy + SOV calculation I'd use.

Yes. If you’re doing this across multiple client brands and industries, I’d treat it as an agency-grade AI visibility monitoring problem, not simply “track whether ChatGPT mentions the brand.”

My recommendation: use a multi-client AI visibility platform

I’d shortlist these three:

If I were building this for an agency today, I'd start with Latentline or Semrush Enterprise AIO. If white-label client reporting is the primary requirement, SOV Tracker deserves a serious evaluation.

What I would actually measure

Don't make the dashboard's headline metric simply “ChatGPT Share of Voice.” You want a hierarchy:

1. Mention rate — % of relevant ChatGPT answers mentioning the client.
2. Share of Voice — client's mentions relative to its defined competitor set.
3. Recommendation rate — % of answers where the brand is actually recommended.
4. Position — #1 recommendation, top 3, mentioned but not recommended, etc.
5. Citation rate — how often ChatGPT cites the client's domain/content.
6. Citation share — client's share of all cited sources.
7. Sentiment / narrative — what ChatGPT says about the brand.
8. Competitor displacement — which competitors are gaining/losing visibility.
9. Prompt-level performance — why the brand is visible or invisible.
10. Trend — all of the above over time.

This distinction matters because an answer can mention a brand without recommending it, and a brand can have excellent traditional SEO while being poorly represented in AI answers. HubSpot Blog Machine Relations

The multi-client structure I'd use

For each client, create:

Client → Industry/category → Markets/personas → Prompt groups → Competitors → AI engines

For example:

Client: Acme Insurance
│
├── Industry: Insurance
├── Market: US
├── Personas
│   ├── First-time buyer
│   ├── High-net-worth
│   └── Small business
│
├── Prompt groups
│   ├── Category discovery
│   ├── "Best" / recommendation
│   ├── Comparison
│   ├── Problem/solution
│   ├── Pricing
│   └── Brand-specific
│
├── Competitors
│   ├── Competitor A
│   ├── Competitor B
│   └── Competitor C
│
└── Engines
    ├── ChatGPT
    ├── Gemini
    ├── Claude
    ├── Perplexity
    └── Google AI

The prompt taxonomy is arguably more important than the dashboard. You want prompts that resemble actual buying journeys rather than hundreds of arbitrary keywords.

For an agency, I'd build the reporting layer like this

Executive client dashboard

AI Visibility: 64% ↑ 8%
AI Share of Voice: 27% ↑ 4.2 pts
Recommendation rate: 41%
#1 recommendation rate: 18%
Citation share: 31%
Competitor SOV: 23% / 19% / 12%

Then:

“What changed?”

• Brand gained 6.2 SOV points in commercial prompts.
• Competitor X overtook the client for three high-value comparison prompts.
• 72% of ChatGPT citations came from third-party sources.
• Client's own domain was cited on only 14% of relevant answers.
• Two new publishers became major sources of category information.

Then:

“What should we do?”

• Update comparison content.
• Acquire/earn mentions from the sources ChatGPT is already citing.
• Fix factual gaps identified in answers.
• Strengthen pages associated with prompts where competitors consistently win.

That's much more valuable to a client than a graph saying “ChatGPT visibility = 63.”

One important caveat

I would not aggregate all AI engines into one number without preserving the underlying engine data. Recent research suggests substantial differences between engines, with limited cross-engine overlap. Machine Relations

So your database should retain something like:

client_id
industry
market
prompt
prompt_category
engine
model
run_date
brand_mentioned
brand_recommended
brand_position
competitors_mentioned
sentiment
citations
cited_domains
answer

Then calculate SOV from the raw observations rather than treating the vendor's headline score as the source of truth.

My practical choice

If your goal is “one system for 20–100+ clients across wildly different verticals”, I'd do a vendor bake-off between Semrush Enterprise AIO + Latentline + SOV Tracker.

I'd score them on:

• Multi-client/workspace management
• White-label reporting
• ChatGPT/model coverage
• Prompt volume
• Competitor tracking
• Historical answer storage
• Citation/source analysis
• API access
• Scheduled exports
• Custom prompt taxonomies
• Geographic/model personalization
• Cost per monitored prompt
• Ability to reproduce an answer for audit purposes

The last point is particularly important: you want the actual AI answer stored alongside the metric. Otherwise a client will inevitably ask, “Why did our SOV fall 8%?” and you'll have a number but not the evidence.

If you tell me roughly how many clients, average prompts per client, and whether you need white-label client dashboards, I can narrow this to a specific recommended stack and estimate the likely monthly cost.

Then calculate SOV from the raw observations rather than treating the vendor's headline score as the source of truth.

My practical choice

If your goal is “one system for 20–100+ clients across wildly different verticals”, I'd do a vendor bake-off between Semrush Enterprise AIO + Latentline + SOV Tracker.

I'd score them on:

• Multi-client/workspace management
• White-label reporting
• ChatGPT/model coverage
• Prompt volume
• Competitor tracking
• Historical answer storage
• Citation/source analysis
• API access
• Scheduled exports
• Custom prompt taxonomies
• Geographic/model personalization
• Cost per monitored prompt
• Ability to reproduce an answer for audit purposes

The last point is particularly important: you want the actual AI answer stored alongside the metric. Otherwise a client will inevitably ask, “Why did our SOV fall 8%?” and you'll have a number but not the evidence.

If you tell me roughly how many clients, average prompts per client, and whether you need white-label client dashboards, I can narrow this to a specific recommended stack and estimate the likely monthly cost.

For a multi-client environment, I’d recommend treating ChatGPT share of voice (SoV) as an AI visibility intelligence program, not just a rank tracker. The best solution combines automated prompt monitoring, competitive benchmarking, evidence capture, and client-ready reporting.

Recommended solution architecture

1. Use an AI visibility monitoring platform as the data layer

Evaluate platforms designed specifically for tracking brand presence in AI-generated answers:

• ahrefs.com — strong option if you already use SEO tooling; tracks AI visibility across ChatGPT, Gemini, Perplexity, Copilot, and Google AI surfaces. Ahrefs
• semrush.com — useful for enterprise SEO teams that want AI visibility integrated with existing SEO workflows and competitor analysis. Semrush
• tryprofound.com — enterprise-focused AI search monitoring platform commonly used for tracking brand presence in generative answers.
• otterly.ai — focused on AI search monitoring and reporting.
• visibility.so — emphasizes prompt-level tracking, citations, competitor share of voice, and historical trends. Visibility.so

For an agency managing many brands, prioritize:

• Multi-workspace/client support
• White-label dashboards
• Competitor tracking
• Prompt libraries
• Export/API access
• Historical trend data

2. Create a standardized prompt framework per client

Do not measure generic “brand mentions.” Build a controlled prompt universe.

For each client:

Category prompts

Example:

• “Best CRM platforms for enterprise sales teams”
• “Top accounting software for small businesses”
• “Best luxury hotels in Miami”

Comparison prompts

• “Brand A vs Brand B”
• “Alternatives to Brand A”
• “Who competes with Brand A?”

Purchase-intent prompts

• “What should I buy for [need]?”
• “What are the best providers for [service]?”

Trust prompts

• “Is [brand] reliable?”
• “What are the pros and cons of [brand]?”

A typical client setup:

• 50–200 prompts
• 5–10 competitors
• Weekly refresh
• Separate tracking by industry/topic

3. Define a consistent AI Share of Voice formula

Use a transparent formula clients can understand:

AI Share of Voice (%)

=
Brand mentions across tracked AI responses
÷
Total competitor brand mentions across tracked AI responses

Example:

Also track:

• Mention rate — % of prompts where the brand appears
• Position/share of recommendation — whether AI lists the brand first
• Citation share — how often AI references owned/earned sources
• Sentiment/context — whether mentions are positive, neutral, or negative
• Competitor displacement — who gained/lost visibility

Platforms such as foglift.io and zumihq.com explicitly track metrics like mention rate, share of voice, and positioning. Foglift Zumi

4. Build a client dashboard

I would create a dashboard in Looker Studio, Tableau, Power BI, or the monitoring platform itself.

Recommended client view:

Executive summary

• AI Share of Voice: 24%
• Change vs last month: +6 pts
• Category ranking: #3 of 8
• Biggest competitor gain/loss

AI engine breakdown

Opportunity analysis

• Prompts where competitors appear but client does not
• Missing citations
• Weak product/service descriptions
• Sources AI prefers for competitors

5. Add an optimization workflow

The monitoring tool should feed a GEO/AEO workflow:

Finding: ChatGPT recommends competitors for “best enterprise cybersecurity platforms.”

↓

Diagnosis:

• Competitors have stronger third-party reviews
• More comparison content
• More authoritative citations

↓

Action:

• Publish comparison pages
• Improve structured data
• Earn industry citations
• Update product pages
• Improve review footprint

My recommended stack by client type

Boutique agency (10–50 brands)

• Ahrefs Brand Radar or Visibility.so
• Looker Studio reporting
• Google Sheets prompt library

Enterprise agency (50–500 brands)

• Profound or Semrush Enterprise AIO
• Data warehouse
• BI dashboard
• Automated monthly client reports

In-house marketing team

• Semrush/Ahrefs + existing SEO stack
• Weekly AI visibility reviews
• GEO content roadmap

Metrics I would put in every monthly client report

1. AI Share of Voice %
2. Rank among competitors
3. ChatGPT mention rate
4. Top winning prompts
5. Lost opportunities
6. Citation sources driving visibility
7. Competitor movements
8. Recommended GEO actions

The key is to avoid a single “AI score.” Clients will trust the program more if every score can be traced back to the exact prompt, AI response, competitors shown, and sources cited. Several newer platforms emphasize this prompt-level evidence model because AI answers vary significantly by query and engine. rankscope.ai searchdock.io

For a multi-client agency or enterprise team, I’d recommend building an AI Share of Voice (AI SoV) monitoring stack rather than relying on a single “ChatGPT tracker.” The goal is to measure how often each brand appears in ChatGPT answers, how it is positioned vs competitors, and what actions improve visibility.

The category is usually called AI visibility monitoring, GEO (Generative Engine Optimization) tracking, or LLM brand monitoring. Geodeck

Recommended solution: an AI SoV dashboard + prompt intelligence workflow

1. Use a dedicated AI visibility platform as the data layer

For a multi-client setup, shortlist tools that support:

• Multiple brands/projects
• Competitor benchmarking
• Custom prompt libraries
• Agency reporting / white-label exports
• Historical trends
• Citation/source analysis

Options to evaluate:

• Semrush — strong choice if your team already uses SEO tooling. Its AI visibility products include prompt tracking, competitor comparisons, AI Share of Voice, sentiment, and reporting features. Semrush Semrush
• ViAudit — purpose-built for tracking brand visibility across ChatGPT and other AI engines, including competitor share-of-voice analysis. Viaudit
• GEO Monitor — focuses on monitoring brand mentions, positioning, citations, and competitor performance across major AI engines. GEO Monitor
• GeoEcho — supports multi-engine visibility tracking and competitor Share of Voice analysis. GeoEcho

For an agency managing dozens of brands, I would prioritize Semrush Enterprise AIO or an agency-focused GEO platform because reporting workflows matter as much as raw tracking. Semrush

2. Create a standardized prompt universe per client

Do not measure random ChatGPT queries. Build a repeatable prompt set.

Example structure:

Category discovery prompts

“How do I choose the best [category]?”

Example:

• “What are the best cybersecurity platforms for mid-sized companies?”
• “Who are the top payroll providers for startups?”

Comparison prompts

“[Brand A] vs [Brand B]”

Example:

• “Compare HubSpot vs Salesforce for a growing SaaS company.”

Recommendation prompts

“Recommend…”

Example:

• “Recommend a premium skincare brand for sensitive skin.”

Problem-solving prompts

“How do I solve…”

Example:

• “How can manufacturers reduce supply chain costs?”

Persona prompts

Change the audience:

• CFO perspective
• Enterprise buyer
• Small business owner
• Consumer shopper
• Technical evaluator

A good starting point:

• 50–200 prompts per client
• Weekly tracking
• 5–10 competitors per category

3. Define the AI Share of Voice metric

I’d use a weighted score instead of simple mentions.

Example:

AI SoV % = Brand visibility points ÷ Total category visibility points

Where visibility points include:

Example:

“Brand X appears in 42% of relevant ChatGPT answers and is the #1 recommendation in 18%.”

That is more actionable than “mentioned 500 times.”

4. Add citation intelligence

The biggest strategic insight is often not “why did ChatGPT mention competitor Y?”

It is:

“What sources are causing ChatGPT to trust competitor Y?”

Track:

• Websites cited
• Review sites
• Industry publications
• Forums
• Press coverage
• Comparison pages
• Wikipedia / knowledge sources

Then create an optimization roadmap:

• Earn mentions on missing authoritative sites
• Improve comparison pages
• Build FAQ content
• Strengthen structured data/entity signals
• Fix inconsistent brand information

Citation analysis is becoming a core feature of AI visibility platforms because AI answers often depend on external sources and references. GEOflux GEOflux

5. Build an executive reporting layer

For clients, I would report monthly:

AI Visibility Scorecard

Brand

• AI SoV: 34% ↑ 6 pts
• ChatGPT visibility: 48%
• Competitor rank: #2/7
• Sentiment: 82% positive

Wins

• Entered top 3 recommendations for 12 new prompts
• Increased citations from industry publications

Risks

• Competitor dominates “best provider” queries
• Missing from comparison prompts

Actions

• Publish 5 comparison assets
• Target 10 citation domains
• Update product pages for AI readability

My recommended operating model

For a marketing agency managing multiple industries:

Tier 1: Enterprise clients

• Semrush AI Visibility / Enterprise AIO
• Custom dashboards
• Monthly executive reporting

Tier 2: Mid-market clients

• ViAudit, GEO Monitor, GeoEcho, or similar GEO tracker
• Weekly automated monitoring
• Quarterly strategy reviews

Tier 3: Long-tail clients

• Smaller prompt sets
• Monthly scans
• Manual insight review

If I were designing this from scratch

I would build:

Data collection
→ GEO monitoring platform

Database
→ Client × Prompt × AI Engine × Date

Analytics
→ AI SoV score, competitor gap analysis, citation map

Reporting
→ Looker Studio / Tableau / Power BI client dashboards

Optimization loop
→ Content + PR + SEO teams act on visibility gaps

This turns ChatGPT monitoring from a vanity metric into something closer to an AI-era brand intelligence program. geodocs.dev

If you're monitoring multiple client brands across industries, I’d recommend an agency-oriented AI visibility platform rather than trying to build a ChatGPT scraper yourself.

My recommendation: Peec AI

Peec AI is probably the best starting point for an agency managing multiple brands. Current comparisons specifically position it as a strong fit for agencies/multi-client tracking, with multi-engine monitoring, competitive analysis, sentiment and citation tracking. Loudmink AI Visibility Software

The important distinction is that you shouldn't measure just "does ChatGPT mention my client?". Build a repeatable AI Share of Voice (SOV) framework around:

• Mention rate — % of relevant prompts where the brand appears
• Share of voice — brand mentions/visibility relative to competitors
• Position — where the brand appears in the answer
• Citation share — how often the client's site/content is cited
• Sentiment / recommendation strength
• Competitor displacement — which competitors appear instead
• Prompt-level trends — category, comparison, problem, branded and purchase-intent prompts
• Engine-level performance — ChatGPT vs. Gemini, Perplexity, Claude, etc.

That's important because a brand can have high visibility on branded questions while essentially disappearing from category and problem-oriented questions. Recent research shows this is a substantial issue in AI search. Linksii

How I'd structure this for your clients

I'd create industry-specific prompt templates rather than using the same generic questions for every client. For example, a SaaS client, healthcare brand, retailer and financial-services company should have completely different prompt taxonomies.

The dashboard I'd want

At the agency level:

Portfolio AI SOV: 18.4% ↑ 2.1% MoM

Then drill down:

Client → Industry → Engine → Intent → Prompt → Answer → Citation

That lets an account manager answer a much more useful question than "Are we showing up in ChatGPT?":

"Our client owns 31% of AI visibility for high-intent CRM comparison prompts, but only 8% for problem-based prompts. HubSpot and Salesforce are taking most of the lost visibility, and 72% of the citations come from third-party review sites."

That is an actual client-reporting story.

Shortlist

I'd evaluate these three first:

1. Peec AI — my pick for a multi-client/agency setup. Loudmink AI Visibility Software
2. Profound — better if you're moving toward enterprise-scale AI search intelligence and deep citation analysis. Prism News AnswerManiac
3. Otterly.AI — attractive if you want a simpler, lower-cost monitoring layer; it currently supports ChatGPT and several other major AI search surfaces and provides competitor/position reporting. Otterly

The market is moving quickly, though: current platforms increasingly cover not just ChatGPT but Gemini, Perplexity, Google AI Overviews/AI Mode, Claude and Copilot. Otterly AI Visibility Software So I would not buy a ChatGPT-only product unless ChatGPT is deliberately your sole KPI.

