ChatGPT answers, unedited

For local business clients, you probably want an AI visibility / GEO (Generative Engine Optimization) tracking platform, not a traditional citation tracker. The key is being able to answer:

1. Which AI assistant mentioned the business? (ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews)
2. What prompt triggered it? (“best dentist near me,” “top HVAC companies in Anaheim,” etc.)
3. Which location was represented? (important for multi-location clients)
4. What sources/citations did the AI rely on?
5. Was the mention a recommendation, citation, or just a passing reference?

A few options worth evaluating:

For local SEO agencies, I would structure the workflow like this:

Per-location tracking model

Create a project per physical location:

Location entity

• Business name
• Address
• Service area
• Primary categories
• Competitors
• Target prompts

Prompt groups

• Discovery:
  • “best [service] near [city]”
  • “[service] companies in [city]”
• Comparison:
  • “who should I hire for [service] in [city]”
• Trust:
  • “most reviewed [service] near me”
  • “best rated [service]”

Metrics

• AI recommendation rate (% of prompts where client appears)
• Citation share (% of answers where client is sourced)
• Position/order in recommendations
• Competitor mentions
• Source URLs cited
• Location accuracy (did AI recommend the correct branch?)

The biggest gap right now is true geo-attribution. Many AI visibility tools tell you “brand appeared,” but they don’t always prove that the answer was tied to a specific GBP/location. For multi-location clients, you may need to add your own layer:

• Store prompts by city/ZIP
• Run them from consistent geographic contexts
• Capture raw AI outputs
• Parse:
  • business mentioned
  • address mentioned
  • citation URL
  • assistant
  • date/time
  • prompt

A practical agency stack would be:

• BrightLocal / Whitespark → traditional local citations + listings
• Profound or Otterly → AI assistant visibility tracking
• Airtable/Looker Studio/custom dashboard → client-facing location attribution layer

If you manage dozens or hundreds of local clients, I would prioritize a tool with API/export access and build your own “AI citations by location” database rather than relying only on a dashboard. www.pleiadesconsultancy.comcitationdesk.com

A good internal schema would look like:

Client
 └── Location
      └── Prompt
           └── AI Run
                ├── Assistant
                ├── Mentioned? 
                ├── Recommended rank
                ├── Citation URLs
                ├── Competitors
                └── Sentiment

That structure lets you sell something much more valuable than “AI SEO”: proof that a specific location is being recommended by AI assistants.

If your goal is agency-grade tracking of AI citations/mentions and attributing them to individual locations, I’d prioritize tools that treat the location/market as the unit of measurement, rather than just tracking brand mentions. That matters because a national average can hide a location that is completely invisible in AI search. gracker.ai

My shortlist

My first choice: Cheers. Its platform explicitly tracks recommendation share by market, cited sources, competitors, and the reviews/profiles/pages behind AI recommendations. It also frames the workflow around diagnosing and fixing evidence gaps for individual markets. www.cheers.tech

Cheers AI Visibility Platform

Local Glyph is worth testing if you want something more directly oriented toward local businesses. It exposes prompt-level responses, citations, sentiment, accuracy problems, competitors, and platform-specific visibility. localglyph.com

Local Glyph

SearchDock is interesting if you want conventional local SEO + AI visibility in one system. It specifically supports geo-modified tracking, AI local-answer monitoring, locations, and multi-engine citation tracking. searchdock.io

SearchDock Local Business

But there's an important distinction

If by "capture citations" you mean:

Customer asks "best HVAC company in Fort Lauderdale," ChatGPT recommends Company X, cites three websites, and I need to know that this citation belongs to Location #17, not the parent brand.

I'd actually consider building a thin attribution layer on top of an AI visibility API/tool, rather than relying entirely on a dashboard.

Your data model should look roughly like:

Client
  └── Location
       ├── address / lat / lng
       ├── Google Business Profile
       ├── service area
       ├── canonical URL
       └── location ID

AI Query
  ├── engine
  ├── prompt
  ├── geo coordinates / city
  ├── timestamp
  └── response

AI Result
  ├── business mentioned
  ├── business rank/order
  ├── sentiment
  ├── cited URL
  ├── cited domain
  ├── citation type
  └── matched location ID

Then you can produce metrics like:

Location 17 — Fort Lauderdale

• AI recommendation rate: 42%
• Citation rate: 31%
• Avg. position: 2.4
• ChatGPT: 48%
• Gemini: 36%
• Perplexity: 29%
• Google AI: 44%
• Citations pointing to location page: 17
• Citations pointing to third-party sources: 43
• Competitor share: 28%

That's considerably more useful to an agency than an "AI visibility score."

Also, don't limit this to ChatGPT. Recent research shows substantial differences between engines in which businesses/sources they cite; one 2026 benchmark found only 18% of brand citations appeared across all three of ChatGPT, Gemini, and Perplexity. www.seorise.io

And the underlying source data is important: Yext's analysis of 17.2 million AI citations found that verified, structured, directly distributed data represented a large share of distinct citation sources. In other words, you want to capture what source caused the AI answer, not merely whether the business appeared. www.yext.com

If you're building this for your own agency, tell me roughly how many clients and locations you manage (e.g. 50 clients / 500 locations), and I can recommend whether I'd use Cheers/Local Glyph/etc. as the underlying platform or build the tracking stack yourself—including the API/data architecture I'd use.

If your core requirement is “show me exactly which AI assistant cited which source for which client location”, I’d avoid treating this as generic “AI visibility.” You want prompt-level citation tracking + location-level attribution.

My recommendation

For an agency serving local businesses, I’d look at this stack:

1. Ahrefs Brand Radar — best broad AI-citation data layer. It tracks mentions, citations, cited pages/domains, competitors, and can be queried by regions/topics. It currently covers seven AI platforms and exposes its AI-visibility metrics via API, which is useful if you want to build your own client dashboard. Ahrefs Help Center Ahrefs Help Center

ahrefs.com

2. Semrush AI Visibility — probably the better choice if you want something more agency/reporting oriented. It supports prompt tracking, competitor comparisons, citations, sentiment, regional/language combinations, white-label reports and exports. Semrush Semrush

semrush.com

3. Build a location-level data model on top of the API if this is going to be a serious product/service. The important distinction is:

Client → Location → Prompt → AI Engine → Response → Mention → Citation → Source URL

So, for example:

ABC Dental → Arlington, VA → “best dentist near Arlington” → ChatGPT → cited → Yelp URL

rather than simply reporting:

ABC Dental has 37 AI citations.

