ChatGPT answers, unedited

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.

5. Sentiment/position SOV
Whether the brand is being described positively, neutrally, or negatively—and where it appears in ranked recommendations.

That gives you a much more useful picture than simply counting mentions.

What I'd compare before buying

I'd shortlist:

• OtterlyAI — best starting point for an agency/multi-brand setup
• Profound — worth evaluating if you need deeper enterprise-level AI-search intelligence
• Peec AI — interesting middle-market alternative
• Semrush AI Visibility — attractive if your agency already runs on Semrush
• Ahrefs Brand Radar — worth considering if Ahrefs is already central to your SEO workflow

The current market has converged around the same basic capabilities—mentions, citations, competitors, SOV and historical tracking—but the differentiator is increasingly prompt-level intelligence: understanding exactly why a competitor gets recommended instead of your client. alignify.co

My recommended stack

For a serious agency, I'd actually do:

OtterlyAI → data/API → your own client dashboard → monthly automated report

with a standardized prompt taxonomy:

Brand
→ Category
→ Problem/use case
→ Commercial investigation
→ "Best X"
→ Comparison
→ Alternative
→ Pricing
→ Reviews
→ Local/market-specific
→ Industry-specific

Then have every client dashboard answer four questions:

Are we being mentioned?
Are we being recommended?
Are competitors taking our visibility?
What should we change to win the lost prompts?

That last question is what turns AI SOV monitoring from a reporting product into an actual client service.

If you're considering this as an agency offering, I can also map out a concrete multi-client ChatGPT SOV architecture, including recommended tools, prompt volumes, KPIs, dashboard structure, and approximate monthly cost per client.

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

My shortlist

1. Best overall for an agency: Profound

This would be my first choice if you’re operating at an agency/enterprise level. Profound tracks share of voice, visibility, sentiment, positioning, competitors, prompts, and citations across ChatGPT and other answer engines. It queries the consumer-facing experiences rather than relying only on model APIs, which is important because you want to measure what users actually see. www.tryprofound.com

It also supports different competitive sets/assets, which makes it better suited to clients in unrelated verticals. www.tryprofound.com

2. Best balance of simplicity + multi-client management: OtterlyAI

Otterly is particularly interesting for an agency because it explicitly supports separate workspaces for individual clients, with separate brands, prompts, reports and team access. You can also reuse prompts across client/competitor reports without consuming the prompt allocation twice. help.otterly.ai

It tracks brand coverage/share of voice, citations and competitors across ChatGPT, Google AI Overviews, Perplexity, Gemini and Microsoft Copilot, and exposes an API if you eventually want to pipe the data into your own reporting stack. otterly.ai

3. Strong analytics alternative: Peec AI

Peec focuses heavily on visibility, position and sentiment, with prompt-level monitoring and competitive analysis. It's a good candidate if your clients care about a more marketing-analytics-oriented view of AI search performance. peec.ai

4. Worth evaluating: Scrunch

Scrunch goes beyond simple "was my brand mentioned?" tracking. It measures share of voice, response position, sentiment, citations, competitive presence, AI referrals and AI-bot traffic. scrunch.com

What I'd actually build for your agency

I'd structure the monitoring around a standardized prompt framework, rather than simply tracking client brand names.

For each client:

Then report four core KPIs:

AI Share of Voice — percentage of relevant answers in which the client appears relative to competitors.

Recommendation Position — where the client appears when it is mentioned.

Citation Share — which sources are causing the AI to recommend the client.

Sentiment/Positioning — why the AI recommends or doesn't recommend the client.

This is much more actionable than a generic "ChatGPT mentions" number.

One important caveat

Don't treat ChatGPT SOV as a single immutable ranking like Google position #3. AI responses can vary by prompt wording, model, location, personalization, retrieval context and time. In fact, recent community research has found substantial visibility differences between ChatGPT model variants. www.reddit.com

So for client reporting I'd calculate something like:

Monthly AI SOV = client mentions ÷ total brand mentions across a fixed, repeatable prompt set

…and keep the prompt set, competitors, geography, platform and measurement methodology constant from month to month.