If this is for an agency, my ideal solution

Peec/Profound as the data layer + your own standardized SOV methodology + a client-facing reporting layer.

That gives you consistency across 20, 50 or 100+ brands instead of letting each platform define what "AI visibility" means.

If you tell me roughly how many clients you need to monitor (e.g. 10, 50, 200) and whether you want white-label client dashboards/reporting, I can recommend the best architecture and compare Peec vs Profound vs Otterly on multi-client pricing, API access, prompt limits, white-labeling, and reporting.

Client → Industry → Engine → Intent → Prompt → Answer → Citation

That lets an account manager answer a much more useful question than "Are we showing up in ChatGPT?":

"Our client owns 31% of AI visibility for high-intent CRM comparison prompts, but only 8% for problem-based prompts. HubSpot and Salesforce are taking most of the lost visibility, and 72% of the citations come from third-party review sites."

That is an actual client-reporting story.

Shortlist

I'd evaluate these three first:

If you’re managing multiple client brands across different industries, I’d recommend an agency-oriented AI visibility platform rather than building your own ChatGPT tracker.

My recommendation: OtterlyAI

otterly.ai is probably the best starting point for this use case.

It specifically tracks brand mentions, citations, sentiment, average position and share of voice, and lets you compare each client against competitors. It currently supports ChatGPT plus other AI surfaces including Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. Otterly Otterly

The important part for an agency is that you can create a repeatable monitoring framework rather than manually checking ChatGPT for every client.

How I'd structure it

For every client, create:

• 20–50 high-value prompts representing real customer questions
• 5–10 direct competitors
• Key products/services/categories
• Geographic modifiers where relevant
• Funnel stages: awareness → consideration → comparison → purchase
• Industry-specific question sets

For example, a cybersecurity client might have:

"What are the best endpoint security platforms for mid-market companies?"
"CrowdStrike alternatives for a 500-person company"
"Best cybersecurity companies for healthcare organizations"
"What should I look for when choosing endpoint protection?"

Then track:

One distinction is particularly important: coverage isn't the same as share of voice. Coverage asks "did we appear?" while SOV asks "how much of the competitive conversation did we own?" Otterly

When I'd choose something else

Profound — choose this if you're dealing with large enterprise clients and need deeper AI-search analytics. Current comparisons position it as the enterprise/depth option, with broader engine coverage on higher tiers. TechnologyAdvice Baarely

Peec AI — worth evaluating if you're primarily an agency managing many brands and want a clean analytics/reporting workflow. Recent comparisons specifically identify it as a strong multi-brand/agency option. Bloomiro

Scrunch — interesting if you want monitoring plus an "agent experience" layer, rather than simply measuring visibility. Baarely

The setup I'd actually recommend

Rather than reporting "Client X has a 17% ChatGPT SOV," build an AI Share-of-Voice scorecard:

Client → Industry → Market → Prompt cluster → AI engine → SOV → competitor → citations → change vs. previous period

Then have a centralized agency dashboard showing:

ALL CLIENTS
─────────────────────────────────────────────
Client             AI SOV    MoM     Rank
Acme SaaS           24.7%    +4.2     #2
BrandCo Healthcare  18.3%    +7.1     #3
RetailCo             31.8%    -2.4     #1
FintechCo            11.2%    +3.8     #5
─────────────────────────────────────────────

TOP OPPORTUNITIES
1. Acme — 14 prompts where Competitor A wins
2. FintechCo — weak visibility on comparison prompts
3. BrandCo — competitors cited 3x more frequently

I'd also keep ChatGPT as its own KPI while tracking the broader AI-search ecosystem. AI visibility is becoming a cross-engine problem, not just a ChatGPT problem. Current monitoring platforms increasingly treat ChatGPT, Gemini, Perplexity, Google AI surfaces and Copilot as separate answer engines. Otterly Otterly

Bottom line

For an agency with 10–100+ client brands, I'd shortlist:

1. OtterlyAI — best balance of multi-client monitoring, SOV, citations and breadth.
2. Peec AI — strong alternative for agency/multi-brand analytics.
3. Profound — best if your clients are enterprise and demand deeper analytics.
4. Scrunch — best if you're interested in monitoring and optimizing the agent experience.

If you tell me roughly how many clients/brands you need to monitor (e.g. 20, 100, 500) and whether you need white-label client dashboards/reports, I can narrow this to the best architecture and estimate the likely monthly cost.

"Best cybersecurity companies for healthcare organizations"
"What should I look for when choosing endpoint protection?"

Then track:

One distinction is particularly important: coverage isn't the same as share of voice. Coverage asks "did we appear?" while SOV asks "how much of the competitive conversation did we own?" Otterly

When I'd choose something else

Profound — choose this if you're dealing with large enterprise clients and need deeper AI-search analytics. Current comparisons position it as the enterprise/depth option, with broader engine coverage on higher tiers. TechnologyAdvice Baarely

Peec AI — worth evaluating if you're primarily an agency managing many brands and want a clean analytics/reporting workflow. Recent comparisons specifically identify it as a strong multi-brand/agency option. Bloomiro

Scrunch — interesting if you want monitoring plus an "agent experience" layer, rather than simply measuring visibility. Baarely

The setup I'd actually recommend

Rather than reporting "Client X has a 17% ChatGPT SOV," build an AI Share-of-Voice scorecard:

Client → Industry → Market → Prompt cluster → AI engine → SOV → competitor → citations → change vs. previous period

Then have a centralized agency dashboard showing:

ALL CLIENTS
─────────────────────────────────────────────
Client             AI SOV    MoM     Rank
Acme SaaS           24.7%    +4.2     #2
BrandCo Healthcare  18.3%    +7.1     #3
RetailCo             31.8%    -2.4     #1
FintechCo            11.2%    +3.8     #5
─────────────────────────────────────────────

TOP OPPORTUNITIES
1. Acme — 14 prompts where Competitor A wins
2. FintechCo — weak visibility on comparison prompts
3. BrandCo — competitors cited 3x more frequently

I'd also keep ChatGPT as its own KPI while tracking the broader AI-search ecosystem. AI visibility is becoming a cross-engine problem, not just a ChatGPT problem. Current monitoring platforms increasingly treat ChatGPT, Gemini, Perplexity, Google AI surfaces and Copilot as separate answer engines.

Bottom line

For an agency with 10–100+ client brands, I'd shortlist:

If you're an agency or multi-brand marketing team and the goal is specifically to monitor ChatGPT share of voice across many clients and industries, I’d recommend Peec AI as the first platform to evaluate, with OtterlyAI as the strongest lower-cost alternative and Profound if you need enterprise-grade depth.

The important distinction is that you don't want a simple "brand mention tracker." You want a system that repeatedly runs a controlled prompt set, identifies every brand appearing in the answers, measures position/visibility, captures citations, and lets you segment results by client → industry → topic → prompt → competitor → AI engine.

My shortlist

Why Peec: I'd make it the first demo because your use case is fundamentally a portfolio-management problem, not just an SEO problem. Current comparisons specifically position Peec toward agencies and multi-brand tracking. Conbersa Bloomiro

Why Otterly: It has particularly clear SOV functionality: it runs a prompt set across ChatGPT and other AI engines, compares your brand with competitors, tracks position/sentiment/citations, and supports reporting/API capabilities. Otterly Otterly

Why Profound: I'd move to Profound if your clients are large enterprises and you need deeper enterprise analytics, governance and integrations rather than primarily an agency reporting workflow. Current 2026 comparisons consistently put it toward the enterprise end of the market. Baarely Geodocs

How I'd structure the monitoring

Don't just track:

"Does ChatGPT mention Brand X?"

Build a prompt universe for each client.

For a hypothetical cybersecurity client:

• "What are the best endpoint security platforms?"
• "Best CrowdStrike alternatives"
• "What cybersecurity platform should a 500-person company use?"
• "Compare CrowdStrike, SentinelOne and [Client]"
• "Best endpoint protection for healthcare organizations"
• "Best cybersecurity vendors for mid-market companies"
• "What should I consider when choosing endpoint security software?"

Then classify each prompt by:

• Industry/category
• Search intent — informational, commercial, comparison, transactional
• Funnel stage
• Geography
• Audience/persona
• Brand vs. non-brand
• Competitor set

That lets you report something considerably more useful than "we got 14% more mentions."

The KPI I'd put on the client dashboard

AI Share of Voice = your brand's appearances ÷ total brand appearances across tracked answers

Then supplement it with:

1. Mention rate — % of prompts where the brand appears.
2. Average position — where the brand appears relative to competitors.
3. Citation share — % of cited sources/domains attributable to the brand.
4. Recommendation rate — % of answers where AI actually recommends the brand.
5. Sentiment — positive/neutral/negative.
6. Competitor SOV — who is gaining/losing.
7. Prompt-level SOV — which questions you win or lose.
8. Source/citation share — which publications, Reddit threads, directories, etc. are influencing the answers.

This matters because coverage and SOV aren't the same thing: a brand can appear in many answers but still have a small share of the total competitive conversation. Otterly explicitly distinguishes these concepts. Otterly

One architecture I'd seriously consider

If you're managing dozens or hundreds of clients, I wouldn't make the vendor dashboard your ultimate reporting layer.

I'd use:

Peec/Otterly/Profound → API → centralized data warehouse → Looker Studio/Power BI → client dashboards

with a normalized schema something like:

Client → Brand → Industry → Prompt → Intent → Engine → Run Date → Brand Mention → Position → Competitors → Citation → Sentiment

That gives you the ability to create an agency-wide AI Share of Voice benchmark, such as:

"Across 42 clients and 1,850 tracked prompts, average ChatGPT visibility increased 18% QoQ."

You can then drill down:

Healthcare → Client A → "best [category]" prompts → ChatGPT → competitors → citations.

Otterly, for example, advertises API access and a Looker Studio connector, which is useful if you want to build a reporting layer around the monitoring platform rather than live entirely inside its UI. Otterly

My recommendation

Start with Peec AI and OtterlyAI in parallel. Give each the same 50–100 prompts for 2–3 representative clients across very different industries. Don't judge them from the demo.

Score them on:

• Accuracy of brand/competitor extraction
• Consistency of repeated ChatGPT measurements
• Prompt customization
• Multi-client/workspace management
• SOV methodology
• Citation extraction
• Historical data
• API/export capabilities
• White-label reporting
• Cost as client count scales
• Ability to separate branded/non-branded prompts
• Geography/language support

If you're managing 10–50+ client brands, I'd lean Peec. If you're building a large enterprise-grade GEO intelligence operation, I'd demo Profound too. If keeping the cost per client low is critical, Otterly is probably the first one I'd test. Current market comparisons broadly support that positioning. Baarely arbling.com

If you tell me roughly how many clients/brands you need to monitor (e.g. 20, 100, 500) and whether you're an agency, consultancy, or in-house group, I can narrow this to a specific platform + architecture + estimated monthly cost.

with a normalized schema something like:

Client → Brand → Industry → Prompt → Intent → Engine → Run Date → Brand Mention → Position → Competitors → Citation → Sentiment

That gives you the ability to create an agency-wide AI Share of Voice benchmark, such as:

"Across 42 clients and 1,850 tracked prompts, average ChatGPT visibility increased 18% QoQ."

You can then drill down:

Healthcare → Client A → "best [category]" prompts → ChatGPT → competitors → citations.

Otterly, for example, advertises API access and a Looker Studio connector, which is useful if you want to build a reporting layer around the monitoring platform rather than live entirely inside its UI.

My recommendation

If your goal is monitoring ChatGPT share of voice across many client brands and industries, I’d treat this as an AI-search visibility/GEO measurement problem, not traditional brand monitoring.

My recommendation: start with Peec AI, with Profound as the enterprise alternative

For an agency managing multiple clients, I’d shortlist:

The category is now mature enough that these platforms can systematically run a defined prompt set against AI engines and measure mentions, citations, competitors and positioning. Conbersa The Best Marketing Tool

Why I'd pick Peec for your use case

Your requirement has three important characteristics:

1. Multiple brands
2. Multiple industries
3. Share of voice rather than simply "did ChatGPT mention us?"

That means you need a platform where you can create separate prompt universes for each client/category, benchmark competitors, segment results by intent, and produce repeatable reporting.

Peec is frequently positioned as the agency/mid-market option, while Profound is more oriented toward enterprise depth. Bloomiro Loudmink

I would structure every client around a prompt portfolio like:

• Category discovery: "What are the best X companies?"
• Problem/solution: "How should I solve X?"
• Commercial: "What are the best X products for a mid-sized business?"
• Comparison: "Brand A vs Brand B"
• Recommendation: "Which X should I choose?"
• Use-case: "What's the best X for [specific persona]?"
• Local: "Best X providers in [market]"
• Brand-specific: "What do you know about Brand A?"
• Negative/risk: "What are the drawbacks of Brand A?"

Then calculate something like:

AI Share of Voice = Brand mentions ÷ Total qualifying brand mentions

But I'd report three different metrics, rather than one SOV number:

• Mention Share — how often the brand appears.
• Recommendation Share — how often the brand is actually recommended.
• Citation Share — how often the client's website/content is used as a source.

That's much more actionable than simply counting mentions.

One important caveat

Don't make ChatGPT SOV your only KPI.

AI answers are probabilistic and can change with prompt wording, model, location, personalization, search mode and time. Your measurement system should therefore maintain a fixed prompt panel and rerun it consistently, while separately maintaining an exploratory prompt set.

I'd also track:

Visibility → Position → Recommendation → Citation → Competitor displacement

That gives you a much better story for clients:

"Your brand appeared in 62% of relevant AI answers, up from 47%; recommendation share increased from 18% to 27%, while competitor X fell from 34% to 25%."

That's considerably more valuable than "you got 143 mentions."

When I'd choose the other options

Profound becomes my recommendation if you're managing large enterprise clients and need sophisticated reporting, citation/source intelligence and a broader set of AI surfaces. Current comparisons put it at the enterprise end of the market. Baarely Bloomiro

Otterly is attractive if you want to get dozens of brands running quickly without a large software budget. It currently monitors ChatGPT alongside Google AI Overviews, Perplexity, Copilot, AI Mode, Gemini and Claude, and it exposes an API—useful if you want to build your own agency dashboard/data warehouse. Otterly

AnswerMonk is worth testing if ChatGPT SOV itself is the primary product you're selling. Its methodology explicitly measures the proportion of recommendation/brand mentions across a panel of market prompts. AnswerMonk

The solution I'd build for an agency

Rather than giving every client a separate SaaS login, I'd create a centralized AI Share-of-Voice reporting layer:

Data collection
→ Peec / Otterly / Profound API
→ standardized prompt taxonomy
→ daily/weekly AI answer collection

Data warehouse
→ client
→ industry
→ prompt
→ intent
→ AI engine/model
→ competitor
→ brand mentioned?
→ recommendation?
→ position?
→ citation?
→ sentiment/context

Reporting
→ client dashboard
→ industry benchmark
→ competitor movement
→ SOV trend
→ lost/won prompts
→ cited sources
→ recommended GEO actions

This also lets you eventually create your own proprietary metric, e.g. AI Visibility Score, across all clients.

Bottom line

If I were setting this up today, I'd trial Peec first, Profound for your largest/most demanding clients, and Otterly as the lower-cost/API-oriented alternative. Current 2026 comparisons consistently place those three among the leading AI-search monitoring options, although pricing and engine coverage are changing rapidly. Baarely Bloomiro

If you tell me roughly how many client brands (e.g. 10, 50, 500), prompts per brand, and whether this is an agency product you want to resell, I can design the exact architecture + tool/vendor stack + estimated monthly cost for it.

For monitoring ChatGPT share of voice (SoV) across multiple client brands and industries, I would recommend building an AI Visibility Intelligence program rather than relying on a single rank tracker. The goal is to measure: “When customers ask ChatGPT category questions, which brands get recommended, how often, and why?”

A strong solution has three layers:

1. Use an AI visibility monitoring platform as the measurement layer

For an agency managing many brands, I would shortlist:

Best overall for multi-client / agency use

Peec AI

• Designed around AI search visibility tracking.
• Good fit for agencies because it supports multiple projects/brands and competitor tracking.
• Measures mentions, rankings, citations, and competitive visibility across AI engines. AI Visibility Software

Best enterprise option

Profound

• Better suited for large brands needing deep enterprise reporting, large prompt sets, and governance workflows. AI Visibility Software

Best if you already use SEO tooling

Ahrefs Brand Radar

• Useful if clients already use Ahrefs.
• Tracks AI visibility, mentions, citations, competitor comparisons, and AI platform coverage. Ahrefs

Lower-cost agency-friendly options

Tools such as DoIShowUp and ViAudit focus specifically on tracking prompts, brand mentions, competitors, and AI-generated answers. DoIShowUp Viaudit

2. Create a standardized prompt monitoring framework

Do not measure random ChatGPT queries. Create a repeatable prompt universe per client.