That's important because the unit of measurement should be the location, not the overall brand when you're dealing with local businesses. GrackerAI

What I'd actually build

For each location, maintain a fixed prompt set such as:

• “best plumbers near [city]”
• “best emergency plumber in [city]”
• “plumber for water heater replacement in [city]”
• “top-rated [service] near [neighborhood]”
• “[service] companies that serve [ZIP]”
• “[business category] near me”

Then run those against ChatGPT, Gemini, Perplexity, Google AI surfaces, and potentially Claude/Copilot, recording:

That gives you something much more valuable to clients than an opaque “GEO score.”

One important wrinkle

Don't track only citations to the client's own website. AI systems frequently cite third-party sources. Ahrefs explicitly distinguishes between pages that are cited and pages that were found/retrieved but not cited. Ahrefs Help Center

For local SEO, I'd therefore report three separate things:

1. AI Recommendation Visibility
“Are you being recommended?”

2. Citation Visibility
“Which sources are AI assistants using to justify the recommendation?”

3. Location Entity Accuracy
“Does the AI have the correct address, service area, phone, hours, reviews, etc. for this particular location?”

That third layer is especially important for multi-location clients.

If you're trying to productize this

I'd lean toward Ahrefs API + your own location/prompt database + your own reporting UI rather than trying to make a generic AI-visibility platform your system of record.

There's evidence that the engines behave quite differently: one recent benchmark found only 18% of brand citations appeared across all three of ChatGPT, Gemini and Perplexity, meaning a single-engine score can give clients a misleading picture. Seorise

And the landscape is changing quickly—for example, recent research found major shifts in which third-party domains ChatGPT cites. Axios

If you tell me roughly how many locations you manage (e.g. 50, 500, 5,000), I can recommend the specific architecture/API/tool stack I'd use—including what I'd use for the actual AI queries, location simulation, database, and client reporting.

If your goal is agency-grade tracking of AI citations/mentions and attributing them to individual locations, I’d look at this as an AI visibility + citation intelligence problem, not traditional citation building.

My shortlist

1. Yext Scout — best fit for serious multi-location clients

Yext Scout tracks visibility across Google, ChatGPT, Gemini, Claude, Perplexity, etc., and—more importantly for you—breaks performance down location by location. It also identifies the sources AI cites when mentioning a business. www.yext.com

That combination is unusually close to what you're describing:

AI prompt → AI answer → business/location mentioned → cited sources → competitor → location-level reporting

Yext also has a knowledge graph/listing infrastructure underneath it, which makes sense if you're managing dozens or hundreds of locations.

2. Cheers — interesting if your clients are primarily local/service businesses

Cheers explicitly focuses on market-level recommendation share and citation intelligence. It maps AI citations back to reviews, directories, local pages and business profiles, and lets you see whether AI recommends a particular location. www.yext.comwww.cheers.techwww.yext.com

I'd seriously demo this if your clients are franchises, home services, healthcare, salons, etc.

3. Local Glyph — good lightweight/agency option

Local Glyph has prompt-level AI response tracking, citations/mentions, competitors, and location-oriented local visibility features. It's currently positioned as a more lightweight platform and is in beta. localglyph.com

The important distinction

Don't just buy something that reports:

"Client X was mentioned 47% of the time."

You want the underlying data to look more like:

That lets you tell the client why Location A is winning while Location B isn't, rather than giving them another opaque "AI visibility score."

This is particularly important because recent research from Yext found that location-grounded AI answers heavily favor sources that contain geographic context; its analysis covered 155.5 million AI citations and found that 80% pointed to sources a brand could influence. www.yext.com

If you're building your own solution

If you're asking because you want to own the data layer rather than subscribe to another SEO platform, I'd actually consider building it.

The core architecture I'd use is:

Location database
→ location ID, GBP/entity ID, address, lat/long, services, markets

Prompt library
→ [service] + [city], [service] near me, "best [service]", comparison/urgent/buying-intent prompts

AI collection
→ ChatGPT, Gemini, Perplexity, Google AI experiences, etc.

Response parser
→ mentioned businesses
→ location/entity resolution
→ recommendation order
→ sentiment/context
→ cited URLs/domains

Citation resolver
→ URL → domain → directory/profile/local page/review site
→ associate citation with the client's specific location where possible

Attribution layer
→ citation → location → client → prompt → AI engine → date

Reporting
→ client/location/market dashboards
→ citation share
→ recommendation share
→ competitor share
→ new/lost citations
→ source opportunities

That would give you something much more defensible than a generic AI SEO score.

My recommendation: if you need something now, demo Yext Scout and Cheers first. If you're trying to build a product/service around this for your agency, I'd lean toward building the citation-attribution layer yourself and using an AI visibility provider/API for collection, because the valuable proprietary asset is ultimately the historical prompt → response → location → citation → competitor dataset.

If you tell me roughly how many clients/locations you manage (e.g. 50 locations vs. 5,000) and whether you need API/white-label reporting, I can narrow this down to the best stack and approximate cost.

If your clients are local businesses and you need to attribute AI citations to individual locations, I’d avoid treating this as generic “AI visibility.” You want a location-aware citation intelligence layer.

My shortlist

For your use case, I'd start with Local Glyph or AuthorityStack.ai.

Local Glyph is explicitly built around local businesses and lets you inspect the AI response, citations, competitors, and business-specific accuracy. localglyph.com AuthorityStack combines local citation auditing with AI recommendation tracking, which is interesting if you want traditional local citations and AI citations in the same workflow. authoritystack.ai

If you're building something more like a proprietary reporting system for your agency, I'd look harder at Foglift. It exposes the underlying pieces you actually want to store: prompt → engine → response → cited URL → competitor → date, plus API/webhook functionality on higher plans. foglift.iowww.pleiadesconsultancy.com

The important distinction

I wouldn't make your primary metric simply:

“Client was cited 37 times.”