My recommendation

If you're managing 10–100+ client brands, I'd start by evaluating Profound vs. OtterlyAI:

• Profound → better if you need enterprise-grade analytics, deeper competitive intelligence and sophisticated client reporting.
• OtterlyAI → better if you want a straightforward, scalable agency workflow with clean client separation.
• Peec → worth considering if analytics/visibility measurement is the primary objective.
• Scrunch → particularly interesting if you also want to measure the technical/traffic side of AI agents.

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 dashboards/API, I can narrow this to the best 1–2 options and design the reporting architecture.

If you’re monitoring ChatGPT share of voice (SOV) for multiple client brands across industries, I’d recommend a centralized AI visibility / LLM monitoring platform rather than trying to measure ChatGPT manually.

Recommended solution architecture

1. Multi-brand AI visibility monitoring
I search and ChatGPT visibility monitoring platforms for agencies managing multiple brands

Track, per client:

• Brand mentions and citations in ChatGPT
• Share of voice vs. competitors
• Which prompts/categories produce visibility
• Position/order of brands in responses
• Sentiment and recommendation rate
• Changes over time
• Industry/category-level benchmarks

2. Prompt intelligence
LM prompt research and AI search query monitoring tools

Build a standardized prompt universe for each client—for example:

• “Best [category] for…”
• “[Brand] alternatives”
• “Compare [Brand] vs competitors”
• “What should I buy/use for [need]?”
• “[Industry] companies with…”

The key is to monitor hundreds of representative prompts per brand, rather than asking ChatGPT a handful of questions periodically.

3. Citation/source monitoring
I citation and LLM source visibility monitoring tools

Measure why a brand is visible—not just whether it appears. This lets you report:

• Domains ChatGPT relies upon
• Brand-owned vs. third-party sources
• Competitor sources
• Publications/reviews driving visibility
• Citation share by topic

The metric I’d use

For an agency managing many clients, I'd define ChatGPT SOV roughly as:

Brand SOV = brand appearances in qualifying ChatGPT responses ÷ total brand/competitor appearances

Then create a dashboard at three levels:

I'd also keep mention rate, recommendation rate, citation share, and competitor displacement as separate metrics. A brand being mentioned isn't necessarily the same as ChatGPT recommending it.

What I'd prioritize when choosing a platform

For a multi-client setup, the most important requirements are agency/multi-workspace support, prompt-scale monitoring, historical data, competitor tracking, API/export capability, geographic/model segmentation, and white-label reporting.

If you're building this as an agency service, I can also lay out a concrete stack—which platforms to evaluate, what each costs, how to calculate SOV reliably, and a dashboard schema for 10–100+ client brands.

If you're trying to monitor ChatGPT share of voice (SoV) for multiple client brands across different industries, I’d treat this as an agency-grade AI visibility measurement problem, not simply a keyword-rank-tracking problem.

My recommendation: Scrunch as the core platform

Scrunch for Agencies is probably the best fit if your priority is many clients + different industries + centralized management + reporting.

It specifically supports multi-client workspaces, so you can manage multiple brands without maintaining separate tracking systems. It also offers API access/white-label outputs at the enterprise level. scrunch.com

Its SoV methodology is particularly useful: it measures the percentage of AI responses within a topic that mention your brand, benchmarked against the other brands appearing in those responses. It can break this down by topic, prompt, platform, persona, funnel stage, country, etc. scrunch.com

For an agency, that's important because you don't want one generic "AI visibility score" for every client. You want something like:

Client → Industry → Topic → Prompt → AI platform → Brand → Competitors → Mentions → Position → Sentiment → Citations

The alternative I'd seriously evaluate: Peec AI

Peec AI is especially attractive if automated client reporting is a major part of your workflow.