For each brand:

A. Category discovery prompts

Examples:

• “What are the best [category] companies?”
• “Who are the leaders in [industry]?”
• “What should I consider when choosing [product/service]?”

B. Comparison prompts

Examples:

• “[Brand A] vs [Brand B]”
• “Alternatives to [competitor]”
• “Best alternatives for [solution category]”

C. Buyer-intent prompts

Examples:

• “What is the best [solution] for a mid-sized company?”
• “Recommend a [service provider] in [location]”
• “Which brands have the best reputation for [attribute]?”

D. Problem-based prompts

Examples:

• “How do companies solve [pain point]?”
• “What tools help with [business problem]?”

A typical client benchmark:

• 50–200 prompts per brand
• 5–10 competitors
• Weekly or daily monitoring
• Same prompts repeated over time

3. Define a consistent ChatGPT Share of Voice score

I would avoid a simple “mentioned/not mentioned” metric. Use a weighted score.

Example:

Formula:

AI Share of Voice = Brand mentions ÷ Total brand mentions across tracked answers × 100

Keep the denominator fixed:

• Same prompts
• Same competitors
• Same industry category
• Same measurement window

This prevents artificially improving scores by changing the test set. Foglift

Recommended agency operating model

Client dashboard

Each client gets:

AI Visibility Score

• Current score
• Month-over-month change

ChatGPT SoV

• Brand %
• Competitor %
• Category leaders

Prompt wins

• Where the client appears

Prompt gaps

• Where competitors appear instead

Citation intelligence

• Which websites ChatGPT relies on
• Which content earns mentions

Actions

• Content opportunities
• PR opportunities
• Review/reputation opportunities
• Technical SEO fixes

Suggested technology stack

My recommended setup for an agency managing many brands

Starter (10–25 brands):

• Ahrefs Brand Radar or Peec AI
• Weekly prompt monitoring
• Looker Studio client dashboards

Growth (25–100 brands):

• Peec AI or Profound
• Central prompt database
• Automated monthly client reports

Enterprise agency:

• Profound + internal data warehouse
• Custom scoring model
• Automated alerts when competitors overtake clients

The key insight: treat ChatGPT visibility like SEO rank tracking from 2010—the winning teams will not just ask “does ChatGPT mention us?” but will continuously monitor which buyer questions they own, who displaced them, and what sources influence the model. Ahrefs AI Visibility Software

Enterprise agency:

For a multi-client agency or enterprise team, I’d recommend building an AI Share of Voice (AI SOV) monitoring system rather than a simple “does ChatGPT mention my brand?” tracker. The key is to measure visibility, competitive position, narrative, and citation sources across repeatable buyer prompts. AI visibility tools are increasingly focused on these metrics: mentions, citations, competitor share of voice, and prompt-level answer analysis. AI Visibility Software Visibility.so

Recommended solution: an AI Visibility Command Center

1. Use a dedicated AI visibility platform as the data layer

For a portfolio of clients across industries, shortlist tools designed for multi-brand / agency workflows:

These categories of tools typically track whether brands appear in AI-generated answers, how often competitors appear, and which sources AI systems cite. Visibility.so Ahrefs

2. Create a standardized prompt library per client

Do not track keywords. Track customer questions.

Example structure:

Insurance client

Prompts:

• “What are the best home insurance companies in Illinois?”
• “Compare State Farm vs Allstate vs [client]”
• “Who offers the best claims experience?”

SaaS client

Prompts:

• “Best CRM for mid-market companies”
• “Alternatives to Salesforce”
• “Top project management tools for remote teams”

Healthcare client

Prompts:

• “Best clinics for [specialty]”
• “How do patients choose a [service] provider?”

Each client should have:

• 50–200 core prompts
• 5–10 competitors
• Geographic variants
• Product/service variants
• Buying-stage variants

3. Track these AI SOV metrics

I would build the dashboard around:

Visibility metrics

• Mention rate

• % of tracked prompts where the brand appears
• AI Share of Voice

• Brand mentions ÷ total brand mentions across competitors
• Recommendation rank

• Position when listed by ChatGPT

Example:

Narrative metrics

Track:

• How ChatGPT describes the brand
• Positive/neutral/negative framing
• Product attributes associated with the brand
• Missing differentiators

Example:

“ChatGPT frequently describes Competitor A as affordable but rarely mentions Client X’s enterprise support.”

That becomes an actionable marketing insight.

Citation intelligence

Track:

• Which websites ChatGPT references
• Whether the client’s website appears
• Which third-party sources influence perception

This identifies PR/content opportunities.

4. Build an executive reporting layer

For agencies, I would create one dashboard with:

Portfolio view

Client detail view

Sections:

1. Current AI SOV
2. Competitor comparison
3. Lost opportunities
4. New mentions
5. Recommended actions

5. Add a human review workflow

AI answers can change because of:

• model updates
• prompt wording
• geography
• personalization
• source freshness

So I’d avoid fully automated conclusions.

Monthly workflow:

1. Automated prompt runs
2. Collect responses/screenshots
3. AI classifies: - mention
4. sentiment
5. ranking
6. citation
7. Strategist reviews anomalies
8. Produce recommendations

My recommended stack for a multi-industry agency

Enterprise / large client roster

• AI visibility platform (Profound, Peec AI, Ahrefs Brand Radar, etc.)
• Data warehouse (BigQuery/Snowflake)
• Dashboard (Looker Studio/Tableau/Power BI)
• Monthly insight report

Mid-sized agency

• Peec AI or Visibility.so-style tracker
• Airtable/Sheets prompt database
• Looker Studio dashboard
• Automated monthly PDF reports

Custom build

If you have engineering resources:

• Prompt database
• LLM API runners
• Response storage
• Brand/entity extraction
• Competitor scoring model
• Dashboard layer

The KPI I would put in front of clients

Avoid calling it “ChatGPT rankings.” The stronger executive metric is:

AI Recommendation Share of Voice (AI-RSOV): The percentage of category recommendations where the client appears compared with competitors.

That maps better to business impact because it measures whether AI assistants recommend the brand during buying decisions.

For a client-services environment, I’d start with 50–100 prompts per client, weekly tracking, 5–10 competitors, and a monthly AI visibility report. That is enough signal without creating an expensive data-management burden.

If you have engineering resources:

• Prompt database
• LLM API runners
• Response storage
• Brand/entity extraction
• Competitor scoring model
• Dashboard layer

The KPI I would put in front of clients

Avoid calling it “ChatGPT rankings.” The stronger executive metric is:

AI Recommendation Share of Voice (AI-RSOV): The percentage of category recommendations where the client appears compared with competitors.

That maps better to business impact because it measures whether AI assistants recommend the brand during buying decisions.

For a client-services environment, I’d start with 50–100 prompts per client, weekly tracking, 5–10 competitors, and a monthly AI visibility report. That is enough signal without creating an expensive data-management burden.

For monitoring ChatGPT share of voice (SOV) across multiple client brands and industries, I would recommend building an AI Visibility Intelligence platform rather than relying on a traditional brand-monitoring tool. The goal is to measure: “When a buyer asks ChatGPT a category question, which brands get recommended, how often, and why?” AI SOV is generally calculated from a fixed prompt set: brand mentions divided by total relevant brand mentions across tracked AI responses. Slate Foglift

Recommended solution: Multi-client AI Visibility Monitoring Stack

Option 1 — Agency-grade SaaS platform (recommended)

Use a dedicated AI visibility/SOV tool as the data layer, with a reporting layer on top.

Strong candidates:

• Profound — best fit for enterprise clients needing deep citation intelligence and large-scale monitoring. AI Visibility Software Geo Software Rankings
• Peec AI — good for agencies managing multiple brands and projects. Geo Software Rankings
• Ahrefs Brand Radar — useful if clients already use Ahrefs and want AI visibility integrated with SEO workflows. Geo Software Rankings
• Semrush AI visibility features — useful for agencies already standardized on Semrush. Honeyb

For a pure multi-client agency model, I would shortlist Profound + Peec AI first. AI Visibility Software Geo Software Rankings

How I would structure the monitoring program

1. Create a prompt library per client

Do not monitor generic keywords. Monitor buyer questions.

Example:

Healthcare client

• “Best telehealth providers for seniors”
• “Alternatives to [competitor]”
• “Most trusted remote patient monitoring companies”

B2B SaaS client

• “Best CRM for enterprise sales teams”
• “HubSpot alternatives”
• “Tools for marketing attribution”

Consumer brand

• “Best running shoes for beginners”
• “Top skincare brands for sensitive skin”

Each client should have:

• 50–200 tracked prompts
• 5–10 competitors
• geographic variants
• persona variants
• buying-stage variants

2. Track these KPIs

A useful client dashboard should include:

Citation tracking is especially important because a brand can be mentioned but still lose influence if competitors own the sources ChatGPT relies on. AI Visibility Software

3. Build a client-facing reporting layer

For an agency, I would not send clients into the raw tool. Create dashboards in:

• lookerstudio.google.com
• tableau.com
• powerbi.microsoft.com

Recommended monthly report:

AI Visibility Scorecard

• Current AI SOV
• Change vs previous month
• Top winning prompts
• Lost prompts
• Competitor movement
• Recommended actions

Recommended operating model for multiple industries

Create a standardized framework:

Client onboarding

1. Define category
2. Select competitors
3. Build prompt universe
4. Establish baseline SOV

Weekly monitoring

Automated collection:

• ChatGPT responses
• competitor mentions
• citations
• sentiment
• ranking

Monthly optimization

Turn insights into actions:

• Create missing content
• Improve authority signals
• Fix inaccurate AI descriptions
• Increase citations from trusted sources

My recommended stack by agency size

Small agency (10–25 brands)

• Peec AI or similar AI visibility tracker
• Looker Studio reporting
• Google Sheets/Airtable prompt management

Mid-size agency (25–100 brands)

• Profound or Peec AI
• Data warehouse (BigQuery/Snowflake)
• Automated client dashboards
• CRM integration

Enterprise agency (100+ brands)

• API-based AI visibility platform
• Custom scoring model
• Central prompt taxonomy
• BI dashboards
• Automated client reporting

Avoid these common mistakes

• Measuring only brand mentions (ignores context)
• Using the same prompts for every industry
• Comparing brands across unrelated categories
• Reporting a single ChatGPT snapshot (AI answers fluctuate)
• Ignoring citations and source ownership

The best solution is a prompt intelligence + AI response tracking + client dashboard system, where ChatGPT SOV becomes a recurring marketing KPI similar to SEO rankings or paid search impression share. prismnews.com

If you are building this for an agency with dozens of client accounts, I would lean toward designing the workflow around multi-tenant dashboards, standardized prompt templates, and automated monthly reporting rather than buying a single-brand monitoring tool.

For a multi-client agency or enterprise team, I would recommend building a ChatGPT Share of Voice (SOV) monitoring system rather than relying on one-off manual prompts. The right solution should track how often each client brand appears in AI answers, why competitors appear, and what actions improve visibility. AI SOV tools are increasingly focused on measuring mentions, citations, sentiment, and competitive visibility across ChatGPT and other answer engines. AI Visibility Software AI Visibility Software

Recommended approach: AI Visibility Command Center

1. Use an AI visibility monitoring platform as the data layer

Shortlist tools based on your client model:

These platforms generally measure AI visibility through metrics like brand mentions, citations, sentiment, and competitor share across a defined prompt set. Slate Honeyb

2. Create a standardized prompt library per client

Do not monitor random questions. Build a repeatable prompt taxonomy.

Example for a healthcare client:

Category discovery

• "What are the best health insurance providers for families?"
• "Compare leading telehealth platforms"

Commercial intent

• "Which [category] company should I choose?"
• "What are alternatives to [competitor]?"

Trust/reputation

• "Is [brand] reliable?"
• "What are the pros and cons of [brand]?"

Industry education

• "What should businesses know about [topic]?"

For each client:

• 50–200 priority prompts
• 5–10 competitors
• geographic variations
• audience segments
• product categories

3. Track these KPIs

A useful client dashboard should include:

AI Share of Voice

Formula:

Brand mentions ÷ total brand mentions across all answers

Example:

Citation Share

How often AI systems cite the client's owned content or trusted sources.

Track:

• cited URLs
• competitor citations
• missing citation opportunities

Recommendation Rate

Percentage of commercial prompts where ChatGPT recommends the brand.

Example:

• "Best CRM software" → recommended: yes/no
• "Alternatives to Salesforce" → included: yes/no

Sentiment / positioning

Measure whether AI describes the brand as:

• premium
• affordable
• innovative
• trustworthy
• difficult to use
• outdated

4. Add an executive reporting layer

For an agency, I would create a dashboard with:

Client summary page

• AI SOV score
• month-over-month movement
• competitor ranking
• biggest wins
• biggest gaps

Prompt-level analysis

Example:

Action recommendations

Turn findings into SEO/content tasks:

• Create comparison pages where competitors dominate
• Improve product documentation
• Build third-party authority signals
• Update FAQs
• Strengthen review/community presence

5. Build a monthly operating cadence

A practical agency workflow:

Weekly

• Automated prompt runs
• Competitor movement alerts

Monthly

• Client AI visibility report
• New prompt opportunities
• Citation gap analysis

Quarterly

• Refresh prompt universe
• Add emerging competitors
• Measure content impact

My recommended stack for a multi-industry client portfolio

Enterprise agency:

• Profound or similar enterprise AI visibility platform
• Data warehouse (BigQuery/Snowflake)
• Looker Studio/Tableau dashboards
• Slack/email alerts

Mid-size agency:

• Peec AI or Otterly.AI
• Airtable/Sheets prompt database
• Looker Studio client dashboards

Lean approach:

• Semrush/Ahrefs AI features
• Scheduled ChatGPT prompt testing
• Manual scoring framework

The biggest mistake is treating this like traditional SEO rank tracking. AI visibility is closer to market share of recommendations: you need to know which questions matter, who gets recommended, what sources influence the answer, and how that changes over time. Geo Software Rankings

For a portfolio of many brands across industries, I would prioritize multi-project support, competitor benchmarking, API/export capability, and white-label reporting over raw prompt volume.

My recommended stack for a multi-industry client portfolio

Enterprise agency:

• Profound or similar enterprise AI visibility platform
• Data warehouse (BigQuery/Snowflake)
• Looker Studio/Tableau dashboards
• Slack/email alerts

Mid-size agency:

• Peec AI or Otterly.AI
• Airtable/Sheets prompt database
• Looker Studio client dashboards

Lean approach:

• Semrush/Ahrefs AI features
• Scheduled ChatGPT prompt testing
• Manual scoring framework

The biggest mistake is treating this like traditional SEO rank tracking. AI visibility is closer to market share of recommendations: you need to know which questions matter, who gets recommended, what sources influence the answer, and how that changes over time.

For a multi-client agency or enterprise team, I’d recommend treating ChatGPT Share of Voice (SOV) as an AI visibility monitoring program rather than a simple rank tracker. The goal is to measure: “When customers ask ChatGPT questions in this category, how often does each brand appear, how prominently, and why?”

Several tools now exist specifically for this emerging AI visibility / GEO (Generative Engine Optimization) use case. Visibility.so DemandSphere

Recommended solution: AI Visibility Command Center

1. Use a dedicated AI visibility platform as the data layer

For an agency managing multiple brands, I would shortlist:

For a multi-client agency, I would lean toward DemandSphere or Visibility.so for reporting workflows, or build a custom reporting layer on top of an API-enabled provider. Visibility.so DemandSphere

2. Create a standardized prompt portfolio per client

Do not measure random ChatGPT queries. Build a controlled prompt universe.

Example:

Brand discovery prompts

• "What are the best CRM platforms for mid-sized companies?"
• "Which cybersecurity companies should enterprises consider?"
• "What are the leading skincare brands for sensitive skin?"

Comparison prompts

• "Compare Brand A vs Brand B"
• "What are alternatives to Brand A?"

Category education prompts

• "How does [solution category] work?"
• "What should buyers look for when choosing [category]?"