Instead, structure every observation something like:

Location: ABC Plumbing — Appleton, WI
Prompt: “Best plumbers near Appleton for water heater replacement”
Engine: ChatGPT
Run date: Aug. 19, 2026
Client mentioned: Yes
Client recommended: Yes
Client location: Appleton
Citation: clientdomain.com/water-heaters
Competitors cited: X, Y, Z
Source domains: Yelp, BBB, local news, client site, etc.
Answer sentiment: Positive
Citation type: First-party / third-party
Confidence: High

That gives you something you can actually report to a client as “AI visibility for this location”, rather than a vague brand-level score.

Also, don't limit this to ChatGPT. Current local-AI research shows meaningful differences between engines, with Google AI Overviews/AI Mode, Perplexity, Grok, and Copilot potentially producing substantially different citation patterns. authoritystack.ailocafy.com

If I were building your agency stack

I'd use:

Local SEO/location data
→ GBP/location pages/reviews/directories

AI query monitoring
→ 20–50 fixed queries per location

AI response capture
→ ChatGPT + Perplexity + Google AI + Gemini/Claude

Citation extraction
→ URL + source domain + cited location + competitor

Attribution database
→ client_id → location_id → prompt_id → engine → run → citation

Client dashboard
→ AI visibility, citation rate, share of voice, cited URLs, competitors, and changes over time.

The fixed-query approach is important because AI answers are stochastic; repeated measurements are much more useful than a one-off “AI visibility score.” foglift.iowww.pleiadesconsultancy.com

If you tell me roughly how many clients/locations you manage (e.g. 20 clients × 5 locations), I can recommend whether you should buy one of these platforms or build the citation-capture layer yourself—and what the architecture would look like.

Yes. If your goal is agency-grade tracking of AI citations for individual locations, I’d avoid treating this like ordinary rank tracking. You want a system that records the prompt → AI answer → cited sources → mentioned businesses → location → engine → timestamp.

A few current options stand out:

My shortlist

1. Local Glyph — best fit for local SEO agencies

It is explicitly built around local AI visibility and lets you inspect the exact responses to tracked prompts, including competitors and the websites AI cited. It tracks ChatGPT, Google AI Overviews, and Perplexity. localglyph.com

The important part for your use case is prompt organization + location-level visibility, rather than simply monitoring whether the client brand appeared.

2. Cheers — interesting for multi-location clients

Cheers is particularly interesting if you're managing franchises or businesses with many locations. Its positioning includes recommendation tracking, citation intelligence, competitor mentions, and location intelligence, with prompts segmented by city, category, urgency and buying intent. www.cheers.techwww.aicited.io

3. Citations.io — strongest general-purpose citation monitoring

It tracks ChatGPT, Gemini, Perplexity and Claude and gives you the underlying answers, cited sources, competitors, share of answer, etc. citations.io

The weakness for your particular use case is that it looks more brand-centric than location-centric. You'd want to verify how deeply its location segmentation works before standardizing it across clients.

4. ModelMention Local — good lightweight/local-first option

This is explicitly designed around queries like “best dentist in Austin” and “who should I hire near me?”, with city-level competitor and share-of-voice analysis across ChatGPT, Gemini and Perplexity. modelmention.io

What I'd actually build for your agency

If you have, say, a client with 30 locations, don't structure the data as:

Client → AI visibility

Structure it as:

Client → Location → Query cluster → AI engine → Response → Mention → Citation

For example:

And I'd capture two different things, because they're easily confused:

Business mention = AI recommended/named the location.

Source citation = AI linked/cited the website or third-party source supporting its answer.

Those aren't the same KPI. An AI can recommend ABC Dental while citing Yelp, Healthgrades, the business website, etc. Current research/tools are increasingly treating citation-source analysis as a separate signal. www.cheers.techwww.aicited.io

The killer feature I'd prioritize

For each location, generate a fixed prompt set such as:

• "best dentist in Mobile AL"
• "best dentist near Mobile AL"
• "emergency dentist Mobile AL"
• "dentist for dental implants in Mobile"
• "best family dentist in Mobile"
• "who are the top rated dentists in Mobile"
• "dentist accepting new patients in Mobile"

Then run that same grid periodically across ChatGPT, Gemini, Perplexity and Google AI surfaces, storing the raw answer and citations.

That's much more defensible in a client report than saying "your AI score went from 43 to 51." A 2026 multi-location tracking framework similarly recommends treating the individual location—not the national brand—as the measurement unit. gracker.ai

If you're thinking about building this yourself

This is actually a pretty compelling agency product to build.

I'd make the database roughly:

clients
  └── locations
       └── prompt_sets
            └── prompts
                 └── runs
                      ├── engine
                      ├── timestamp
                      ├── raw_response
                      ├── mentioned_entities
                      ├── cited_urls
                      ├── cited_domains
                      ├── competitors
                      └── sentiment/recommendation

Then your client dashboard becomes:

Location AI Visibility

• Citation rate: 38%
• Recommendation rate: 24%
• Avg. recommendation position: 2.7
• Share of voice: 18%
• Citations to client's site: 31%
• Citations to directories: 44%
• Competitor citations: 67%
• Incorrect information detected: 3

That gives you something much more valuable than another SEO dashboard: a location-level AI search intelligence product you can potentially sell as its own recurring service.

If you're managing dozens/hundreds of local clients, I'd lean toward building on top of an API/data provider rather than manually operating one of these dashboards. The differentiator for your agency would be the location attribution, prompt library, historical database, and client reporting—not merely querying the LLMs.