Peec explicitly supports agency/client tracking and defines SoV as your brand's mentions divided by total tracked-brand mentions. It also separates SoV from simple visibility, which is a useful distinction. peec.ai

Its agency offering includes branded dashboards, client reporting, CSV/API exports, and an MCP workflow that can automatically generate client summaries. peec.aiscrunch.com

I'd choose Peec over Scrunch if: your business model is heavily report-driven and you want to automate the production of monthly/weekly client deliverables.

Semrush is the best choice if you already live in SEO

Semrush AI Visibility Toolkit is worth considering if your agency already uses Semrush.

Its Brand Performance reports track SoV, sentiment and narrative drivers across ChatGPT, Google AI Mode, Perplexity and Gemini. It also has competitor research and prompt/topic analysis. www.semrush.com

The big advantage is connecting traditional SEO data with AI visibility data. For example, you can tell a client:

"Your Google visibility increased 14%, while your ChatGPT SoV increased from 8% to 15%."

rather than creating a completely separate GEO reporting stack.

One caveat: Semrush says its AI Visibility Toolkit currently analyzes the latest ChatGPT model in search mode and updates its brand-performance data weekly. www.semrush.com If precise, repeatable ChatGPT experimentation is your primary requirement, I'd test its methodology against Scrunch/Peec before standardizing on it.

How I'd build the monitoring system

Regardless of vendor, I would not simply track "Does ChatGPT mention Client X?"

Build a standardized prompt taxonomy for every client.

1. Create 5–8 prompt categories

For each client, generate prompts around:

The critical metric is non-branded prompts. Branded queries tell you whether ChatGPT knows the company; category queries tell you whether ChatGPT recommends it.

2. Track competitors consistently

For every client, maintain a competitor set of roughly 5–10 brands.

Then calculate:

AI Share of Voice

Client mentions ÷ total competitor + client mentions

But I'd also report:

• Visibility: % of responses where client appears
• Position: average position in the answer
• Recommendation rate: % of responses where client is actually recommended
• Sentiment: positive / neutral / negative
• Citation share: % of citations pointing to client-owned sources
• Competitor displacement: prompts where competitor appears but client doesn't
• Topic SoV: SoV by category/topic
• Platform SoV: ChatGPT vs Gemini vs Perplexity, etc.

This prevents a client from celebrating a 40% SoV number when they're actually appearing at the bottom of enormous lists.

One important methodological warning

Don't treat a single ChatGPT response as ground truth.

Generative-search outputs are stochastic. Research published in 2026 specifically argues that AI visibility should be treated as a statistical estimate rather than a fixed number because repeated runs can produce materially different citations and rankings. arxiv.org

There's also evidence that different underlying models can produce substantially different brand visibility even for the same prompt. www.reddit.com

So I'd establish a sampling protocol, e.g.:

• 100–500 prompts per client/month
• repeated measurements
• fixed geography/language
• fixed competitor set
• fixed prompt taxonomy
• weekly collection
• monthly aggregation
• report confidence/variance where possible

That gives you a defensible client metric instead of:

"I asked ChatGPT three times and it mentioned you twice."

My shortlist

What I'd actually do

If you're managing 10+ brands across unrelated verticals, I'd run a 30-day bake-off between Scrunch and Peec.

Use the same 100–200 prompts for 3–5 representative clients—say B2B SaaS, ecommerce, healthcare, professional services and local business—and compare:

1. ChatGPT SoV accuracy
2. Repeatability of measurements
3. Competitor detection
4. Citation tracking
5. Prompt/topic segmentation
6. Multi-client administration
7. API/export capability
8. White-label reporting
9. Cost per client
10. Time required to produce a client report

Scrunch is currently particularly compelling for the multi-client infrastructure side, while Peec is compelling for automated reporting and agency workflows. Scrunch's published agency/enterprise offering supports centralized client management and APIs, while Peec explicitly offers client dashboards and automated reporting workflows. peec.aiscrunch.com

If you tell me roughly how many clients you have (e.g. 10, 50, 500) and whether this is for an agency or an in-house portfolio, I can recommend the specific architecture, prompt volume, KPIs, and likely tooling stack.