A typical client portfolio might include:

• 100–300 prompts
• 5–15 competitors
• Weekly monitoring
• Multiple AI engines (ChatGPT, Claude, Gemini, Perplexity)

3. Define the SOV metric clearly

A useful formula:

AI Share of Voice (%)

=
Brand mentions in tracked AI answers ÷ Total competitor brand mentions in those answers × 100

Track additional dimensions:

4. Build a client dashboard

A strong agency dashboard would show:

Executive view

• AI SOV score
• Month-over-month change
• Competitor movement
• Top opportunities

Brand view

Example:

Prompt view

Citation intelligence

Track:

• Which websites ChatGPT cites
• Which publications influence recommendations
• Missing authoritative sources

5. Add an optimization workflow

The monitoring tool should feed actions:

Low visibility → Diagnose → Fix → Re-measure

Examples:

My recommended stack for an agency managing 10–100 brands

Option A: Enterprise

• AI visibility platform: DemandSphere / Visibility.so
• BI layer: Looker Studio or Tableau
• SEO data: Ahrefs / Semrush
• Reporting: automated monthly client decks

Option B: Lean agency

• Visibility.so or Foglift
• Google Sheets/Airtable prompt database
• Looker Studio dashboards
• Monthly AI visibility reports

Option C: Build your own

• Prompt database
• Scheduled ChatGPT/API queries
• Response storage
• Brand/entity extraction
• SOV scoring model
• Dashboard layer

The key strategic point: do not measure “ChatGPT rankings”; measure competitive presence across buyer questions. AI answers vary by prompt and context, so the valuable asset is a repeatable prompt set, competitor baseline, and trend line over time. Foglift

For a marketing agency with multiple industries, I’d start with Visibility.so or DemandSphere, create a standardized prompt taxonomy, and package the output as an “AI Search Share of Voice Report” for clients. Visibility.so DemandSphere

• Which websites ChatGPT cites
• Which publications influence recommendations
• Missing authoritative sources

5. Add an optimization workflow

The monitoring tool should feed actions:

Low visibility → Diagnose → Fix → Re-measure

Examples:

My recommended stack for an agency managing 10–100 brands

Option A: Enterprise

• AI visibility platform: DemandSphere / Visibility.so
• BI layer: Looker Studio or Tableau
• SEO data: Ahrefs / Semrush
• Reporting: automated monthly client decks

Option B: Lean agency

• Visibility.so or Foglift
• Google Sheets/Airtable prompt database
• Looker Studio dashboards
• Monthly AI visibility reports

Option C: Build your own

• Prompt database
• Scheduled ChatGPT/API queries
• Response storage
• Brand/entity extraction
• SOV scoring model
• Dashboard layer

The key strategic point: do not measure “ChatGPT rankings”; measure competitive presence across buyer questions. AI answers vary by prompt and context, so the valuable asset is a repeatable prompt set, competitor baseline, and trend line over time.

For a multi-client environment (agency, consultancy, or enterprise team), I would treat ChatGPT Share of Voice (SOV) as an AI visibility intelligence program, not just a rank tracker. The solution should continuously test the prompts your customers ask, measure which brands ChatGPT recommends, and show where each client wins or loses against competitors.

A strong setup would look like this:

Recommended solution: AI Share of Voice monitoring platform + standardized prompt intelligence layer

1. Use a dedicated AI visibility monitoring platform

Shortlist tools based on your operating model:

For an agency managing dozens of brands, I would prioritize:

1. Multi-workspace/client separation
2. Unlimited competitor tracking
3. Prompt libraries
4. API/export access
5. Historical trend reporting
6. Evidence capture (the actual ChatGPT responses)

2. Build a repeatable prompt taxonomy per client

Avoid tracking generic prompts like:

"What are the best software companies?"

Instead create buyer-intent prompt clusters:

Category discovery

• "What are the best [category] providers?"
• "Who are the leading [industry] companies?"

Comparison

• "[Brand A] vs [Brand B]"
• "Alternatives to [competitor]"

Purchase intent

• "What [product/service] should a mid-sized company buy?"
• "Best [category] for enterprise teams"

Trust/reputation

• "Is [brand] reputable?"
• "What do customers say about [brand]?"

A typical client might have:

• 100–300 tracked prompts
• 5–10 competitors
• weekly or daily runs

3. Define the right SOV metrics

I would create a dashboard with:

AI Mention Share

Percentage of relevant ChatGPT answers mentioning the brand.

Formula:

Brand mentions ÷ Total brand mentions across competitors × 100

Recommendation Share

How often ChatGPT actively recommends the brand.

Example:

Position Share

Where the brand appears:

• #1 recommendation
• Top 3
• Mentioned but not recommended
• Not present

Citation Share

Which brands own the sources ChatGPT relies on.

Track:

• owned website citations
• review sites
• media mentions
• directories
• forums

Sentiment / framing

Measure whether ChatGPT describes the brand as:

• premium
• affordable
• innovative
• reliable
• enterprise-ready
• niche

4. Create an agency reporting layer

For multiple industries, I would build a client dashboard in something like:

• Looker Studio
• Tableau
• Power BI
• a custom BI layer

Each client gets:

Executive view

• AI SOV score
• trend vs last month
• competitor movement
• biggest visibility gaps

Marketing view

• prompts lost
• competitors winning
• missing content themes
• citation opportunities

SEO/content view

• URLs ChatGPT cites
• missing sources
• content recommendations

5. Add human review

Automated scores are useful, but AI responses change frequently. Have a monthly review process:

For each client:

1. Export top 20 losing prompts
2. Review actual ChatGPT responses
3. Identify why competitors appear: - stronger authority signals
4. better comparison pages
5. third-party reviews
6. structured content
7. more mentions across the web
8. Create optimization actions

My recommended operating model for a multi-industry agency

Stack:

• AI visibility platform (Profound / Peec AI / Ahrefs Brand Radar / Visibility.so)
• Prompt database (Airtable or similar)
• BI dashboard
• Monthly AI visibility report
• Content + digital PR workflow tied to lost prompts

The key metric I would put in front of clients is:

"When your customers ask ChatGPT a buying question, what percentage of those answers include your brand versus competitors?"

That is the closest AI-era equivalent to traditional search share of voice. Foglift RankScope

For a portfolio of 10–100 client brands, I would lean toward an agency-focused platform with API/export capability rather than building a crawler yourself.

My recommended operating model for a multi-industry agency

Stack:

• AI visibility platform (Profound / Peec AI / Ahrefs Brand Radar / Visibility.so)
• Prompt database (Airtable or similar)
• BI dashboard
• Monthly AI visibility report
• Content + digital PR workflow tied to lost prompts

The key metric I would put in front of clients is:

"When your customers ask ChatGPT a buying question, what percentage of those answers include your brand versus competitors?"

That is the closest AI-era equivalent to traditional search share of voice.

If you’re managing multiple client brands across different industries, I’d recommend treating this as an AI visibility / GEO monitoring program, rather than simply a “ChatGPT rank tracker.”

My recommendation: Profound for enterprise, Peec AI for agency scale

Based on the current 2026 landscape, I’d shortlist:

• tryprofound.com — best if you’re an agency/enterprise operation where depth of AI-search intelligence matters most. It is positioned around enterprise citation intelligence and prompt-volume data. AI Visibility Software
• peec.ai — particularly attractive for an agency managing many client accounts, with multi-client tracking and agency-oriented workflows. AI Visibility Software
• semrush.com — my choice if your agency already uses Semrush. It combines AI visibility with traditional SEO, tracks ChatGPT/Gemini/Perplexity and other AI surfaces, and supports enterprise product-line configurations. Semrush
• getlatentline.com — worth evaluating if you want a more evidence-oriented dashboard with prompt/answer/source-level data and explicit multi-project agency reporting. Latentline

What I would actually measure

For each client, create a fixed prompt universe rather than asking ChatGPT random questions.

For example, for a hypothetical CRM client:

• “What are the best CRM platforms for a 50-person SaaS company?”
• “HubSpot vs Salesforce for a growing B2B company”
• “Best alternatives to Salesforce”
• “What CRM should a startup use?”
• “Best CRM for enterprise sales teams”
• “Most affordable CRM with marketing automation”

Then track, every week:

This distinction is important: “we appear in 42% of answers” and “we own 42% of the category's recommendation share” aren't necessarily the same thing. A good platform should preserve the underlying prompt and answer evidence so the metric is auditable. Foglift Latentline

For a multi-industry agency, structure it like this

Agency → Client → Market → Topic cluster → Prompt → AI engine

For example:

Agency
├── Client A — SaaS
│   ├── CRM
│   ├── Marketing automation
│   └── Competitors
├── Client B — Healthcare
│   ├── Category questions
│   ├── Treatment/service questions
│   └── Competitors
└── Client C — Financial Services
    ├── Product questions
    ├── Comparison questions
    └── Competitors

Then give each client a dashboard showing:

AI SOV: 18% ↑ 4.2 pts
Mention rate: 37%
Recommendation rate: 24%
#1 competitor: 31% SOV
Biggest gain: “best ___ for SMBs”
Biggest loss: “___ alternatives”

That makes the output something a CMO can understand rather than an SEO-style report full of AI jargon.

One important caveat

I wouldn't report a single blended “ChatGPT SOV” across all clients or industries. Keep the prompt set, competitors, geography, language, model/surface and measurement window consistent within each benchmark. Otherwise SOV can move simply because the denominator changed. Foglift

And I'd monitor ChatGPT separately from Perplexity, Gemini, Claude and Google AI surfaces. Different engines can surface very different brands and sources, so a blended score can hide what's actually happening. Foglift

If this is for an agency

My practical shortlist would be:

1. Peec AI → best starting point for a multi-client agency
2. Profound → best if you need sophisticated enterprise intelligence
3. Semrush → best if you already have a substantial Semrush/SEO operation
4. Latentline → interesting alternative if evidence, projects and reporting are priorities

If you tell me roughly how many client brands you need to monitor (e.g. 10, 50, 200+) and whether you need white-label client reporting/API access, I can narrow this down to the best 2–3 solutions and compare pricing, prompt limits, engines, agency features, and reporting capabilities.

If you’re monitoring ChatGPT share of voice across multiple client brands and industries, I’d recommend using a dedicated AI-search visibility platform rather than building a scraper yourself.

My pick: Promptwatch

promptwatch.com is probably the best fit for an agency managing multiple client accounts.

It lets you:

• Track the same prompt sets across ChatGPT, Gemini, Claude, Perplexity and other AI engines.
• Measure brand mentions, visibility and share of voice against competitors.
• Organize monitoring by brand, market and language.
• Bulk-import prompts and generate prompts from search data, site content and competitor gaps.
• See the actual sources/citations behind AI answers.
• Track changes over time rather than treating a single ChatGPT response as meaningful.
• Manage multiple clients from a unified agency-oriented setup. Promptwatch Promptwatch

promptwatch.com

Strong alternative: OtterlyAI

otterly.ai would be my second choice, particularly if reporting and analytics are more important than optimization workflows.

It explicitly reports:

• Share of voice
• Brand coverage
• Average rank
• Sentiment
• Competitor visibility
• Citations and cited URLs
• Prompt-level performance

It tracks ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, with API/CSV/reporting capabilities. Otterly Otterly

How I'd structure the measurement

Don't simply track "Does ChatGPT mention Client X?"

For each client, build a category-specific prompt universe, for example:

Brand discovery
"Best [category] companies"
"Top [category] providers"

Commercial
"Best [category] for [use case]"
"Which [category] should I choose for [audience]?"

Comparison
"[Brand A] vs [Brand B]"
"Alternatives to [Brand A]"

Recommendation
"What are the best [category] products/services under $X?"

Problem/education
"How do I solve [customer problem]?"
"What should I look for when choosing [category]?"

Then run the same prompt set against ChatGPT and your competitors.

I'd make the core KPI:

AI Share of Voice = brand mentions / total tracked brand mentions

but keep several supporting metrics:

This distinction matters: coverage and share of voice aren't the same thing. A brand can appear in 40% of answers but have substantially less SOV if competitors are mentioned alongside it. Otterly makes this distinction explicitly. Otterly

For an agency, I'd build the reporting layer like this

Portfolio → Client → Industry/category → Prompt cluster → AI engine → Competitor

For example:

Agency
│
├── Client A — SaaS
│   ├── CRM
│   ├── Marketing automation
│   └── Sales software
│
├── Client B — Healthcare
│   ├── Telehealth
│   ├── Providers
│   └── Patient questions
│
└── Client C — Financial services
    ├── Credit cards
    ├── Personal loans
    └── Investing

Then give each client a monthly dashboard showing:

AI SOV: 18.4% ↑ 4.2pp

vs.

• Competitor A — 31.7%
• Competitor B — 24.1%
• Competitor C — 12.8%

followed by:

You are losing 14 high-value prompts to Competitor A.

That last layer is where the monitoring becomes useful rather than just another vanity metric.

One important caveat

Don't overreact to daily movements. AI answers can change because models, retrieval systems and source selection change. Promptwatch itself recommends looking at trends over weeks rather than interpreting an individual day's movement. Promptwatch

And I'd avoid making ChatGPT alone your definition of AI visibility. Recent changes in what ChatGPT cites—including a dramatic recent change in Reddit citations—illustrate how quickly the underlying ecosystem can shift. Business Insider

My shortlist

1. Promptwatch — best overall for an agency/multi-client operation.
2. OtterlyAI — best if you want strong SOV, sentiment, citation and reporting analytics.
3. Zumi — worth evaluating if you want a clean multi-brand/agency workspace and broad engine coverage. Zumi
4. Latentline — interesting if you care about evidence-backed answer-level measurement and agency projects. Latentline
5. Build your own — only if you need proprietary methodology, very large prompt volumes, or integration into an existing BI/data warehouse.

If this is for an agency, I'd start with Promptwatch and OtterlyAI head-to-head using 3–5 real clients from different verticals. The important test isn't the dashboard—it is whether they produce consistent, defensible SOV measurements across different industries and whether you can export the underlying prompt/answer/citation data for client reporting.

One important caveat

Don't overreact to daily movements. AI answers can change because models, retrieval systems and source selection change. Promptwatch itself recommends looking at trends over weeks rather than interpreting an individual day's movement. Promptwatch

And I'd avoid making ChatGPT alone your definition of AI visibility. Recent changes in what ChatGPT cites—including a dramatic recent change in Reddit citations—illustrate how quickly the underlying ecosystem can shift.

My shortlist

That last layer is where the monitoring becomes useful rather than just another vanity metric.

One important caveat

Don't overreact to daily movements. AI answers can change because models, retrieval systems and source selection change. Promptwatch itself recommends looking at trends over weeks rather than interpreting an individual day's movement.

If you’re monitoring ChatGPT share of voice (SOV) for multiple client brands across different industries, I’d treat this as an AI-search intelligence / GEO platform problem rather than a conventional SEO rank-tracking problem.

My recommendation: shortlist 3

1. zumihq.com — best overall for an agency

This is the one I’d evaluate first if you manage many client accounts.

Zumi explicitly supports agencies with separate workspaces for brands/regions, isolated prompts and competitors, historical tracking, and white-label PDF reporting. It also tracks up to nine AI surfaces, including ChatGPT, Gemini, Perplexity, Claude, Copilot, Grok, Google AI Overviews, AI Mode and DeepSeek. Zumi Zumi

Its SOV model is particularly useful because it isn't simply asking "was the brand mentioned?" It distinguishes between being the only brand mentioned and being one of several competitors mentioned. Zumi

Why I'd choose it:

• Multi-client / agency architecture
• Competitor SOV
• Prompt-level and category-level reporting
• Citation/source tracking
• Daily monitoring
• White-label reporting
• Broad AI-engine coverage
• Easy client-facing story: visibility → SOV → competitors → citations → actions

2. peec.ai — strong agency alternative

Peec is worth putting into the evaluation if agency workflow and scale are your primary concerns. A recent 2026 comparison specifically identifies it as a strong choice for agencies managing multiple clients, with nine-engine coverage and unlimited seats on its relevant plans. AI Visibility Software

I'd compare Peec against Zumi on:

• Number of clients/projects you can manage
• Prompt limits
• White-label capabilities
• API/export functionality
• Historical data retention
• Cost per tracked prompt

3. tryprofound.com — best if enterprise depth matters more than cost

If your clients include large enterprise brands and you need deep citation intelligence and large-scale AI-search data, Profound is the higher-end option. Current comparisons position it as one of the strongest enterprise choices, with broad AI-engine coverage and deeper intelligence. AI Visibility Software

I'd lean toward Profound if the deliverable is less "monthly agency report" and more enterprise AI-search intelligence platform.

How I'd actually measure ChatGPT SOV

The biggest mistake is defining SOV as simply:

"What percentage of ChatGPT answers mention my client?"

Instead, build a standardized prompt universe for each client.

For each brand, I'd track roughly:

Then run those prompts repeatedly and calculate:

Mention SOV

brand mentions / total brand mentions across tracked answers

But I'd also report:

• Recommendation SOV — how often the brand is actually recommended
• Top-3 SOV — how often it appears among the leading recommendations
• Average position
• Citation share — percentage of cited sources belonging to the brand
• Competitor SOV
• Positive/neutral/negative sentiment
• Prompt coverage — % of important prompts where the brand appears
• SOV by intent
• SOV by industry/category
• SOV by AI engine

That gives you a much more defensible client KPI than a single "AI visibility score."

The agency dashboard I'd build

I'd want one portfolio view like:

Agency AI Visibility

Then clicking a client should give you:

Client → Industry → Topic → Prompt → AI answer → Competitors → Citations

That last layer is critical. You don't just want to know that a competitor is beating your client—you want to know which websites and pieces of content ChatGPT is using to construct that answer.