If your goal is agency-grade tracking of AI citations and attributing them to individual client locations, I’d avoid a generic “AI visibility score” tool. You want a system whose basic data model is:

Client → Location → Prompt → AI engine → Answer → Mentioned business → Cited URL/source → Competitors → Timestamp

That distinction matters because a national brand-level score can hide the fact that, say, Location A is getting recommended while Location B is invisible. Recent local-AI research specifically recommends treating the location as the unit of measurement. gracker.aiwww.rank.ai

What I'd shortlist

1. Cheers — strongest fit if you're managing lots of local clients/locations

Cheers AI Visibility Platform

This is probably the closest match to what you're describing. It explicitly supports location-level recommendation tracking, market-specific prompts, competitor comparisons, and citation intelligence showing the sources behind AI recommendations. It tracks ChatGPT, Gemini, Perplexity and Google search experiences. www.cheers.tech

The important part for you is that it doesn't just answer “did the brand appear?” It can answer:

• Which location/market was tested?
• Which AI provider?
• Which prompt?
• Was the client recommended?
• Which competitors appeared?
• Which sources did the AI cite?
• What evidence/reviews/pages are associated with the gap?

That's the architecture I'd want for client reporting.

2. LLM Pulse — worth testing for an agency workflow

LLM Pulse for Local SEO Agencies

It's explicitly positioned for local SEO agencies and tracks local prompts across ChatGPT, Perplexity, Gemini and Google AI answers, including the sources AI trusts and competitor businesses. llmpulse.aiwww.cheers.tech

I'd put this on a trial alongside Cheers and see which gives you the better client → location → prompt → citation export/API workflow.

3. Local Glyph — good lighter-weight option

Local Glyph

It tracks local visibility across Google AI Overviews, ChatGPT and Perplexity, lets you inspect individual prompt responses, and shows the websites AI cited. It also has accuracy checking against business data such as address, phone, hours and services. localglyph.com

This looks attractive if you want something simpler rather than a large enterprise platform.

One thing I'd be careful about

Don't make “AI mentioned the business” your only metric.

For local clients, I'd capture at least these separately:

And save the raw AI response, not just the extracted citation. AI answers are stochastic, so a single screenshot/check isn't a reliable longitudinal metric; repeated standardized prompts are much more useful. gracker.aiwww.rank.ai

If you're building this into your own agency product

I'd actually consider using an existing AI-visibility platform for the collection layer and building your own attribution/reporting layer on top.

Your database could look roughly like:

agency → client → location → prompt → engine → run → answer → entity mention → citation → source domain

Then you can produce reports like:

Downtown Dental — Austin

42% recommendation share
31 citations captured
18 unique cited domains
7 competitor citations
12 prompts where competitors beat client

Top citation sources: Yelp, Healthgrades, client website, local publication
Biggest opportunity: 6 prompts where competitors are repeatedly cited from local review pages.

That is considerably more valuable to a local-business client than “Your AI visibility score is 64.”

My pick: start by demoing Cheers and LLM Pulse. Cheers looks particularly aligned with your location attribution + citation intelligence requirement, while LLM Pulse is explicitly agency/local-SEO oriented. If neither gives you the API/export granularity you need, I'd build the attribution layer yourself rather than trying to force a generic rank tracker to do it. llmpulse.aiwww.cheers.tech

For local business clients, you’ll want something closer to AI visibility tracking + local attribution, not just a generic “brand mention” tracker. The hard part is that AI assistants don’t expose a clean analytics feed like Google Business Profile does, so most platforms work by running controlled prompts (e.g., “best plumber in Dallas,” “dentist near me,” “roof repair company in Austin”) and recording the answers, mentions, and cited URLs. www.citeflow.io

For an agency/local SEO use case, I’d evaluate these:

Best fits for local SEO agencies

1. Rankability ChatGPT Rank Tracker — probably closest to your use case

• Supports location-aware prompts (city/region appended to queries).
• Tracks:
  • whether the business appears
  • where it appears in the answer
  • which URLs are cited
  • competitors appearing instead
• Good if you need client-facing reporting around “AI recommendations in your market.” www.rankability.com

2. Citations.io — good citation-focused reporting

• Built around AI citation visibility.
• Tracks cited sources across ChatGPT, Gemini, Perplexity, and Claude.
• Useful if your deliverable is “here are the places AI trusts for this category, and here is where your client is missing.” citations.io

3. MentionScout — stronger for agency-scale monitoring

• Tracks multiple AI engines.
• Shows citations, mentions, competitor share of voice, and prompt gaps.
• Better if you manage many brands/categories. mentionscout.com

4. Nightwatch Citation Intelligence — useful if you already do SEO reporting

• Combines AI citation monitoring with traditional ranking data.
• Helpful for explaining “your Google rankings changed, which influenced AI visibility.” nightwatch.io

What I would build into your client reporting model

For each location, track a fixed prompt set:

Discovery prompts

• “Best [service] in [city]”
• “Who should I call for [problem] in [city]?”
• “Top-rated [category] near [neighborhood]”

Attributes

• Business mentioned? ✅
• Position in answer?
• AI engine?
• Citation URL?
• Citation source type:
  • Google Business Profile / Maps ecosystem
  • local directory
  • review site
  • local news
  • business website
  • competitor website
• Sentiment/context:
  • recommended
  • alternative
  • mentioned but not preferred

Then report a metric like:

AI Local Visibility Score
“Your location appeared in 42% of tracked AI recommendations for your category across ChatGPT, Gemini, and Perplexity.”

My recommendation for a local SEO agency stack

If you have 10–100 locations:

• Start with Rankability or Citations.io.
• Add your own prompt library per vertical.
• Export monthly PDFs/dashboard screenshots for clients.

If you have 100+ locations/franchise clients:

• Look at MentionScout or an enterprise platform.
• Consider building a lightweight internal layer that stores:
  • location
  • prompts
  • responses
  • citations
  • competitors
  • changes over time

The key distinction: don’t sell “AI mentions.” Sell “which locations are being recommended by AI assistants, for which customer questions, and what sources are causing the recommendation.” That maps much better to local SEO outcomes.

If your core requirement is “show me exactly which AI assistant cited which source, for which client location”, I’d use a dedicated AI-visibility platform rather than trying to bolt this onto a normal local-rank tracker.