For monitoring ChatGPT Share of Voice (SOV) across multiple client brands and industries, I’d recommend building a Generative Engine Optimization (GEO) intelligence stack rather than relying on traditional SEO rank trackers. The goal is to measure: “When someone asks ChatGPT about this category, how often does my client appear, how prominently, and why?”

Recommended solution: GEO SOV monitoring platform stack

1. Use a dedicated AI visibility tracking platform (core layer)

These tools are designed to track brand mentions in AI answers across ChatGPT, Claude, Gemini, Perplexity, etc.

For an agency managing multiple client brands, I would prioritize:

1. Profound for enterprise reporting and scale
2. Peec AI for marketer-friendly dashboards
3. Scrunch AI for deeper GEO strategy work

2. Build a standardized prompt monitoring framework

The biggest mistake is tracking random ChatGPT questions. Create a repeatable prompt universe per client.

Example: Automotive client

Category prompts

• “Best electric SUVs in 2026”
• “Most reliable luxury SUVs”
• “Cars with the best resale value”

Commercial prompts

• “Should I buy Brand X or Brand Y?”
• “Compare Brand X vs competitors”

Trust prompts

• “Which car brands are safest?”
• “Which automakers have the best customer service?”

Track:

• Brand mentioned? (yes/no)
• Position in answer
• Share of recommendations
• Sentiment
• Competitor mentions
• Sources cited
• Citation quality
• Product/service attributes associated with brand

3. Create a Share of Voice scoring model

A simple agency-friendly metric:

AI Share of Voice %

Brand mentions ÷ Total brand mentions across monitored prompts × 100

Example:

100 category prompts run monthly:

Add weighted scoring:

AI Visibility Score =

• 40% mention frequency
• 25% ranking/prominence
• 20% sentiment
• 15% citation authority

4. Add citation/source monitoring

ChatGPT recommendations are influenced by the information ecosystem around a brand.

Monitor:

• Which websites ChatGPT references
• Whether client-owned content appears
• Whether industry publications mention the brand
• Whether competitors have stronger third-party validation

Useful supporting tools:

• Semrush for content authority and SEO signals
• Ahrefs for backlinks and competitive research

5. Build a client reporting dashboard

For an agency, I’d create a dashboard in:

• Looker Studio
• Tableau
• Power BI

Monthly client report:

AI Visibility Overview

• ChatGPT SOV %
• Month-over-month change
• Competitor movement

Prompt Performance

• Top winning prompts
• Lost opportunities
• New competitors appearing

Recommendation Analysis

“ChatGPT describes your brand as:

• premium
• innovative
• expensive
• sustainable”

Action Plan

• Create missing content
• Improve structured data
• Increase third-party mentions
• Strengthen product/service pages

6. For a multi-industry agency, use a data warehouse approach

At scale, I would not manually manage dashboards. Build:

Data collection
→ Profound / Peec AI / API-based prompt runner

Storage
→ BigQuery / Snowflake

Analysis
→ Python or BI layer

Reporting
→ Client dashboards

Data model:

Client
 └── Industry
      └── Prompt Set
            └── AI Engine
                  └── Response
                       ├── Brand mentions
                       ├── Position
                       ├── Sentiment
                       ├── Sources
                       └── Competitors

My recommended agency setup

For 10–100+ client brands:

Enterprise option

• Profound
• BigQuery
• Looker Studio
• Custom prompt library

Mid-market option

• Peec AI
• Semrush/Ahrefs
• Looker Studio

Budget option

• Otterly.AI
• Google Sheets
• Manual prompt sampling

A key strategic point: AI Share of Voice is closer to PR/reputation measurement than traditional SEO ranking. The strongest programs combine AI answer tracking with content authority, digital PR, reviews, and third-party validation—not just keyword optimization.

If your goal is to monitor ChatGPT share of voice (SoV) for multiple client brands across industries, I'd recommend a platform that can do three things reliably:

1. Run the same prompts repeatedly at scale.
2. Attribute which brands are mentioned, recommended, or cited.
3. Track changes over time with reporting by client, industry, competitor, and prompt category.