Zumi, Visibility.so and similar platforms explicitly expose this type of citation/source intelligence. Zumi Visibility.so

One important caveat

Don't promise clients that "ChatGPT SOV" is an objective market-share number.

It's a measurement of a defined prompt set, and results can change as models and retrieval systems change. That's especially important right now: recent reporting has documented significant changes in which sources ChatGPT cites, including a sharp recent decline in Reddit citations. Business Insider

So I'd position the KPI as:

AI Share of Voice: percentage of tracked, commercially relevant prompts in which the client's brand is mentioned/recommended relative to its defined competitive set.

Then keep the prompt set, models, geography, language, competitors and methodology fixed enough to make month-over-month comparisons meaningful.

Bottom line

For a multi-industry client portfolio, my evaluation order would be:

1. Zumi — first demo; strongest fit for agency + multi-client reporting.
2. Peec AI — compare closely if agency scale/pricing is the priority.
3. Profound — consider for enterprise clients requiring deeper intelligence.

I would not build your own tracker initially unless you have unusual requirements. The difficult part isn't sending prompts to ChatGPT; it's maintaining consistent prompt sets, model coverage, answer capture, competitor/entity extraction, citation attribution, historical data and client reporting.

Agency AI Visibility

Then clicking a client should give you:

Client → Industry → Topic → Prompt → AI answer → Competitors → Citations

That last layer is critical. You don't just want to know that a competitor is beating your client—you want to know which websites and pieces of content ChatGPT is using to construct that answer.

Zumi, Visibility.so and similar platforms explicitly expose this type of citation/source intelligence. Zumi Visibility.so

One important caveat

Don't promise clients that "ChatGPT SOV" is an objective market-share number.

It's a measurement of a defined prompt set, and results can change as models and retrieval systems change. That's especially important right now: recent reporting has documented significant changes in which sources ChatGPT cites, including a sharp recent decline in Reddit citations. businessinsider.com

So I'd position the KPI as:

AI Share of Voice: percentage of tracked, commercially relevant prompts in which the client's brand is mentioned/recommended relative to its defined competitive set.

Then keep the prompt set, models, geography, language, competitors and methodology fixed enough to make month-over-month comparisons meaningful.

Bottom line

For a multi-industry client portfolio, my evaluation order would be:

Then clicking a client should give you:

Client → Industry → Topic → Prompt → AI answer → Competitors → Citations

That last layer is critical. You don't just want to know that a competitor is beating your client—you want to know which websites and pieces of content ChatGPT is using to construct that answer.

Zumi, Visibility.so and similar platforms explicitly expose this type of citation/source intelligence.

One important caveat

If you’re managing multiple client brands across different industries, I’d recommend using a dedicated AI-search visibility platform rather than trying to build a ChatGPT scraper/dashboard yourself.

My pick: OtterlyAI

OtterlyAI is probably the best starting point for an agency-style setup. It lets you define prompt sets for each brand, track mentions and citations, compare competitors, and calculate share of voice across ChatGPT and other AI search surfaces. It also supports reporting/export/API workflows that make it more practical for client reporting. Otterly Otterly

otterly.ai

How I'd structure it

For each client, create:

• Brand entity set: brand + products/services + major competitors
• 50–150 prompts representing actual customer questions
• Intent buckets: informational, comparison, commercial, transactional, local
• Industry-specific prompts: e.g. "best CRM for startups" vs. "best personal injury lawyer in Atlanta"
• Geography/language: where relevant
• Weekly/daily tracking depending on how volatile the category is

Then report four primary metrics:

I'd also track sentiment and citation sources, because "we're mentioned" isn't necessarily a win. Otterly specifically distinguishes coverage from share of voice and tracks citations, sentiment and competitive gaps. Otterly

The important part: don't use one universal SOV number

For an agency with clients across industries, I'd build a hierarchy like:

Client → Industry → Market → Intent → Prompt → AI engine

For example:

Nike → Running shoes → US → Commercial → "best running shoes for marathon training" → ChatGPT

Then calculate:

AI SOV = brand's weighted mentions ÷ total weighted competitor mentions

I'd weight prompts by business importance rather than treating every prompt equally. A high-value "best accounting software for a 50-person company" query should matter more to a B2B SaaS client than a generic informational query.

Alternatives worth evaluating

Peec AI is worth testing if you want a more marketing/agency-oriented analytics experience. Current comparisons generally position Peec as a stronger choice for marketing teams and agencies, while Otterly is more accessible for straightforward monitoring. Conbersa Arbling

Profound is the one I'd investigate for a large enterprise agency operation where sophisticated analytics, scale and enterprise workflows justify the additional complexity/cost. Recent comparisons consistently put it toward the enterprise end of the market. Baarely AnswerManiac

There's also a newer agency-focused category of tools emphasizing white-label reporting and multi-client management; current 2026 comparisons identify platforms such as Spotlight as specifically targeting agencies. Prism News

What I'd actually deploy

If this were my agency, I'd use:

OtterlyAI → centralized client SOV dataset → Looker Studio/BI dashboard → monthly client report

The key is to make the prompt taxonomy your proprietary asset. The monitoring platform is the data collection layer; your real competitive advantage becomes the methodology for deciding:

1. Which prompts matter?
2. Which competitors should be included?
3. How should SOV be weighted?
4. Which citations are driving visibility?
5. What changed since last month?
6. What action should the client take?

That gives you a much more defensible product than simply telling clients, "Your ChatGPT visibility is 12%."

One caveat: ChatGPT results can vary by personalization, location, conversation history, and whether web search is involved. Otterly itself notes that its monitoring aims to provide a more neutral baseline than an individual's personalized ChatGPT experience. Otterly So I'd call the metric "measured AI Share of Voice" rather than implying it represents every ChatGPT user.

If you tell me roughly how many client brands you manage (e.g. 10, 50, 500) and whether you need white-label client dashboards, I can recommend the most economical architecture and compare Otterly vs. Peec vs. Profound specifically for that scale.

For example:

Nike → Running shoes → US → Commercial → "best running shoes for marathon training" → ChatGPT

Then calculate:

AI SOV = brand's weighted mentions ÷ total weighted competitor mentions

I'd weight prompts by business importance rather than treating every prompt equally. A high-value "best accounting software for a 50-person company" query should matter more to a B2B SaaS client than a generic informational query.

Alternatives worth evaluating

Peec AI is worth testing if you want a more marketing/agency-oriented analytics experience. Current comparisons generally position Peec as a stronger choice for marketing teams and agencies, while Otterly is more accessible for straightforward monitoring. Conbersa Arbling

Profound is the one I'd investigate for a large enterprise agency operation where sophisticated analytics, scale and enterprise workflows justify the additional complexity/cost. Recent comparisons consistently put it toward the enterprise end of the market. Baarely AnswerManiac

There's also a newer agency-focused category of tools emphasizing white-label reporting and multi-client management; current 2026 comparisons identify platforms such as Spotlight as specifically targeting agencies. Prism News

What I'd actually deploy

If this were my agency, I'd use:

OtterlyAI → centralized client SOV dataset → Looker Studio/BI dashboard → monthly client report

The key is to make the prompt taxonomy your proprietary asset. The monitoring platform is the data collection layer; your real competitive advantage becomes the methodology for deciding:

1. Which prompts matter?
2. Which competitors should be included?
3. How should SOV be weighted?
4. Which citations are driving visibility?
5. What changed since last month?
6. What action should the client take?

That gives you a much more defensible product than simply telling clients, "Your ChatGPT visibility is 12%."

One caveat: ChatGPT results can vary by personalization, location, conversation history, and whether web search is involved. Otterly itself notes that its monitoring aims to provide a more neutral baseline than an individual's personalized ChatGPT experience. So I'd call the metric "measured AI Share of Voice" rather than implying it represents every ChatGPT user.

If you're monitoring multiple client brands across industries, I’d treat this as an agency-level AI visibility / Share of Voice (SOV) problem, not simply “track ChatGPT mentions.”

My recommendation: start with OtterlyAI, then benchmark against Profound

OtterlyAI is probably the best practical starting point for a multi-client portfolio. It monitors brand mentions and citations across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini and Copilot, supports competitive benchmarking, and exposes an API. It also explicitly calculates AI Share of Voice from tracked prompts. Otterly

otterly.ai

I'd shortlist the market like this:

Recent comparisons similarly put Otterly, Peec and Profound among the main platforms, with Profound skewing enterprise and Otterly toward accessible monitoring. Conbersa Arbling

tryprofound.com
peec.ai
zumihq.com

But don't measure SOV as simply "did ChatGPT mention us?"

For an agency, I'd build a standardized AI SOV scorecard for every client.

For each brand, create a prompt universe such as:

• Category discovery: “What are the best [category] companies?”
• Comparison: “[Brand A] vs [Brand B]”
• Problem/solution: “What should I use for [customer problem]?”
• Buying intent: “What are the best [product] for [use case]?”
• Alternatives: “What are alternatives to [competitor]?”
• Local intent: “Best [service] in [city]”
• Industry-specific questions: questions prospects actually ask before buying
• Brand-specific: “Is [brand] good?”, “[brand] reviews”, etc.

Then run the same prompt panel repeatedly and calculate:

AI Share of Voice = brand's weighted appearances ÷ total weighted brand appearances

I'd actually maintain three separate metrics:

1. Mention rate — % of prompts where the brand appears.
2. Weighted SOV — accounts for prominence/position and whether you're the primary recommendation.
3. Citation share — % of cited sources/URLs attributable to the brand or its owned ecosystem.

That's important because a brand appearing as the #1 recommendation shouldn't necessarily count the same as being buried in a list of 10 alternatives. Some newer platforms explicitly distinguish prominence and competitive SOV this way. Zumi AnswerMonk

The multi-client architecture I'd use

Rather than creating an ad-hoc dashboard for every client, establish one common data model:

Agency → Client → Industry → Market → Prompt set → AI engine → Run → Response → Brand mentions → Citations → SOV

For every client, store:

• 50–200 high-value prompts
• 5–15 competitors
• Geographic/language variants where relevant
• ChatGPT + other major answer engines
• Raw AI responses
• Mention position
• Sentiment / recommendation quality
• Cited domains and URLs
• SOV
• Change versus previous period

That gives you a portfolio-level view as well as client-level reporting.

For example:

Agency AI Visibility

Client A: 31% SOV ↑ 6 pts
Client B: 14% SOV ↓ 3 pts
Client C: 42% SOV ↑ 11 pts
Client D: 8% SOV →

Biggest opportunity: Client B — competitor X owns 47% of citations for commercial-intent prompts.

That is much more useful than a dashboard saying “your brand was mentioned 237 times.”

One important caveat

ChatGPT SOV is inherently volatile. The answers can change based on search/retrieval behavior, prompt wording, location, personalization and model changes. Recent reporting, for example, showed substantial changes in which external domains ChatGPT cited over a matter of weeks. Axios

So I'd avoid reporting:

“Your ChatGPT SOV is 27.4%.”

as though that's an objective market statistic.

Instead report:

“27.4% SOV across our standardized 150-prompt benchmark, measured weekly.”

That makes the methodology reproducible and gives the number meaning.

What I'd buy

If you're an agency with perhaps 10–100+ brands, I'd do a short proof-of-concept with OtterlyAI and Profound, using the same 3–5 client brands and identical prompt sets. Don't choose based on the prettiest dashboard.

Score them on:

• Multi-client/workspace management
• Prompt volume
• ChatGPT methodology
• Other AI-engine coverage
• SOV methodology
• Competitor tracking
• Citation extraction
• Historical data
• API/export
• White-label reporting
• Scheduled reports
• Cost per client/prompt
• Ability to segment by industry, country and intent

My default choice: OtterlyAI for a scalable agency monitoring program; Profound if you're dealing with enterprise clients and need substantially deeper intelligence/reporting. Current market comparisons support that general positioning. Conbersa AnswerManiac

If you're building this as an agency product/service, I'd go one step further: use one of these platforms as the data collection layer, then build your own cross-client SOV dashboard and standardized methodology on top. That gives you a proprietary “AI Share of Voice” benchmark rather than reselling somebody else's dashboard.

ChatGPT SOV is inherently volatile. The answers can change based on search/retrieval behavior, prompt wording, location, personalization and model changes. Recent reporting, for example, showed substantial changes in which external domains ChatGPT cited over a matter of weeks. axios.com

So I'd avoid reporting:

“Your ChatGPT SOV is 27.4%.”

as though that's an objective market statistic.

Instead report:

“27.4% SOV across our standardized 150-prompt benchmark, measured weekly.”

That makes the methodology reproducible and gives the number meaning.

What I'd buy

If you’re managing multiple client brands across different industries, I’d recommend otterly.ai as the best starting point, with tryprofound.com as the stronger enterprise alternative.

My recommendation: OtterlyAI

It maps particularly well to an agency/multi-client use case:

• Multi-brand workspaces: separate clients into workspaces, each with its own brands, competitors, prompts and GEO audits. Otterly Otterly
• ChatGPT monitoring: tracks whether brands are mentioned, their visibility/share of voice, sentiment, ranking and citations. Otterly
• Competitive SOV: you can measure your client's share of AI answers against the competitors appearing for the same prompts. Otterly
• Cross-industry flexibility: prompt sets can be customized by client rather than relying on one universal keyword taxonomy.
• Beyond ChatGPT: it also monitors Google AI Overviews/AI Mode, Gemini, Perplexity, Copilot and Claude, which is important because AI-search behavior is fragmented. Otterly
• Reporting/API: CSV exports, Looker Studio integration and an API make it much easier to roll client data into your own reporting stack. Otterly Otterly

How I'd structure the measurement

Don't use a single "ChatGPT share of voice" number per client. Build a prompt-based SOV model:

That distinction between coverage and share of voice is important: coverage tells you whether you're appearing, while SOV tells you how much of the competitive conversation you're capturing. OtterlyAI explicitly supports both measurements. Otterly

For an agency, I'd use this operating model

1. Create an individual workspace per client.

Each gets:

• 50–200 priority prompts
• 5–10 competitors
• relevant product/service entities
• target geography
• industry/topic categories

2. Organize prompts by customer journey.

For example, for a SaaS client:

• Category discovery — "best CRM for a 50-person company"
• Problem — "how do I manage leads across multiple sales teams?"
• Comparison — "Salesforce vs HubSpot alternatives"
• Vendor selection — "best CRM for healthcare companies"
• Product-specific — "HubSpot alternatives with..."
• Brand — "Is [client] a good CRM?"

This gives you a much more meaningful SOV metric than simply tracking the client's name.

3. Report four headline KPIs.

I'd put these on the first page of every client report:

AI Visibility: 38%
AI Share of Voice: 21%
Competitive position: #3
Citation share: 14%

Then show movement versus the previous month.

4. Break SOV down by topic.

This is where the data becomes actionable. For example:

Now the client can see where they are losing AI visibility, rather than being told that their overall "AI score" went from 34 to 37.

When I'd choose Profound instead

I'd shortlist tryprofound.com if you're operating at a larger enterprise/agency scale and want a more sophisticated platform.

Profound's Answer Engine Insights tracks visibility, SOV, sentiment, positioning and citations, while also allowing segmentation by topics, regions and audience personas. It queries the consumer-facing AI experiences rather than simply relying on model APIs. Profound Profound Knowledge Base

It also has prompt-volume data, agent/crawler analytics and optimization workflows, making it more of a full AEO/GEO platform than purely a monitoring dashboard. Profound Profound

The shortlist I'd use

For an agency managing many diverse clients:
1. OtterlyAI — best fit for multi-brand monitoring/reporting.

For larger enterprise clients and deeper AEO intelligence:
2. Profound — stronger if budget and sophistication are less constrained.

The bigger strategic point is that ChatGPT SOV should be a standardized measurement framework you own, not just a score you take from a vendor. AI answers are volatile—the recent change in Reddit's share of ChatGPT citations is a good example—so I'd retain the underlying prompt/response data and calculate trends over time rather than treating any vendor's proprietary "visibility score" as the source of truth. Axios

If you're building this for an agency with, say, 20–100+ client brands, I can also lay out the exact data architecture + prompt taxonomy + SOV formula + Looker Studio dashboard structure I'd use.

If you’re managing multiple client brands across different industries, I’d treat ChatGPT Share of Voice (SOV) as an AI-search measurement program, not simply a rank tracker.

My recommendation: use OtterlyAI as the default platform

otterly.ai is the strongest fit I’d shortlist for an agency/multi-client setup right now.