My picks

1. Best fit: Cheers

This is the closest match to your use case. It explicitly supports:

• Location/market-level tracking
• ChatGPT, Gemini, Perplexity and Google search experiences
• The exact sources cited in AI answers
• Competitor mentions
• Prompt-level tracking
• Recommendation/share-of-voice reporting
• Diagnosing which reviews, profiles, citations and pages are contributing to visibility

Most importantly, its model treats the location as the reporting unit, rather than averaging everything at the brand level. www.cheers.tech

For an agency, I'd structure each client like:

Client → Location → Market → Prompt → AI Engine → Run → Mention → Citation URL → Cited Source → Competitor

That lets you answer:

“Our Phoenix location was recommended in ChatGPT for ‘best emergency plumber near Phoenix,’ and ChatGPT cited Yelp + our Phoenix service page.”

rather than simply:

“Client has 37 AI citations.”

2. Best agency-oriented alternative: LLM Pulse

LLM Pulse is specifically positioned for local SEO agencies. It tracks local prompts across ChatGPT, Perplexity, Gemini and Google AI answers, including which sources AI cites and competitors. llmpulse.ai

I'd shortlist this if you care heavily about white-label/client reporting and managing lots of clients.

3. Best if actual citation URLs are your highest priority: Rankability

Rankability is particularly interesting because it says it captures the actual source URLs ChatGPT links to, rather than just recording whether the brand was mentioned. It also supports location-aware queries. www.rankability.com

That's useful if your eventual product/report needs a citation-level database.

What I would build around the tool

Don't make “AI citations” the primary metric. Store the raw evidence and derive metrics from it.

For every AI run, capture:

Then calculate:

AI Citation Rate = runs where client received a citation ÷ total runs

Recommendation Rate = runs where location was recommended ÷ total runs

Citation Share = client citations ÷ all competitor citations

Source Share = how frequently each domain/source contributes to citations

Location AI Visibility = weighted score across engines × prompts × markets

The important bit is not aggregating locations too early. A 50-location client might have excellent AI visibility in Dallas and terrible visibility in Austin. A national average would hide that. Current local-AI tracking guidance similarly recommends treating the individual location as the measurement unit. gracker.ai

One thing I'd avoid

I wouldn't build this around a generic “ChatGPT rank tracker.” AI answers are stochastic, and different engines cite different sources. You need to retain the raw answer + prompt + engine + timestamp + citation URLs + location context, not just a score. www.cheers.tech

If I were running your agency: I'd demo Cheers and LLM Pulse first, and evaluate them specifically on whether you can export/API-access the raw citation records at the location × prompt × engine level. If neither gives you that granularity, I'd use Rankability as the citation collection layer and put your own client/location reporting database on top.

If by “capture citations from AI assistants” you mean “show me when ChatGPT/Perplexity/Gemini/etc. cite or recommend my client, which source they cited, and tie that result to the correct business location,” I’d treat that as AI visibility/citation tracking, not traditional citation tracking.

What I’d use

Best practical stack for an agency:

• AI citation tracker: Citations.io — specifically tracks ChatGPT, Gemini, Perplexity and Claude, including the cited URLs, competitors, prompts and citation share. citations.io
• Local/location data layer: BrightLocal — strong for managing individual locations, citations, GBP, reviews, geo-grid rankings and client reporting. It also has APIs if you want to build your own dashboard. www.brightlocal.com

That combination is considerably closer to what you're describing than something like a traditional citation builder.

The important part: location attribution

I would structure your data around:

Client → Location → Prompt → AI Engine → Response → Citation → Source URL → Citation Type

For example:

The location needs to be part of the prompt and the tracking record, rather than trying to infer location afterward.

That's especially important because AI visibility is highly query/location dependent. Current local-search research indicates that unstructured citations, reviews, industry-relevant sites and location-relevant content are significant AI visibility factors. www.brightlocal.com

If you're building this into your own client product

I'd actually go one step further and not make any single third-party SaaS your system of record.

Use an AI visibility tool/API to collect:

• prompt
• engine
• timestamp
• location
• answer
• cited URLs
• cited domain
• cited business
• competitor mentions
• citation position/order
• whether client's location was explicitly identified
• whether the citation points to the client's own site vs. a third party

Then use something like BrightLocal's API for the traditional local data layer—locations, listings, reviews, rankings and citations. Their API is explicitly designed for agencies/platforms managing many locations. www.brightlocal.com

One caveat: don't equate “AI mentioned the business” with “AI cited the business.” Those should be separate metrics. A good system should record both.

My recommendation

If you're an agency with, say, dozens/hundreds of local-business locations, I'd build around:

BrightLocal = location/local SEO source of truth
Citations.io (or another AI visibility API) = AI citation collection
Your database/dashboard = client/location attribution + reporting

If you tell me roughly how many clients/locations you have and whether you want an off-the-shelf tool or an API you can integrate into your own SaaS, I can narrow this down to the best 2–3 options and compare API access, per-location pricing, AI engines covered, prompt-level data, and white-label reporting.

If your core requirement is “capture exactly what an AI assistant cited, then attribute that citation to the correct physical location”, I’d look at this as an AI visibility + local entity tracking problem, not just traditional AEO.

My shortlist

1. Yext Scout — best fit for multi-location agencies

Yext is the one I’d investigate first if you manage lots of local-business clients. Scout explicitly supports AI citation analysis, geographic/location performance, prompts, competitors, and citation source domains, and can track ChatGPT, Gemini, Perplexity, Claude and Google. www.yext.com

The important part for you is that it goes beyond “brand mentioned” and is designed around location-level intelligence. You can benchmark competitors per location and identify which citations/sources are driving visibility.

Yext Scout

2. SearchDock — potentially the most directly aligned

SearchDock specifically markets geo-targeted AI citation tracking, including city/location-specific prompts across ChatGPT, Perplexity and Gemini, alongside local-pack tracking. searchdock.iowww.yext.com

If your workflow is something like:

Client → 25 locations → 100 local prompts → AI response → cited URLs → which location was recommended → competitor comparison

this is very close to the product category I'd want.

SearchDock Local Business

3. Local Glyph — good lightweight option

Local Glyph tracks local-business visibility across ChatGPT, Google AI Overview and Perplexity, with individual prompt responses and categorization. localglyph.com

I'd consider it if you're an agency that doesn't need the broader listings/knowledge-graph infrastructure of Yext.