The market for this is evolving quickly, but the strongest options fall into three categories.

If you're an agency managing many brands

A dedicated AI visibility platform is usually the fastest path because it provides:

• client workspaces
• scheduled prompt monitoring
• competitor benchmarking
• trend reporting
• alerts when recommendations change
• executive dashboards

This avoids having to maintain infrastructure as models evolve.

If you need maximum flexibility

A custom monitoring stack often provides better long-term value for agencies with dozens or hundreds of clients.

Typical architecture:

• LLM orchestration
  • Scheduled prompts across ChatGPT, Gemini, Claude, Perplexity, etc.
• Prompt library
  • Industry prompts
  • Purchase-intent prompts
  • Local prompts
  • Comparison prompts
  • Informational prompts
• Evaluation layer
  • Brand mentioned?
  • Position in list
  • Sentiment
  • Recommendation strength
  • Competitors present
  • Sources cited
• Data warehouse
  • Snowflake
  • BigQuery
  • PostgreSQL
• Dashboard
  • Power BI
  • Tableau
  • Looker
  • Grafana

That enables metrics such as:

• AI Share of Voice
• Recommendation Rate
• First Mention Rate
• Average Rank
• Competitive Share
• Citation Frequency
• Source Domains
• Sentiment
• Visibility by prompt category
• Visibility by industry

For multiple industries

I'd organize prompts into reusable taxonomies rather than client-specific lists.

For example:

• Healthcare
  • "Best orthopedic clinic..."
  • "Top telehealth providers..."
• Finance
  • "Best wealth management firms..."
  • "Best payroll software..."
• Retail
  • "Best running shoes..."
  • "Most sustainable clothing brands..."
• B2B SaaS
  • "Best CRM..."
  • "Best HR software..."
  • "Best project management platform..."

Each client inherits the relevant prompt library plus custom prompts.

Recommended reporting

A dashboard for each client could include:

• AI Share of Voice over time
• Visibility vs. top 10 competitors
• Prompt coverage
• Brand appearance frequency
• Average recommendation rank
• Citation sources
• Emerging competitors
• Lost/gained recommendations
• Monthly trend analysis

My overall recommendation

If you're managing 5–20 clients, start with a commercial platform like Profound or Scrunch AI to reduce setup and maintenance.

If you're managing 50+ brands or want to build a differentiated agency offering, invest in a custom monitoring platform that can:

• query multiple AI systems on a schedule,
• store every response,
• calculate standardized AI SoV metrics,
• and deliver white-labeled dashboards for each client.

That combination gives you more control over prompt design, scoring methodology, and reporting, while making it easier to adapt as AI assistants and ranking behaviors change.

If your goal is to monitor ChatGPT share of voice (SoV) across multiple client brands and industries, you need something that goes beyond traditional SEO rank tracking. The core problem is measuring how often a brand is mentioned, recommended, or cited by AI systems for relevant prompts, and how that changes over time.

The solutions fall into three categories.

Some of the strongest dedicated platforms include:

• Profound — One of the most mature enterprise AI visibility platforms. Tracks prompts across ChatGPT, Google AI, Gemini, Claude, and Perplexity. Good agency dashboards, competitive benchmarking, and trend reporting.
• Goodie AI — Focuses on brand visibility in AI search with prompt monitoring and recommendations.
• AthenaHQ — Tracks AI search performance and brand mentions across multiple LLMs.
• Peec AI — Designed for monitoring AI search visibility and benchmarking competitors.
• Scrunch AI — Enterprise-focused AI discoverability platform with governance and analytics.

If you're already invested in SEO tooling, consider:

• Semrush — Expanding AI search visibility capabilities alongside traditional SEO.
• Ahrefs — Increasing focus on AI search analysis in addition to organic search.
• Similarweb — Offers AI traffic and market intelligence that can complement SoV reporting.