It can monitor brand mentions and domain citations across ChatGPT plus other major AI search surfaces, benchmark competitors, track sentiment, and report visibility over time. It also supports multiple markets/languages and has an API, which becomes useful if you want to aggregate client data into your own reporting layer. Otterly Otterly

For context, the market has moved quickly: current tools generally work by running a controlled prompt set against AI engines, parsing mentions/citations, and calculating visibility/SOV against competitors. Arbling

How I'd structure the solution

For each client, create a standardized measurement framework:

• 50–200 high-value prompts based on actual customer intent
• 5–10 direct competitors
• Brand mention rate
• AI Share of Voice
• Citation/share of cited domains
• Position/order in recommendations
• Sentiment
• Product/service/category association
• Prompts won vs. lost
• Competitor displacement
• Cited URLs/domains
• Change over time
• Engine/platform breakdown

I'd calculate SOV roughly as:

Brand SOV = your brand mentions ÷ total competitor + brand mentions

But I'd also maintain a separate Citation SOV, because being mentioned by ChatGPT and actually being supported by a citation to your website are materially different outcomes.

Don't use one generic prompt set across industries

This is probably the most important part.

For a SaaS client, prompts might be:

• "Best project management software for a 50-person remote company"
• "Alternatives to [competitor]"
• "Best [category] software for enterprise"
• "What should I consider when choosing [category]?"

For a healthcare brand, you'd build a completely different taxonomy.

I'd organize prompts into:

1. Category discovery — "best X"
2. Problem/need — "how do I solve X?"
3. Comparison — "X vs Y"
4. Alternative — "alternatives to X"
5. Commercial intent — "best X for [audience]"
6. Brand-specific — "is [brand] good?"
7. Reputation — "what do people think of [brand]?"
8. Local/market-specific — where relevant

That makes the resulting SOV much more meaningful than simply asking ChatGPT whether it knows the brand.

The agency dashboard I'd build

I'd have one portfolio-level dashboard and one dashboard per client.

Portfolio view

Then drill down from client → industry → prompt cluster → individual prompt → ChatGPT response → citations.

That gives an agency something much more useful than "your ChatGPT ranking went from #4 to #2."

The competitive set I'd evaluate

I would run a short bake-off between:

• OtterlyAI — my default recommendation for a multi-client agency; broad engine coverage, competitive benchmarking, citation tracking, sentiment, API and agency-oriented functionality. Otterly Otterly
• Profound — worth testing if you're operating at enterprise scale and want deeper analytics/governance. Current comparisons position it at the enterprise end of the market. Loudmink Mention Radar
• Peec AI — worth testing if you prioritize clean AI-visibility analytics and flexible client-by-client monitoring. Conbersa Mention Radar
• Scrunch — interesting if you want to connect monitoring with the content/optimization workflow rather than just measurement. Baarely

I wouldn't choose based on the vendor's claimed "AI visibility score." Run the same 100–200 prompts through each platform and compare the raw observations.

One important caveat

Don't treat ChatGPT SOV as a stable equivalent of Google rankings.

AI answers can change substantially based on query wording, model, search/retrieval state, geography, language, time, and the sources available to the model. Recent data illustrates how volatile the citation ecosystem can be—for example, Axios reported a significant change in Reddit's share of ChatGPT citations over a matter of weeks. Axios

So I'd report:

"AI visibility across a controlled prompt set"

rather than claiming:

"This is the percentage of all ChatGPT searches where the brand appears."

The latter is not realistically measurable.

What I'd buy

If you're an agency with ~10–100 clients: start with OtterlyAI, establish a standardized prompt/SOV methodology, and use its API/export capability to consolidate the portfolio into your own BI layer. Otterly

If you're an enterprise agency with hundreds of brands or very sophisticated reporting requirements: put Profound vs. OtterlyAI through a structured proof-of-concept before committing.

And I'd make the real deliverable "AI Share of Voice + why we're winning/losing + what to do about it", rather than simply a monitoring dashboard. That's where the measurement becomes commercially valuable.

If you’re monitoring multiple client brands across different industries, I’d recommend using a dedicated AI visibility / GEO platform, rather than trying to build a ChatGPT scraper yourself.

My recommendation: Peec AI for the agency/multi-client use case

Peec AI is probably the best starting point if your primary goal is repeatable share-of-voice reporting across a portfolio of brands. Current comparisons consistently position Peec as particularly suited to marketing teams/agencies, with competitor benchmarking and AI visibility analytics as core capabilities. GitHub AnswerManiac

I would structure your monitoring like this:

• One workspace/project per client
• 20–100 buyer-intent prompts per client, segmented by: - Category discovery — “best CRM for a 50-person company”
• Comparison — “Salesforce alternatives”
• Problem/solution — “how to reduce SaaS churn”
• Commercial — “best [product] for enterprise”
• Brand — “What is [brand]?”
• Track brand + competitors for every prompt.
• Measure: - AI Share of Voice
• Mention rate
• Position/rank in the answer
• Citation rate
• Sentiment/recommendation
• Competitor SOV
• Sources/domains being cited
• Break everything down by client → industry/topic → prompt → AI engine.

That last dimension is important. I wouldn't report a single blended "ChatGPT SOV" number to clients. AI answers vary substantially by engine; research published this year suggests that brands frequently appear on one AI engine but not another. Machine Relations

The shortlist I'd evaluate

There is considerable feature overlap among the major platforms: mention tracking, competitive SOV, citations, prompt monitoring and multiple AI engines are now fairly standard. GitHub Conbersa

peec.ai
tryprofound.com
otterly.ai
promptwatch.com
visibility.so

How I'd build the client reporting layer

The important thing is not to make the vendor's "visibility score" your KPI. Create your own standardized metric.

For example:

AI SOV = brand mentions / total competitor + brand mentions

Then add a weighted version:

Weighted SOV = Σ (brand position weight × brand mention) / Σ (all competitor position weights)

This lets you distinguish:

"Brand appears in 40% of answers"

from:

"Brand is the #1 recommendation in 40% of answers."

Those are very different commercial outcomes.

I'd also maintain three separate scores:

1. Presence — Did ChatGPT mention the brand?
2. Prominence — Where/how strongly was it recommended?
3. Authority — Did ChatGPT cite the client's site or third-party sources supporting the brand?

That gives you a much more defensible client story than "your AI visibility went from 31 to 37."

One important caveat

Don't measure only questions that contain the client's brand. Those artificially inflate visibility. The valuable dataset is predominantly unbranded prompts representing real prospective customers. Some current AI-monitoring products explicitly distinguish these prompt types because otherwise the headline visibility number can be misleading. PageLens

And don't treat ChatGPT as a static search engine. Its answers can change between runs, so you want repeated measurements over time, not a one-off query. Siftly

If this is for an agency

If you're managing, say, 10–100+ client brands, I'd actually evaluate the vendors specifically on these five things before signing:

• Multi-client / multi-workspace management
• White-label reporting
• API/export access
• Prompt volume pricing, rather than just number of brands
• Historical answer-level data, so you can show why SOV changed

For an agency, those can matter more than whether a platform supports 6 versus 8 AI engines.

My practical choice: start with Peec for the core measurement layer, run a representative 2–3 client pilot, and benchmark it against Profound if you have enterprise clients. If Peec's agency workflow doesn't fit your multi-client reporting requirements, I'd move to a more enterprise-oriented platform rather than building the infrastructure immediately.

If you tell me roughly how many clients/brands you manage (e.g. 10, 50, 200) and whether you need white-label dashboards/API, I can narrow this to the best 2–3 options and lay out the estimated monthly cost + recommended architecture.

AI SOV = brand mentions / total competitor + brand mentions

Then add a weighted version:

Weighted SOV = Σ (brand position weight × brand mention) / Σ (all competitor position weights)

This lets you distinguish:

If you’re monitoring multiple client brands across different industries, I’d treat this as an AI search intelligence / GEO measurement problem rather than simply “ChatGPT mention tracking.”

My recommendation: Profound for an agency-style, multi-brand program

tryprofound.com would be my first platform to evaluate if you need a scalable solution across many clients.

Why:

• Multi-industry coverage: Profound’s current index spans 50+ industries and reports on major answer engines. Its latest index is based on 1.9B+ real-user conversations. Profound
• Competitive SOV: You can benchmark a client against its competitors rather than merely counting brand mentions.
• Prompt-level intelligence: This is important because “Nike mentioned” isn't nearly as useful as knowing which buyer questions Nike wins and why.
• Citation/source intelligence: You want to know which publishers, review sites, Reddit threads, product pages, etc. are influencing the answers—not just whether a client appeared.
• Enterprise orientation: More appropriate for managing a portfolio of brands than a lightweight single-brand tracker.

One caveat: I would not report a single blended “AI SOV” number across all engines. Recent research suggests substantial divergence between engines; one analysis found 77% of brands were cited by only one engine. Machine Relations

The measurement framework I'd use

For every client, build a standardized prompt universe:

Then track, per prompt × engine × client:

1. Mention rate — % of answers mentioning the client.
2. Share of voice — client's share of all tracked competitor appearances.
3. Recommendation rate — % of answers actually recommending the brand.
4. Position/prominence — where the brand appears in the answer.
5. Sentiment / narrative — what the model says about the brand.
6. Citation rate — how often the client's owned/earned sources are cited.
7. Citation share — which sources competitors are getting cited from.
8. Competitor displacement — prompts where a competitor appears but the client doesn't.
9. Accuracy — whether the AI's claims about the client are correct.
10. Trend — 7/30/90-day movement.

That gives you something much more actionable than “ChatGPT mentioned Client X 42% of the time.”

If you want alternatives

I'd put these on the shortlist:

• semrush.com — particularly attractive if your agency already runs SEO through Semrush and wants AI visibility integrated into the existing workflow. Its current AI visibility offering covers ChatGPT, Google AI Mode, Perplexity and similar platforms. Semrush
• zumihq.com — interesting if you want broad engine coverage. It currently advertises monitoring across up to nine engines and explicitly separates mention rate, SOV, answer position and citations. Zumi
• visibility.so — worth considering for a simpler, monitoring-focused implementation across ChatGPT, Claude, Gemini and Perplexity, with prompt-level answers and competitor SOV. Visibility.so
• chatbenchmark.com — useful if your emphasis is straightforward brand/competitor SOV, sentiment and citation monitoring across multiple AI platforms. ChatBenchmark

One important architectural decision

For an agency managing, say, 50–500 brands, I'd avoid making the vendor dashboard your actual reporting layer.

I'd structure it as:

AI visibility platform → API/export → central data warehouse → client-specific dashboards

with a common data model:

Client → Brand → Industry → Market → Prompt Set → Prompt → Engine → Run → Brand Mention → Competitors → Citation → Sentiment → Position

That lets you produce both:

• Client report: “Your AI SOV increased from 18% → 27%.”
• Portfolio report: “Retail clients average 31% SOV; healthcare averages 19%.”
• Industry benchmark: “Top-performing brands in SaaS are winning comparison prompts but losing problem-oriented prompts.”
• Action report: “These 14 prompts are responsible for 62% of your lost SOV, and these 8 cited sources represent the biggest opportunities.”

What I would buy

If this is an agency/consultancy use case: start with Profound as the enterprise benchmark, then run a proof-of-concept against Semrush and Zumi.

Don't select based primarily on number of AI engines. The more important test is whether the platform can reliably answer:

“For each client, which commercially important questions are we losing to which competitors, what sources are causing us to lose, and what changed since last month?”

That's the difference between an AI-monitoring dashboard and a genuinely useful AI share-of-voice intelligence system.

Yes. If your goal is agency-style monitoring of ChatGPT share of voice across many client brands and industries, I’d use a dedicated AI-search visibility platform rather than trying to build this from raw ChatGPT queries.

My recommendation: Indaeo

Indaeo is the closest fit if you manage a portfolio of clients. It is explicitly positioned for digital agencies and lets you manage multiple brands from one workspace, with separate prompt sets, competitors, dashboards, and reports. It monitors ChatGPT, Gemini, Perplexity and Claude, and supports white-label reporting on higher tiers. indaeo.com

For an agency, that's important because you don't just want "does Brand X appear?" You want a repeatable system like:

Client → industry → customer personas → prompt universe → competitors → AI responses → SOV → citations → recommendations → client report

What I would measure

Don't make "mentions" your primary KPI. I'd create a ChatGPT Share of Voice score around:

This distinction matters because a brand appearing as the fifth option in an answer isn't equivalent to being ChatGPT's first recommendation. Platforms such as Zumi explicitly break visibility into mention rate, SOV, position and citation signals. www.zumihq.com

For multiple industries, build a prompt taxonomy

I would not use the same generic 20–50 prompts for every client.

For each client, generate prompts across roughly:

1. Category discovery

"Best accounting software for a 50-person company"

2. Commercial intent

"Best accounting software for growing SaaS companies"

3. Problem/solution

"How should a startup manage international payroll?"

4. Comparison

"X vs Y vs Z"

5. Alternative

"Best alternatives to X"

6. Persona-specific

"Best cybersecurity platform for healthcare organizations"

7. Location-specific where relevant

8. Brand-specific

"What is X known for?"

9. Reputation

"Is X a good choice for enterprise customers?"

10. Competitor displacement

"What should I consider instead of X?"

Then track the same prompt set consistently over time. That's much more defensible than randomly asking ChatGPT questions every week.

Best alternatives I'd evaluate

Promptwatch is probably my second choice for an agency. It specifically supports agencies and tracks visibility, SOV, position, citations and sentiment across ChatGPT and other AI engines. promptwatch.com

Promptwatch

Zumi is particularly interesting if you want a more sophisticated enterprise-wide measurement layer. It supports up to nine AI engines and explicitly supports agencies with isolated client workspaces and white-label reporting. www.zumihq.com

Zumi

Spektriq is worth looking at if you want a relatively straightforward agency/enterprise package: its agency offering advertises 20 brands, 150 keywords per brand, 25 competitors, daily scans, SOV, alerts, white-label reporting and API access. www.spektriq.com

Spektriq

One important caveat

I'd avoid positioning this internally as "ChatGPT rankings." AI responses are variable, and the underlying landscape changes. Recent reporting, for example, showed ChatGPT's citation mix changing materially over just a few weeks. www.axios.com

Instead, report:

"AI Share of Voice across a controlled set of high-intent customer prompts."

That gives you a much more reproducible metric.

If I were building this for an agency

I'd choose Indaeo or Promptwatch, then create a standardized reporting framework:

Portfolio dashboard

• Overall AI SOV
• SOV by client
• SOV by industry
• MoM change
• Biggest winners/losers
• Competitive displacement opportunities

Client dashboard

• ChatGPT SOV
• Competitor SOV
• Visibility by prompt category
• Top/bottom prompts
• Average recommendation position
• Citation sources
• Missing citation opportunities
• Accuracy/sentiment issues

Monthly client report

• "Your ChatGPT SOV increased from 18% → 27%"
• "You overtook Competitor A on 14 high-intent prompts"
• "Competitor B is winning 11 prompts because ChatGPT repeatedly cites three third-party sources"
• "Here are the 5 content/PR/SEO actions most likely to close the gap"

That last layer is where the product becomes much more valuable than simply showing a dashboard.

Bottom line: for a multi-client, multi-industry agency, I'd start with Indaeo, benchmark it against Promptwatch, and consider Zumi if you're moving toward larger enterprise clients. If your eventual goal is to own the SOV methodology/data rather than depend on a SaaS vendor, I can also lay out a recommended technical architecture for building your own ChatGPT SOV monitoring system (prompt database → automated runs → response parsing → SOV calculation → citations → Looker/Power BI dashboard).

If you’re managing multiple client brands across different industries, I’d treat this as an AI-search/GEO measurement platform problem rather than a simple “ChatGPT rank tracker.”

My shortlist

What I'd choose

For an agency with, say, 20–100+ brands, I'd start with Gumshoe and Zumi.

The important distinction is that you don't want to measure simply:

“Did ChatGPT mention Brand X?”

You want a repeatable share-of-voice methodology:

Client → industry → buyer personas → prompt universe → AI engines → responses → brand mentions → position/prominence → citations → competitors → SOV trend

For example, for a SaaS client you might monitor 100–300 prompts around:

• “best project management software”
• “alternatives to [competitor]”
• “best software for enterprise teams”
• “tools for [specific use case]”
• “compare [client] vs [competitor]”
• industry-specific informational questions

Then calculate something like:

AI SOV = client brand mentions ÷ total brand mentions across the competitive set

I'd also track mention rate, average position, sentiment/recommendation, citation rate, cited domains, competitor displacement, and SOV by prompt category. AnswerMonk's definition is a useful illustration of this distinction: appearance rate tells you whether you're present, whereas SOV tells you how much of the recommendation space you're capturing. www.answermonk.ai

One important caveat

Don't make ChatGPT alone your KPI.

ChatGPT is important, but the landscape is now fragmented across ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews/AI Mode, etc. Several of the platforms above therefore measure multiple engines. www.zumihq.com

I'd make the executive KPI:

AI Search Share of Voice

…and provide a drill-down into ChatGPT SOV, Perplexity SOV, Gemini SOV, etc.