Local Glyph

One important distinction

I wouldn't report simply:

“Client X received 47 AI citations.”

That's misleading because AI responses are non-deterministic. Repeated searches can return different citations, and recent research specifically recommends treating citation visibility as a statistical estimate rather than an exact fixed number. arxiv.org

For your clients, I'd capture something more like:

Then calculate location-level AI share of voice over repeated runs.

If you're building this yourself

I'd actually consider building a thin layer on top of an AI-search data provider/API rather than trying to make Yext or another platform your entire reporting system.

Your data model should have:

client → location → prompt → engine/model → run → response → mentioned entity → recommended entity → citation → cited URL → source domain → location attribution

The tricky piece is location attribution. A citation isn't inherently tied to a physical location. You need to resolve the cited entity against a canonical location record using things like:

• Google Business Profile / Place ID
• exact NAP
• website location URL
• schema LocalBusiness / @id
• address
• phone
• domain
• third-party directory listing
• service-area relationships

That lets you answer the much more valuable client question:

“Which sources are causing AI assistants to recommend our Charlotte location, and which sources are causing them to recommend our Matthews location?”

That's considerably more useful than generic “AI visibility.”

My pick: If you're an agency with multi-location clients, start with Yext Scout vs. SearchDock. Yext is the more mature local-entity/data infrastructure play; SearchDock appears more purpose-built around the exact geo AI citation tracking use case. searchdock.iowww.yext.com

If you tell me roughly how many clients/locations you manage and whether you want an off-the-shelf SaaS or an API you can build your own dashboard around, I can narrow this down to the best 2–3 options and compare pricing/API capabilities.

If your goal is agency-grade tracking of AI citations for local businesses, with the citation attributable to the specific location, I’d avoid treating this as a generic “AI visibility” problem. You need a geo-aware citation monitoring layer.

What I’d use

1. Local Glyph — best fit if you want something purpose-built for local SEO

Local Glyph tracks ChatGPT, Google AI Overviews, and Perplexity, lets you inspect the exact prompt/response, and supports prompt categories/tags. That makes it a good starting point for an agency managing many local clients. localglyph.com

2. GrackerAI — best if multi-location attribution is the core requirement

GrackerAI specifically recommends running the same prompt set separately in each market and measuring citation frequency, share of voice, sentiment, and accuracy at the location level rather than just the brand level. gracker.ai

3. Citations.io — good general-purpose citation monitor

Citations.io tracks actual AI answers and citations across ChatGPT, Gemini, Perplexity, and Claude, including competitors and the sources being cited. It's more brand-oriented, though, so I'd want to verify its location model before making it the backbone of a local SEO reporting system. citations.io

The important part: your data model

For your use case, I would structure every observation something like:

That distinction between brand mention → location mention → recommendation → actual URL citation is important. A business can be mentioned without being cited, and the corporate/homepage URL can be cited while the AI is actually recommending a particular location.

Also, don't rely on one query/run as a definitive metric. AI answers are stochastic: identical prompts can produce different citations, so repeated sampling and aggregation are much more meaningful than a single “AI visibility score.” arxiv.org

If you're building this into your agency stack

I'd actually consider building a thin attribution layer around an existing AI-visibility API/tool, rather than trying to build the entire AI-search tracker yourself.

Your differentiator would be:

AI response → citation → source URL → entity/business → specific location → client

Then you can give clients reporting like:

Chicago – Location #3

38% AI citation visibility
14/40 local prompts cited this location
+11% vs. previous month
7 citations from third-party sources
4 citations from the location page
3 competitor locations appearing more frequently

That's much more compelling to a local-business client than “your AI visibility score is 62.”

One other reason I'd make the location the primary reporting unit: recent local-AI research is finding substantial variation between AI systems and locations, and even established businesses can be invisible to individual systems. arxiv.org

My shortlist:
Local Glyph if you want something ready to use → GrackerAI if multi-location analysis is paramount → custom attribution layer if this is something you want to turn into a proprietary agency product.

If you tell me roughly how many clients/locations you manage (e.g. 20 clients × 5 locations), I can recommend the architecture and likely API/tool stack I'd use.

Yes. For local-business clients, I’d treat this as AI visibility / citation intelligence, but with location as the primary reporting dimension rather than just tracking a brand.

What I’d use

1. Cheers — best fit if you manage multi-location local businesses.
Cheers explicitly positions itself around multi-location service brands. It tracks local/category/intent prompts, recommendation share, cited sources, competitors, and market-level performance. That is very close to your use case. www.cheers.tech

2. Rank Prompt — worth evaluating for an agency workflow.
Rank Prompt is built around AI visibility across ChatGPT, Gemini, Perplexity, Google AI experiences, Claude, etc., and is aimed partly at agencies needing repeatable reporting and benchmarking. Its API/webhook capabilities are particularly interesting if you want to build your own client/location reporting layer.

3. Birdeye — good if your clients already use it for reputation/local SEO.
Birdeye's AI visibility product supports a single-location vs. brand view and reports citations, competitors, and visibility. birdeye.com That makes it attractive if you want AI visibility alongside reviews, listings, and reputation management.

4. Build your own measurement layer on top of an AI-visibility platform if this is something you want to sell as a differentiated agency product. The important part is your data model.

I'd structure each observation roughly as:

Client
  └── Location
       ├── Market: Seattle, WA
       ├── Category: plumber
       ├── Prompt: "best plumber near me"
       ├── Engine: ChatGPT
       ├── Date/run
       ├── Recommended? yes/no
       ├── Position: #2
       ├── Citation URL
       ├── Citation domain
       ├── Citation type: directory/review/site/forum/etc.
       ├── Competitors mentioned
       └── Sentiment / factual accuracy

That distinction matters because "Brand X was cited 43 times" isn't very useful to a local client. You want to be able to say:

Bellevue location: cited in 31% of relevant AI answers
Seattle location: cited in 18%
Tacoma location: cited in 7%

And then drill into which sources caused each location to appear.