For an agency managing dozens of brands, I would look for these capabilities:

• Prompt library organized by client, product, and industry.
• Scheduled execution (daily or weekly).
• Support for multiple AI systems (ChatGPT, Gemini, Claude, Perplexity, Google AI).
• Geographic and language targeting.
• Competitor comparison.
• Citation/source tracking.
• Sentiment and recommendation quality scoring.
• Historical trends.
• API access for BI dashboards.
• White-label client reporting.

A useful framework for measuring AI share of voice is:

AI Share of Voice =

Brand mentions ÷ Total brand mentions across all competitors for a standardized prompt set

You can also layer in:

• Recommendation rate
• First-mentioned rate
• Average ranking position in responses
• Citation frequency
• Positive vs. neutral vs. negative mention
• Response consistency
• Topic coverage
• Source domains influencing AI responses

For agencies with multiple industries, organize prompts into a hierarchy:

Client
 ├── Brand prompts
 ├── Product prompts
 ├── Category prompts
 ├── Competitor prompts
 ├── Local prompts
 └── Buying-intent prompts

For example, a healthcare client might include:

• "Best orthopedic clinics in Chicago"
• "Who offers knee replacement surgery?"
• "Top sports medicine providers"
• "Should I choose Brand A or Brand B?"

The platform would run these repeatedly and compare how often each brand appears over time.

If you have a larger client portfolio (50+ brands), an effective architecture is to use an AI visibility platform for data collection, then export results via API into a warehouse (such as Snowflake or BigQuery) and build executive dashboards in Power BI or Tableau. That gives you cross-client benchmarks, industry averages, and custom KPIs while avoiding the complexity of maintaining your own prompt execution infrastructure.

For most digital agencies today, Profound is often the strongest choice if AI share of voice is the primary KPI. If budget is a major factor, Peec AI or AthenaHQ can provide much of the same core functionality at a lower price point, while organizations already standardized on enterprise SEO platforms may prefer to extend Semrush rather than introduce another tool.

If your goal is to monitor ChatGPT share of voice (SoV) across many client brands, I'd recommend treating it as an LLM visibility platform rather than a traditional SEO rank tracker. The key challenge is that ChatGPT responses are probabilistic, personalized in some cases, and change over time, so you need systematic sampling rather than a single query.

A practical solution has four components:

What to measure

For each client, build a prompt set of 100–500 prompts across the customer journey.

For example, if your client is a bank:

• Best online banks
• Best checking account
• Banks for students
• Best business checking
• Alternatives to Chase
• Is X Bank trustworthy?
• Best bank for freelancers

For each response capture:

• Was the brand mentioned?
• Mention position (1st, 2nd, etc.)
• Frequency of mentions
• Positive/neutral/negative framing
• Whether it was recommended
• Competitors mentioned
• Sources/citations (when available)
• Response length
• Confidence score

From this you can calculate:

• Share of Voice = Brand mentions ÷ Total brand mentions
• Recommendation Rate
• Top-3 Inclusion %
• Average Position
• Competitive Visibility Index

Scale across industries

Structure data like this:

Industry
   Client
      Competitors
         Prompt Cluster
              Prompt
                  Model
                      Date

Example:

Healthcare
   Client: Mayo Clinic
      Cleveland Clinic
      Johns Hopkins
      Kaiser

Retail
   Nike
      Adidas
      Hoka
      Brooks

Insurance
   Progressive
   GEICO
   State Farm

This lets agencies benchmark multiple brands consistently.

Models to monitor

Don't limit yourself to ChatGPT.

Monitor:

• ChatGPT
• Gemini
• Claude
• Perplexity
• Microsoft Copilot

Clients increasingly care about overall AI visibility rather than one assistant.

Frequency

For enterprise clients:

• Weekly monitoring for high-volume prompts
• Daily monitoring for priority prompts
• Monthly deep audits

Keep historical snapshots because answers evolve.

Automation architecture

A typical pipeline looks like:

Scheduler
      ↓
Prompt Library
      ↓
LLM Runner
      ↓
Response Storage
      ↓
LLM Extractor
      ↓
Metrics Engine
      ↓
Dashboard

Store raw responses so you can re-analyze them as your scoring improves.