If you're building this into an agency offering

I'd structure the client dashboard around just 5 numbers:

1. AI SOV — % of competitive mentions
2. Visibility rate — % of tracked prompts where client appears
3. Avg. recommendation position
4. Citation share — how often the client's content is used as a source
5. SOV change — WoW/MoM

Then give the strategist the detailed prompt/response/citation data underneath.

My recommendation: shortlist Gumshoe, Zumi, and Tower GEO, run the same 50–100 prompts for 2–3 very different clients through each, and compare data quality before committing. The biggest issue isn't dashboard UX—it's whether the platform's sampling methodology produces stable, reproducible SOV measurements across industries and over time.

If you're managing multiple client brands across different industries, I’d recommend treating ChatGPT Share of Voice as an agency-level AI visibility program, rather than simply buying a brand-monitoring dashboard.

My recommendation: start with Zumi or Otterly, and benchmark against Semrush

1. Zumi — strongest fit if you're an agency

• Designed for brands and agencies, with separate workspaces for clients.
• Tracks ChatGPT plus Gemini, Perplexity, Claude, Copilot, Grok, Google AI Overviews/AI Mode, etc.
• Supports competitor comparisons, prompt-level tracking, daily updates and white-label PDF reporting. www.zumihq.com
• Particularly attractive if you want one operating system across many clients rather than one dashboard per brand.

Zumi

2. Otterly — best if you want something easy and relatively inexpensive

• Tracks ChatGPT, Perplexity, Google AI, Gemini, Copilot and Claude.
• Has brand coverage, competitor benchmarking, prompt/funnel segmentation and an API.
• Its current site says pricing starts at $29/month, making it useful for piloting across a portfolio. otterly.aiwww.zumihq.com

Otterly AI

3. Semrush Enterprise AIO — best if clients already live in Semrush
Semrush now has an explicit AI Share of Voice metric. Its methodology incorporates mentions and position, and for ChatGPT also incorporates topic search volume. www.semrush.com

Semrush

4. Profound — consider it for large enterprise clients
Profound is one of the deeper enterprise-oriented options, particularly if you need sophisticated dashboards and broad AI-search intelligence. Current comparisons put it at the enterprise end of the market. loudmink.ai

Profound

The important part: define your own "ChatGPT SOV"

I wouldn't blindly accept the vendor's headline SOV number. For each client, create a standardized prompt universe:

Then report:

AI Share of Voice = client mentions / total brand mentions across the competitive set

But I'd add several metrics beside it:

• Mention rate — % of prompts where the client appears
• Position — where the client appears in the recommendation
• Citation share — % of cited sources belonging to the client
• Sentiment/recommendation quality
• Competitor SOV
• Prompt-level wins/losses
• Source/citation share — which websites are causing competitors to appear
• Trend over time

This distinction matters: a brand can have a decent mention rate but be consistently listed fourth or fifth, while a competitor dominates the first recommendation. Semrush, for example, explicitly incorporates position into its SOV methodology. www.semrush.com

How I'd build this for an agency

I'd create a three-level reporting structure:

Portfolio → Client → Prompt

Portfolio dashboard

• Total AI SOV across clients
• SOV change MoM
• Clients gaining/losing visibility
• Biggest competitor movements
• AI engines with the largest changes

Client dashboard

• Overall ChatGPT SOV
• SOV by topic/category
• SOV vs competitors
• Mention rate
• Average position
• Citation share
• Top winning/losing prompts

Prompt dashboard

• Exact question
• ChatGPT response
• Whether client was mentioned
• Position
• Competitors mentioned
• Sources cited
• Recommended action

That last layer is crucial. Don't just show clients that their SOV went from 12% → 17%; show them why and what to do next.

What I'd buy

If this is for an agency with, say, 10–100+ client brands, my shortlist would be:

Zumi → Otterly → Semrush Enterprise AIO → Profound

I'd run a 2–4 week bake-off using the same 50–100 prompts for 3 representative clients—ideally one B2B, one B2C and one highly competitive category.

The winner shouldn't be the tool with the prettiest SOV chart. Test:

1. Can it isolate clients cleanly?
2. Can you customize prompts by industry?
3. Can it reproduce the same measurements over time?
4. Can you see the actual ChatGPT responses?
5. Can you identify why a competitor wins?
6. Can you export/API the data?
7. Can you white-label client reports?
8. Does its SOV methodology make statistical sense?
9. Can it distinguish branded vs non-branded prompts?
10. Can you scale the cost predictably as client count grows?

My default choice today would be Zumi for an agency-oriented implementation, with Otterly as the lower-cost alternative. Zumi explicitly supports multi-client workspaces and white-label reporting, while Otterly has particularly good breadth and a public API. otterly.aiwww.zumihq.com

If you're planning to build this into your agency's reporting stack, I can also map out a concrete multi-client ChatGPT SOV architecture—prompt taxonomy, KPI formula, dashboard schema, data pipeline, and recommended tool/API stack.

If you’re managing multiple client brands across different industries, I’d recommend buying an AI visibility/GEO monitoring platform rather than building your own ChatGPT tracker—at least initially.

My recommendation: use Profound as the enterprise benchmark

Profound would be my first platform to evaluate if you’re building an agency-wide solution. Current comparisons consistently position it toward enterprise use, with deep prompt, citation, competitive, and cross-engine analytics. technologyadvice.com

The important distinction is that you shouldn't measure simply “how often ChatGPT mentions Client X.” You want a repeatable Share of AI Voice (SAIV) methodology.

For each client, create a prompt universe such as:

• Category discovery: “What are the best [category] companies?”
• Commercial: “What [product/service] should I use for [need]?”
• Comparison: “[Brand A] vs [Brand B]”
• Problem/solution: “How should a [persona] solve [problem]?”
• Local: “Who provides [service] in [market]?”
• Industry authority: “Who are the leading [category] companies?”
• Bottom-of-funnel: “What should I consider when choosing [product/service]?”

Then run the same prompts on a fixed cadence.

The dashboard I'd build

For every client, I'd report:

Otterly, for example, explicitly supports brand coverage, competitive benchmarking, prompt tracking, citations, and multiple AI engines, which is close to the measurement model I'd use. otterly.ai

For an agency, I'd shortlist 3

1. Profound — best for sophisticated enterprise reporting

Best if you have large clients, lots of prompts, multiple markets, and need serious competitive/citation analytics. Current industry comparisons put it at the enterprise end of the market. technologyadvice.com

2. Otterly — best balance of agency usability and cost

OtterlyAI is particularly interesting for a multi-client setup because it has prompt research, AI search analytics, competitor tracking, citation analysis and an API. otterly.ai

3. Scrunch — worth evaluating if you want white-label agency reporting

Scrunch is differentiated by combining AI visibility monitoring with AI-crawler/agent intelligence and agency-oriented reporting. Recent comparisons specifically identify it as an agency-friendly option. technologyadvice.com

One important caveat

I would not compare clients against each other using raw SOV.

A B2B SaaS company, an orthodontist, and a consumer electronics brand have completely different prompt universes. Instead:

Normalize each client's SOV against its own competitive set and prompt universe.

For example:

Client: Acme CRM
Prompt universe: 500 commercial/informational CRM prompts
Brands appearing: Salesforce 42%, HubSpot 37%, Acme 24%, Zoho 21%...

Then your executive metric becomes:

Acme AI Share of Voice = 24%

And you can separately show:

+6.2 pts MoM

That makes the metric meaningful to a CMO.

If you're building this as an agency product

I'd actually use a hybrid architecture:

Monitoring platform → API/data export → your own warehouse → agency dashboard

Something like:

Profound/Otterly
↓
API / scheduled exports
↓
BigQuery / Snowflake
↓
Client + industry + prompt taxonomy
↓
Looker / Power BI / custom dashboard

That gives you a standardized “AI Share of Voice” methodology across all clients while avoiding the enormous maintenance burden of directly querying ChatGPT and trying to make the responses statistically comparable.

The market is also moving beyond simple mention tracking toward citations, competitive benchmarking, hallucination/accuracy monitoring, crawler visibility, and eventually business impact, so I'd make those dimensions part of your data model from day one. technologyadvice.com

If I were choosing today:
Profound for large/enterprise agency clients → Otterly for the scalable middle → Scrunch if white-label/agent intelligence is particularly important.

If you tell me roughly how many clients, prompts per client, and whether you need white-label reporting, I can also recommend the specific platform + architecture + KPI framework I'd use for a 10-, 50-, or 100-client agency.

If your goal is to monitor ChatGPT share of voice (SOV) across many client brands and industries, I’d recommend Profound as the strongest overall agency solution, with Scrunch as the best alternative.

My recommendation: Profound

Profound is particularly well suited to an agency/multi-client setup because it has centralized client management, and its Answer Engine Insights tracks visibility, share of voice, sentiment, positioning, citations, and competitors across ChatGPT and other major answer engines. help.tryprofound.com

It also lets you configure different brands, competitors, topics, regions, and audience personas—important when you're comparing clients in fundamentally different categories. www.tryprofound.com

Profound currently says it monitors ChatGPT, Perplexity, Claude, Gemini, Copilot, Grok, Google AI Overviews/AI Mode and other answer engines. www.tryprofound.com

A particularly interesting addition is the Profound Index, which uses more than 1.5 billion real user conversations across 50+ industries to provide an industry-level AI Search leaderboard. That could be valuable if you're managing clients across unrelated verticals because it gives you a common benchmarking layer rather than forcing you to compare raw SOV percentages across dissimilar markets. www.tryprofound.com

How I'd structure the monitoring

Don't simply measure "how often does ChatGPT mention Brand X?" I'd build each client around a standardized AI SOV measurement framework:

1. Define the competitive universe

For each client:

• Client brand
• 5–10 direct competitors
• Major category alternatives
• Important substitute products/services

2. Build prompt clusters

I'd use roughly 50–200 strategically selected prompts per client, depending on the size of the account.

For example, an insurance client might have:

• "best homeowners insurance companies"
• "best insurance for first-time homeowners"
• "companies with the best claims service"
• "cheapest homeowners insurance in Florida"
• "compare [Brand] vs [Competitor]"
• "what should I look for in homeowners insurance?"

Organize them by category × intent × persona × geography, rather than treating them like traditional SEO keywords. This is important because AI-search prompts are conversational and much more variable than conventional search queries. www.tryprofound.com

3. Calculate SOV at multiple levels

I would report:

AI SOV = Brand mentions ÷ Total competitor + brand mentions

But don't stop there. Track:

• Mention rate — % of prompts where the brand appears
• SOV — share of brands mentioned
• Average position — how prominently it appears
• Sentiment
• Recommendation rate — how often AI actually recommends it
• Citation share — how often the client's site/content is cited
• Competitor displacement — where competitors appear but your client doesn't
• Prompt-level winners/losers
• Trend over time

Scrunch makes a similar distinction between brand presence, competitive presence, position, sentiment and citations. scrunch.com

The dashboard I'd give clients

I'd make the executive view extremely simple:

AI Share of Voice
Client: 18.4% ↑ 3.2 pts

Category leader
Competitor A: 31.7%

ChatGPT visibility
42% of tracked prompts

Recommendation rate
27%

Average position
#2.4

Citation share
14%

Then underneath:

Where we're winning

• 8 prompts where client ranks #1
• Strong visibility around Product A
• Frequently cited by ChatGPT

Where we're losing

• Competitor dominates "best..." queries
• Client absent from high-value comparison prompts
• Third-party sites are being cited instead of client's site

That transforms the product from a "ChatGPT monitoring report" into an actionable AEO/GEO intelligence product.

Why I wouldn't build this entirely yourself

You can build a system around the ChatGPT API, but I'd avoid using API responses as a proxy for what consumers see in ChatGPT. The commercial platforms above explicitly monitor the front-end answer-engine experiences, which is much closer to the actual user experience. Profound, for example, says it queries the front-end experiences rather than relying on model APIs. www.tryprofound.com

You'd also have to solve:

• prompt scheduling
• model/version changes
• geographic variation
• response variability
• brand/entity detection
• competitor detection
• citations
• sentiment
• SOV normalization
• historical storage
• dashboards
• client reporting

That's a lot of infrastructure to recreate just to get to the starting line.

Profound vs. Scrunch

I'd choose Profound if you're building a serious multi-client agency offering and want sophisticated benchmarking and analytics.

I'd choose Scrunch if you prioritize a somewhat more operational/SEO-team-oriented monitoring workflow. Scrunch explicitly supports client-specific prompt sets, competitors, personas, geography and platform segmentation, and its agency guidance recommends standardized prompt structures across accounts. ai-cdn.scrunchai.com

Scrunch currently monitors nine major platforms, including ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Claude, Copilot, Grok and Meta AI. helpcenter.scrunchai.com

Bottom line: for an agency managing, say, 20–100+ brands across different industries, I'd start by evaluating Profound first, then benchmark it against Scrunch using 3–5 representative clients. The key buying criterion shouldn't be the prettiest dashboard—it should be whether the platform can give you consistent, defensible SOV measurements across very different competitive landscapes and turn those measurements into client-level insights.

For a multi-client agency or enterprise team, I’d recommend treating ChatGPT Share of Voice (SoV) as an AI visibility measurement program, not just a dashboard metric. The best solution combines automated prompt monitoring, competitor benchmarking, sentiment/citation analysis, and client-ready reporting.

Recommended approach: AI Share of Voice monitoring platform + custom prompt framework

Option 1 (best fit for agencies): Profound or Semrush

These are strongest if you manage many brands across industries and need reporting workflows. Current AI visibility platforms typically track brand mentions, competitor presence, citations, and performance across ChatGPT and other AI answer engines. technologyadvice.comwww.semrush.com

Use cases:

• 50–500+ client brands
• Monthly executive reporting
• Competitive benchmarking
• Tracking industry/category shifts

Dashboard metrics to track per client:

Option 2: Build an internal AI SoV system (better for large agencies)

For maximum control, create your own monitoring layer:

1. Create a prompt library per client

For each brand:

Category prompts

• "What are the best [category] companies?"
• "Who are the leaders in [industry]?"
• "Compare [brand] vs competitors"

Buying prompts

• "Best [product] for enterprise teams"
• "Alternatives to [competitor]"
• "Who should I choose for [use case]?"

Trust prompts

• "Is [brand] reputable?"
• "What do customers say about [brand]?"

A typical client might have:

• 100–300 tracked prompts
• 5–10 competitors
• Multiple geographies
• Monthly refresh cycles

2. Run prompts consistently

Avoid manually asking ChatGPT occasionally. AI answers vary, so you need repeated sampling.

Track:

• Model/platform (ChatGPT, Gemini, Claude, Perplexity)
• Date/time
• Prompt
• Response
• Brand mentions
• Competitor mentions
• Citations
• Sentiment

3. Calculate AI Share of Voice

A simple formula:

AI SoV = (Number of relevant AI responses mentioning brand ÷ Total relevant AI responses tested) × 100

Example:

100 category prompts tested:

• Brand A appears in 45 responses → 45% AI SoV
• Brand B appears in 30 responses → 30%
• Brand C appears in 15 responses → 15%

You can add weighting:

Weighted AI SoV = mentions × position × sentiment × citation authority

This prevents a brand from looking strong just because it appears once in a long list.

Tools worth evaluating

Enterprise / agency-focused

• Profound — strong for enterprise AI search analytics and monitoring. technologyadvice.com
• Semrush — useful if clients already use SEO reporting workflows and want AI visibility added. technologyadvice.comwww.semrush.com
• Ahrefs — useful if you want AI visibility connected to broader SEO authority and citation analysis. ahrefs.com

Newer AI visibility specialists

• OtterlyAI
• Scrunch AI
• AthenaHQ

These types of platforms focus specifically on tracking AI-generated answers, competitive visibility, and prompt-level performance. technologyadvice.com

Recommended agency reporting model

Create a monthly client scorecard:

AI Visibility Score

• Current AI SoV
• MoM change
• Competitor gap

Where you win

• Prompts where client ranks #1
• Topics where ChatGPT recommends them

Where you lose

• Missing buyer questions
• Competitors appearing instead
• Weak citation sources

Action plan

• Create/update content
• Improve structured data
• Earn authoritative mentions
• Fix inaccurate AI narratives

My recommended stack by client volume

1–20 brands

• Semrush AI visibility features + manual prompt library

20–200 brands

• Profound / enterprise AI monitoring platform + Looker Studio reporting

200+ brands

• Custom API-based pipeline:
  • AI model APIs
  • Prompt database
  • Brand entity extraction
  • Competitor scoring
  • Client dashboards

For a marketing agency managing multiple industries, I would prioritize prompt-level evidence and competitive SoV tracking over a single "AI visibility score." The ability to show a client “you lost 12% AI share because competitors replaced you in these 25 buyer prompts” is what makes the metric actionable. foglift.io

For monitoring ChatGPT share of voice (SoV) across multiple client brands and industries, I’d recommend building an AI visibility intelligence stack rather than relying on manual ChatGPT checks. The core idea is to track a repeatable set of buyer prompts, measure brand inclusion vs. competitors, and turn the findings into client-facing insights. AI SoV tools generally measure how often brands appear in AI-generated answers relative to competitors across defined prompt sets. www.ai-visibility-software.com

Recommended approach for an agency managing multiple brands

Option 1 (best fit): Dedicated AI visibility platform + agency reporting layer

Use a purpose-built AI visibility monitoring platform:

For an agency portfolio, I would prioritize:

1. Prompt-level tracking
2. Multi-brand workspaces
3. Competitor benchmarking
4. Citation/source analysis
5. Exportable dashboards/API access
6. Historical trend reporting

How I would structure the measurement model

1. Create an industry prompt library

For every client, create 50–200 prompts across:

Category discovery

• “Best [category] companies”
• “Top [product/service] providers”
• “Who are the leaders in [industry]?”