There's also a methodological reason to avoid relying on a single query/run: AI answers are stochastic, and recent research finds substantial variation in citations between repeated queries. arxiv.org A good system should therefore aggregate repeated observations rather than treating one ChatGPT answer as a definitive ranking.

If I were building this for an agency

I'd probably test Cheers and Rank Prompt first, then choose based on whether their API/export gives you the granularity you need.

If your actual goal is "I want to capture every AI citation, resolve it to the correct physical location, and put that into my own client dashboard", I can also map out the ideal technical stack + APIs/data model for building that yourself, including how I'd handle ChatGPT, Gemini, Perplexity, AI Overviews, citations, geolocation, and multi-location attribution.

If your core requirement is “show me exactly what AI assistants cite, and tie that visibility back to a specific physical location,” I’d use Yext Scout as the primary platform.

My shortlist

Why I'd pick Yext Scout: it explicitly models AI visibility by location, keyword and competitor, captures citation sources, and supports ChatGPT, Gemini, Perplexity and Claude. Its API/MCP access is particularly interesting if you want to build your own client-facing reporting layer rather than just use somebody else's dashboard. www.yext.com

For example, you could have a client record like:

Client: ABC Dental
Location: Orlando, FL
Query: "best dentist near Winter Park"
AI: ChatGPT
Mentioned: Yes
AI position: #2
Citation sources:
  - abc-dental.com
  - healthgrades.com
  - yelp.com
  - local-news-site.com
Sentiment: Positive
Competitors:
  - Competitor A
  - Competitor B
Scan date: 2026-08-10

That is much closer to the data model I'd want for an agency.

Where Local Falcon fits

Local Falcon is worth serious consideration if geographic attribution is more important than having the broadest AI-engine coverage. Its model of local visibility is naturally suited to this problem: scan a geographic grid, run local queries, and see where the business appears. Community reports specifically mention its ChatGPT scans exposing citation sources and the actual response at individual grid points. www.reddit.com

I'd choose:

• Yext Scout → multi-location agency, API, structured location data, scalable reporting.
• Local Falcon → local SEO agency where geo-grid visualization is the killer feature.
• OtterlyAI → if the main objective is citation intelligence across many AI engines, rather than precise local attribution. Otterly captures the actual AI response and citation URLs and can export raw responses/citation data. help.otterly.ai

One important distinction

Don't confuse AI citation tracking with local citation tracking.

Traditional local citation software answers:

"Is this business listed correctly on Yelp, Bing, directories, etc.?"

What you're asking for is:

"When someone in this specific geographic area asks an AI assistant for this service, what answer did it give, which location did it recommend, and which sources did it cite?"

That's a substantially different dataset.

And the location dimension matters. Yext's Scout documentation explicitly separates branded/unbranded local queries and tests questions such as "What are the top 10 [Category] near [Location]?" at the local level. help.yext.com

If you're actually building this into your own product

I'd not build the entire collection infrastructure from scratch initially.

I'd use Yext Scout/API as the data source, then build your own:

Location → Prompt → AI engine → Response → Mention → Position → Citation → Citation domain → Sentiment → Timestamp

That gives you a clean relational structure that can power:

• per-location AI visibility scores
• citation/share-of-voice reports
• competitor comparisons
• "why aren't we showing up?" reports
• citation-source recommendations
• monthly client reports
• before/after SEO measurements
• agency-wide dashboards

Scout explicitly exposes API data for custom applications and reports, and its current citation data includes citation count, source domains and performance by model. www.yext.com

If you tell me roughly how many client locations you're talking about (e.g. 50, 500, 10,000+) and whether you want to build your own dashboard, I can recommend the specific stack/API architecture rather than just the SaaS tool.

If your core requirement is “capture AI citations and attribute each citation to the correct physical location,” I’d look for a multi-location AI visibility platform, not a generic SEO rank tracker.

My shortlist

1. Citations.io — best fit for citation capture
   • Tracks ChatGPT, Gemini, Perplexity and Claude.
   • Captures the actual answers and cited sources.
   • Lets you see competitors and citation share.
   • Good if your primary need is “show me exactly what AI cited.” citations.io
   • Caveat: I would verify its multi-location/client architecture before committing.

2. Lighthouse — broader AI visibility
   • Tracks 10+ AI platforms.
   • Includes citation tracking, sentiment, competitors and page-level recommendations.
   • Better if you're selling a broader GEO/AEO reporting service rather than just citation monitoring. www.ahoylighthouse.com

3. Genwolf — good monitoring-oriented option
   • Daily prompt tracking across multiple LLMs.
   • Keeps answer history and identifies mentions, citations and source domains. www.genwolf.ai

But for local businesses, there's an important distinction

You don't merely want:

“ChatGPT cited example.com.”

You want something closer to:

Client: Joe's Dental
Location: Brooklyn, NY
Prompt: “Best dentist near Park Slope accepting new patients?”
AI: ChatGPT
Mentioned: Joe's Dental
Rank/position: #2
Citation: example.com/locations/brooklyn
Citation source: Yelp
Location confidence: Brooklyn, NY
Date/run: Aug. 8, 2026
Competitors cited: X, Y, Z

That location-level attribution is the part I'd make a hard requirement when evaluating vendors.

Local AI visibility is particularly dependent on geographic context, structured data, listings, reviews and other local signals. Yext's analysis of millions of AI citations specifically found that location context is important to AI-generated local search results. www.youtube.com

Also, don't measure this from a single prompt/run. AI answers and citations are stochastic; recent research shows substantial citation variability between repeated runs. arxiv.org

If you're building this into your agency

I'd actually consider building a thin attribution layer on top of an AI visibility API/data provider, rather than making the vendor's dashboard your product.

Your data model could be:

Client → Location → Prompt → AI Engine → Run → Answer → Mention → Citation → Source URL → Location Attribution

Then you can report things like:

• AI visibility by location
• citation rate by location
• AI recommendation position
• citations to each location page
• third-party citations supporting each location
• competitor visibility by market
• citation growth over time
• hallucinated/wrong address detection
• prompts where one location appears but another doesn't

My pick: start by testing Citations.io for raw citation collection, then see whether its API/data model can support your location hierarchy. If it can't, I'd look for an API-first provider rather than forcing a consumer-facing dashboard into an agency workflow.