Commercial platforms

If you don't want to build from scratch, several vendors now focus on AI search and LLM visibility:

• Profound
• Goodie AI
• Scrunch AI
• Peec AI
• AthenaHQ

These generally track AI visibility, prompt performance, competitive mentions, and changes over time, though capabilities vary.

Building your own

If you're serving many agency clients, a custom platform often provides the most flexibility.

Suggested stack:

• Prompt execution: Python orchestration with scheduled jobs
• Storage: PostgreSQL for structured metrics plus object storage for raw responses
• Extraction: LLM-based analysis to identify brands, recommendation strength, sentiment, and themes
• Analytics: dbt or SQL models to compute SoV metrics
• Visualization: Power BI, Looker, or Tableau

This approach lets you define your own scoring methodology, adapt to different industries, and add new models as they emerge.

Suggested KPI dashboard

For each client, include:

• AI Share of Voice
• Top-3 Recommendation Rate
• Prompt Coverage
• Visibility Trend (30/90/365 days)
• Competitive Gap
• New Competitors Emerging
• Missing Buying Questions
• Citation Frequency
• Brand Sentiment
• Industry Benchmark Percentile

For an agency managing multiple brands, this produces a repeatable "AI visibility" scorecard that can be compared across industries while still allowing prompt libraries and competitors to be tailored to each client's market.

If your goal is to monitor ChatGPT share of voice (SOV) for multiple client brands across industries, you need a platform that can repeatedly test prompts, measure brand visibility, compare competitors, and track changes over time. This is an emerging category often called AI search optimization or LLM visibility monitoring.

The strongest options today depend on your use case.

For an agency managing many brands

I'd separate the problem into four layers:

1. Prompt Library
   • 100–500 standardized prompts per industry
   • Informational
   • Commercial
   • Local
   • Product comparison
   • Best-of lists
   • Buying intent
   • Brand-specific

2. Scheduled Testing
   • Run every week or daily
   • Query ChatGPT
   • Query Gemini
   • Query Claude
   • Query Perplexity

3. Metrics

For each prompt calculate:

• Brand mentioned (Yes/No)
• Mention position
• Recommendation frequency
• Competitor frequency
• Sentiment
• Citation sources
• Confidence
• Response length
• Share of Voice %

4. Client Dashboard

Display:

• Overall AI SOV
• Industry SOV
• Competitor comparisons
• Month-over-month changes
• Winning prompts
• Lost prompts
• New competitors appearing
• Citation opportunities

Example SOV calculation

Suppose you test 200 prompts.

Client AI Share of Voice:

82 ÷ (82+64+39+15) = 41%

You can also weight prompts by importance:

• Purchase intent = 3x
• Comparison = 2x
• Informational = 1x

This produces a more meaningful business metric than a simple mention count.

If you manage dozens of clients

A scalable architecture might look like:

• Prompt database
• Industry taxonomy
• Automated prompt scheduler
• LLM testing pipeline
• Response parser
• Brand/entity extraction
• Citation extraction
• Competitor detection
• Historical database
• Dashboard (e.g., Looker, Power BI, or Tableau)

Then each client receives:

• Overall AI visibility score
• Share of Voice
• Competitor movement
• Recommended content opportunities
• New prompt opportunities
• Weekly or monthly reports

My recommendation

For an agency serving multiple clients across industries:

• If you want an off-the-shelf enterprise solution, Profound is one of the most mature offerings for AI visibility monitoring and competitive benchmarking.
• If you're looking for a cost-effective option, Otterly.AI provides solid prompt tracking and reporting suitable for many agencies.
• If AI visibility monitoring will become a core service offering, consider building a custom platform that combines prompt orchestration, LLM APIs, automated entity extraction, and a client-facing dashboard. This gives you the flexibility to tailor scoring, industries, and reporting to your clients while avoiding dependence on a single vendor.