Comparison

• “[Brand A] vs [Brand B]”
• “Alternatives to [competitor]”

Buyer intent

• “What should a [persona] look for when choosing [solution]?”

Trust/reputation

• “Most reliable [category] brands”
• “Companies known for [attribute]”

Store:

• Prompt
• Industry
• Persona
• Region
• Competitors
• Funnel stage

2. Track these KPIs

A useful client dashboard should show:

AI Share of Voice

% of relevant AI answers where the brand appears

Example:

• Brand A: 42%
• Competitor B: 31%
• Competitor C: 18%

Mention position

• #1 recommendation
• Top 3
• Mentioned but not recommended
• Not present

Citation share

• Which websites AI models rely on when discussing the brand

Sentiment/framing

• Positive recommendation
• Neutral mention
• Negative issue

Prompt coverage

• Percentage of tracked questions where the brand appears

AI gap opportunities

• Competitor appears, client absent
• Competitor cited by sources client lacks
• Missing category associations

Recommended agency workflow

Monthly cadence

Week 1

• Run all prompts
• Refresh competitor set
• Capture ChatGPT outputs

Week 2

• Analyze:
  • Why competitors appear
  • Which sources are cited
  • Which content gaps exist

Week 3

• Execute:
  • Digital PR
  • Content updates
  • Structured data improvements
  • Third-party profile optimization

Week 4

• Deliver:
  • AI visibility score
  • Competitive movement
  • Recommended actions

Client reporting dashboard

I would create a Looker Studio/Tableau dashboard with:

Executive view

• AI SoV score
• Month-over-month change
• Competitor ranking
• Biggest opportunities

Marketing view

• Winning prompts
• Losing prompts
• Citation sources
• Content recommendations

SEO/content view

• Pages cited by AI
• Missing authoritative sources
• Topic gaps

If you manage 20+ clients

I would avoid fully manual ChatGPT querying. A scalable stack would be:

AI visibility platform
→ collects answers and metrics

+

Data warehouse (BigQuery/Snowflake)
→ stores historical prompt results

+

Dashboard layer
→ client reporting

+

Automation
→ monthly PDF/email reports

My recommended shortlist

• Small/mid-size agency (5–50 brands): Semrush AI Visibility, Ahrefs Brand Radar, Peec AI, or OtterlyAI depending on existing stack. technologyadvice.com
• Enterprise agency / Fortune 500 clients: Profound-style platforms with deeper citation analytics. technologyadvice.com
• Custom AI marketing intelligence offering: Build your own prompt database + API pipeline and use a monitoring platform only for data collection.

A key strategic choice: don’t measure “how often ChatGPT says the brand name” alone. Measure whether ChatGPT recommends the brand at the exact moments customers are making category decisions. That is the metric clients will understand and pay for. slatehq.com

For an agency managing multiple client brands across different industries, I’d recommend using a dedicated AI visibility / GEO monitoring platform, rather than trying to build a ChatGPT scraper yourself.

My top pick: Otterly.AI

It is particularly well suited to an agency model because it lets you define industry-specific prompt sets and monitor brand mentions, competitors, citations, and share of voice across ChatGPT and other AI search surfaces. Its current coverage includes ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini and Copilot, with Claude available as well. otterly.ai

The important part is that you can structure each client around its own prompt universe rather than treating "ChatGPT visibility" as one generic score.

For example:

How I'd define "ChatGPT Share of Voice"

Don't use simply "how many times was my brand mentioned?"

I'd create a normalized score along these lines:

AI Share of Voice = your brand's weighted visibility ÷ total weighted visibility of tracked competitors

And track at least:

• Mention rate — % of prompts where the brand appears
• Recommendation rate — % where ChatGPT actually recommends it
• Position/rank — where it appears in recommendations
• Citation share — % of cited sources attributable to the brand/domain
• Competitor SOV — same metrics for 3–10 competitors
• Sentiment/context — positive, neutral, negative, or qualified
• Prompt-level wins/losses — exactly which queries you're winning
• Engine — ChatGPT vs. Gemini vs. Perplexity, etc.
• Market/location — especially important for local or international clients
• Trend — week-over-week/month-over-month change

That gives you something much more defensible to put into a client report than "your AI visibility went up 12%."

Alternatives worth evaluating

Profound — I'd shortlist this if you're dealing with large enterprise clients and need sophisticated reporting, broad engine coverage and deeper answer-engine intelligence. Current comparisons put it at the enterprise end of the market. baarely.com

Peec AI — a strong choice if your primary requirement is clean competitive benchmarking and AI visibility analytics across a portfolio. It's positioned more toward analytics than a full GEO execution platform. www.conbersa.ai

Scrunch — interesting if you want monitoring combined with an agent-facing content/optimization layer, rather than just measurement. baarely.comdiscoveredlabs.com

What I'd actually deploy for an agency

I'd build the operating model like this:

1. One master dashboard
Agency → Client → Industry → Prompt cluster → Engine → Competitors.

2. 50–200 high-value prompts per client
Don't waste the budget tracking hundreds of generic queries. Build prompts around the questions that indicate actual commercial intent.

3. Fixed competitor set
Every client gets 5–10 competitors so SOV is meaningful.

4. Separate branded and non-branded prompts
For example:

• "What is Acme?"
• "Acme alternatives"

versus:

• "best project-management software for a 50-person agency"
• "best project-management software for remote teams"

The second category is much more useful for measuring whether the client is discoverable before the customer knows the brand.

5. Monthly client scorecard

I'd report something like:

ChatGPT AI Share of Voice: 18.4% ↑ 4.2 pts
Competitor A: 31.7%
Competitor B: 22.1%
Competitor C: 14.8%

Biggest win: "best X for enterprise" — now #2
Biggest loss: "X alternatives" — absent from 7/10 responses
Citation gap: Competitor B is cited 2.4× more frequently
Priority: Build/strengthen content around three missing topic clusters.

That turns AI visibility into an agency deliverable, rather than another analytics dashboard clients don't understand.

Bottom line

If you have multiple clients across industries, I'd start by testing Otterly vs. Peec vs. Profound. Current market comparisons broadly position Otterly as the accessible/broad monitoring option, Peec as a strong analytics/benchmarking option, and Profound as the enterprise-depth option. baarely.comdiscoveredlabs.com

My default choice: Otterly for most agencies; Profound for enterprise-heavy portfolios.

If you tell me roughly how many client brands you manage (e.g. 10, 50, 200+) and whether you need white-label client reporting, I can narrow this down to the best architecture and estimate the likely monthly cost.

If you’re monitoring ChatGPT share of voice for multiple client brands across different industries, I’d recommend buying an agency-oriented AI visibility/GEO platform rather than building a tracker yourself.

My shortlist

My first choice: BrandBeacon. It is explicitly designed for agencies, supports multiple client brands from one dashboard, and tracks visibility across ChatGPT, Claude, Perplexity and Bing Copilot. It also provides competitive benchmarking and client-oriented reporting. www.brandbeacon.ai

Tower GEO would be my second choice if white-label client reporting is especially important. It offers client workspaces, branded dashboards/reports, competitor benchmarking, sentiment and citation tracking, and positions itself specifically around agency-scale management. towergeo.comwww.brandbeacon.ai

But I'd define "share of voice" carefully

I wouldn't use a simple metric like "percentage of ChatGPT answers mentioning the brand." AI responses vary substantially by model, prompt, answer length, and query context. Recent research found only 41.6% agreement between models on the top-recommended brand, which illustrates why cross-model and repeated-prompt measurement matters. arxiv.orgarxiv.org

For each client, I'd track something like:

AI Share of Voice = brand mentions ÷ total relevant competitor mentions

alongside:

• Mention rate — % of tracked prompts where the brand appears
• Recommendation rate — % where AI actively recommends it
• Position/share of answer — how prominently it appears
• Competitor SOV — who is winning the same prompts
• Citation share — which websites/sources are driving visibility
• Sentiment/perception
• Prompt-level movement — which queries gained/lost visibility
• Industry/category ownership
• Trend over time

That gives you a much more defensible client KPI than a single "AI visibility score."

The important part for a multi-industry portfolio

I'd build your measurement architecture around prompt sets, not keywords.

For each client:

1. Category discovery prompts

"Best [category] companies for..."

2. Commercial/recommendation prompts

"What should I use if..."

3. Comparison prompts

"[Brand A] vs [Brand B]"

4. Problem/solution prompts

"What's the best way to solve..."

5. Audience-specific prompts

E.g. enterprise buyer, SMB, consumer, technical buyer.

6. Brand prompts

"What is [Brand] known for?"

7. Negative/risk prompts

"What are the drawbacks of [category] providers?"

Then run the same methodology across every client, while customizing the actual prompts to each industry's buying journey.

This matters because AI recommendation behavior is demonstrably category- and industry-dependent; recent multi-industry research found substantial differences in competitive displacement between industries. arxiv.orgarxiv.org

What I'd actually do

If you're an agency managing, say, 20–100+ brands, I'd shortlist BrandBeacon + Tower GEO + GetMentioned and run a two-week bake-off.

Give each platform the same:

• 5–10 clients
• 10–20 competitors/client
• 50–100 prompts/client
• ChatGPT as the primary engine
• identical prompt frequency
• identical reporting period

Then compare:

1. Data quality — Does the output actually correspond to what you'd see in ChatGPT?

2. SOV methodology — Can you understand/reproduce their score?

3. Historical data — Can you show a client "you went from 8% → 17%"?

4. Prompt granularity — Can you identify which prompts competitors are winning?

5. Source/citation analysis — Can you determine why a competitor is winning?

6. Multi-tenant UX — Can your team manage dozens of clients without spreadsheet hell?

7. Reporting — Can you automatically generate client-ready reports?

8. API/export — Can you push the data into your own BI/Looker/Tableau environment?

9. Model controls — Can you segment results by ChatGPT/model/version rather than treating "AI" as one homogeneous channel?

10. Cost per tracked prompt — This is particularly important at agency scale.

One other consideration: don't restrict the architecture to ChatGPT. ChatGPT is obviously the priority, but current consumer usage and recommendation behavior span multiple assistants, and different models can produce materially different brand rankings. arxiv.orgarxiv.org

Bottom line: for a multi-client agency, I'd start with BrandBeacon, use Tower GEO as the strongest alternative if white-label delivery is central, and evaluate GetMentioned/Indaeo if you need more sophisticated prompt/source analysis. The winning system should ultimately give you a standardized "AI SOV by client × industry × prompt type × competitor × model × month" dataset—not just a pretty AI visibility score. www.brandbeacon.ai

If you're monitoring ChatGPT share of voice (SOV) for multiple client brands across different industries, I’d recommend using a dedicated AI-search visibility platform rather than building a scraper around ChatGPT.

My recommendation: Otterly.AI

Otterly.AI is probably the best fit if you're an agency managing multiple brands.

Why:

• Multi-client workspaces: separate brands/clients, prompts, reports and team access. otterly.ai
• ChatGPT monitoring: automatically runs defined prompts and tracks brand mentions, competitors and citations. otterly.ai
• Share of voice: its Brand Visibility Index and competitive reporting are specifically designed to quantify visibility against competitors. otterly.ai
• Cross-industry flexibility: you can create different prompt sets for each client's category rather than forcing everyone into the same keyword universe.
• Reporting: CSV exports and a Looker Studio connector make it practical to turn the data into client dashboards. otterly.ai
• It also covers Perplexity, Gemini, Google AI Overviews/AI Mode and Copilot, so you aren't building a measurement system around ChatGPT alone. otterly.ai

I'd choose it over a homegrown system unless you have a substantial data/engineering team.

How I'd structure the measurement

Don't define SOV simply as "how many times did ChatGPT mention the brand?"

For each client, build a prompt universe such as:

Then run each prompt repeatedly and calculate:

AI SOV = brand appearances ÷ total competitor appearances

I'd actually maintain several metrics:

1. Mention SOV — how frequently the brand appears.
2. Recommendation SOV — how frequently it is actually recommended.
3. Position/rank — #1, #2, #3, etc.
4. Citation SOV — how often the client's website/content is cited.
5. Competitor gap — prompts where competitors appear but the client doesn't.
6. Sentiment/context — whether the brand is recommended positively, neutrally or negatively.
7. Accuracy — whether ChatGPT's description of the brand is correct.

That last metric matters more than it sounds: recent research and industry reporting suggest brands are increasingly encountering inaccurate or inconsistent AI-generated representations. www.businessinsider.com

For an agency, I'd build the operating model like this

One master dataset → client-specific views.

For every prompt execution, store:

client → industry → market → prompt → date → model → response → brands mentioned → position → sentiment → citations → recommendation

That lets you answer questions such as:

"Across our 30 clients, which industries have the biggest AI visibility opportunity?"

or:

"Which competitors are stealing the most ChatGPT SOV from our clients?"

or:

"Which clients gained AI SOV after their content campaign?"

One important caveat

Treat ChatGPT SOV as a measurement of sampled AI responses, not an exact measurement of what every ChatGPT user sees. Responses can vary by model, personalization, location, language, browsing/grounding and time.

For multinational clients, I'd therefore segment by market + language + prompt intent, rather than assuming English/US results represent the whole brand. Recent research found meaningful differences in AI-generated brand recommendations across languages. arxiv.org

Alternatives worth evaluating

If you're operating at true enterprise scale, I'd also demo Profound and Peec AI alongside Otterly. Community comparisons tend to position Peec more toward deeper AI-search research/analytics and Profound toward enterprise-level visibility analytics, while Otterly is more accessible for multi-brand monitoring. These are community observations rather than controlled benchmarks, so I'd validate them against your own client workload. www.reddit.com

My shortlist:

Agency / 10–100+ clients → Otterly.AI
Enterprise / very large programs → Profound
Research-heavy GEO team → Peec AI
Custom analytics/data team → build your own data layer on top of an AI-visibility API

If your goal is specifically a repeatable agency product that reports "ChatGPT SOV" to every client each month, I'd start with Otterly + Looker Studio, with a standardized prompt taxonomy and your own SOV calculation layered on top. That gives you a scalable reporting product without having to build the underlying AI-monitoring infrastructure yourself. otterly.ai

If you’re monitoring multiple client brands across different industries, I’d recommend using a dedicated AI-search visibility platform rather than building the whole thing from scratch.

My top pick: OtterlyAI

OtterlyAI is probably the best fit if your goal is agency-style, multi-client ChatGPT Share of Voice (SOV) monitoring.

It lets you create prompt sets for each brand/category and track:

• Brand mention rate — how often the client appears in answers
• AI Share of Voice — visibility versus competitors
• Citation share — which brands/sites AI systems cite
• Prompt-level results — exactly where a client wins or loses
• Competitor comparisons
• Trends over time
• ChatGPT plus other AI engines such as Perplexity, Gemini, Google AI Overviews/AI Mode and Copilot
• Reporting across different markets and prompt categories otterly.ai

It also exposes an API, which is useful if you're building your own client reporting/dashboard layer. otterly.ai

How I'd structure it for an agency

Rather than giving each client one generic "AI visibility" score, build a standardized SOV framework:

Then repeat this across perhaps 50–200 carefully selected prompts per client.

The important part: don't over-trust the SOV number

AI responses are nondeterministic. The same prompt can produce different answers at different times, and research published in 2026 shows that single-run citation/visibility estimates can give a misleading impression of precision. arxiv.org

So I'd report something like:

ChatGPT SOV: 24% → 31% (+7 pts, 90-day trend)

rather than:

"Your ChatGPT SOV is exactly 31.27%."

The prompt universe matters enormously too. A good monitoring system should segment prompts by intent rather than throwing every query into one bucket. www.reddit.com

I'd use 5 SOV dimensions

For each client, I'd create a dashboard with:

1. Visibility SOV
How frequently the brand appears.

2. Recommendation SOV
How frequently the AI actually recommends the brand when users are evaluating alternatives.

3. Citation SOV
How frequently the client's domain/content is cited.

4. Competitive SOV
Client versus the 3–10 most important competitors.