If you tell me how many clients/locations you manage (e.g. 50 clients × 10 locations) and whether you need an API/white-label dashboard, I can narrow this down to the best 2–3 options and compare their pricing/features.

If your clients are local/multi-location businesses, I’d avoid a generic “AI visibility” tool and choose something that treats location as a first-class dimension.

My shortlist

My first choice for an agency: Unhide if your priority is client reporting and separating locations. It specifically offers per-location analytics, tracks AI visibility across 5+ models, and supports agency multi-tenancy and white labeling. www.tryunhide.com

Unhide

If you want a more established local-search ecosystem: Yext Scout is particularly interesting for multi-location businesses. Yext's current research is based on 155.5 million location-grounded AI citations and finds that websites and listings account for roughly four out of five citations in its dataset. www.yext.com

Yext Scout / Yext

If you want one dashboard combining traditional local SEO and AI: SearchDock explicitly combines local-pack rankings, geo-targeted AI citations, and per-location Search Console data. searchdock.io

SearchDock

One important distinction

I'd make sure the product captures the actual citation, not merely:

“Client was mentioned by ChatGPT.”

For each observation, you ideally want:

client → location → prompt → AI engine → response → cited URL → cited source/domain → position/mention → competitor citations → timestamp

For example:

ABC Dental — Kansas City location
Prompt: “Best Invisalign dentist near me”
Engine: ChatGPT
Mention: ABC Dental
Citation: example.com/kansas-city/invisalign
Competitors cited: XYZ Dental, 123 Orthodontics
Location context: Kansas City, MO
Captured: Aug. 7, 2026

That distinction matters because AI visibility and citation attribution aren't the same thing. Current research also suggests that location substantially changes which sources AI systems cite. www.yext.com

If you're building this into your own product

I would actually consider using an API/data-collection layer rather than buying a dashboard. You'd want to run standardized prompts with controlled geographic context, save the complete responses/citations, and associate each run with a specific location_id.

That gives you something much more valuable to clients than an “AI visibility score”: a historical citation database by location.

If you tell me roughly how many clients/locations you manage (e.g. 20 clients / 200 locations), I can recommend whether I'd buy an existing platform, use an API, or build the citation-tracking layer yourself, including the likely cost/architecture.

If your goal is to answer "Which AI assistants mentioned my client's business, for which location, and can I attribute that mention to a specific store or office?", there isn't a perfect analytics platform yet because AI assistants generally do not expose referral data the way Google Search does.

Instead, most agencies are building a stack that combines AI monitoring with attribution.

A good setup looks like this:

For local SEO agencies, I'd recommend:

1. BrightLocal

• Manage NAP consistency
• Citation audits
• Google Business Profile monitoring
• Multi-location support

This handles the traditional local SEO side.

2. Profound
One of the strongest enterprise platforms for AI search visibility.
It can:

• Monitor prompts
• Detect when businesses are mentioned
• Show citation sources
• Compare competitors
• Track changes over time

3. CallRail

This is where attribution happens.

Create:

• unique phone numbers per location
• unique landing pages
• location-specific forms

When someone says:

"ChatGPT recommended ABC Dental"

you can attribute that lead to:

• AI
• specific location
• campaign

4. GA4 + BigQuery

Capture:

• referral traffic
• landing pages
• conversions
• UTM parameters

Although ChatGPT traffic isn't always labeled cleanly, you can increasingly identify AI-originating sessions and analyze their behavior.

If you want location-level AI citation reporting

Suppose your client has:

• Dallas
• Austin
• Houston

Run scheduled prompts like:

Best dentist in Dallas

Emergency dentist in Dallas

Invisalign near downtown Dallas

Best cosmetic dentist in Dallas

Then repeat for every city.

Store:

• assistant (ChatGPT/Gemini/Claude/Perplexity)
• date
• prompt
• cited URLs
• businesses mentioned
• ranking position
• confidence
• location

This becomes a historical dataset.

Build your own monitoring

Many agencies are moving toward custom solutions using APIs.

Pipeline:

1. Generate hundreds of local prompts.
2. Query multiple AI assistants (where APIs permit).
3. Extract:
   • businesses mentioned
   • citations
   • URLs
   • Google Business Profile references
4. Map each mention to a client location.
5. Store results in PostgreSQL or BigQuery.
6. Visualize in Looker Studio or Power BI.

This gives you reporting such as:

along with the exact citations each assistant used.

What I'd recommend for an agency

If you're managing dozens or hundreds of local businesses, a combination of:

• BrightLocal (local SEO and citation management),
• an AI visibility platform such as Profound or Peec (AI mention and citation monitoring),
• CallRail (lead attribution),
• and a custom prompt-monitoring pipeline backed by a database and dashboard,

will give you much more actionable reporting than relying on any single off-the-shelf tool.

One question that affects the recommendation: are you trying to attribute actual customer leads that originated from AI assistants, or track how often AI assistants cite each client's locations in their responses? Those are related but require different technical approaches.

If your goal is to know which AI assistants (ChatGPT, Gemini, Claude, Perplexity, Copilot, etc.) are mentioning a client's business and which location those mentions refer to, there isn't a perfect off-the-shelf attribution solution yet. AI assistants generally don't pass referrer data the way traditional search does.

Instead, most agencies are building a stack with a few components.

For agencies managing many local businesses, I'd recommend something like this:

1. AI citation monitoring

These platforms continuously ask LLMs prompts like:

"Best HVAC company in Tampa"

or

"Best dentist near Temple Terrace"

and record which businesses appear.

Some of the stronger options are:

• Profound (enterprise)
• Goodie AI
• Otterly.AI
• Peec AI
• Scrunch AI

These are excellent for measuring AI visibility, although most are aimed at brands rather than local SEO.

2. Location-level attribution

This is where most tools fall short.

If you have 50 locations, create prompts such as:

• Best roofer in Tampa
• Best roofer in Brandon
• Best roofer in Riverview
• Best roofer in Wesley Chapel

Each prompt maps to one GBP/location.

Now you can calculate:

• appearance rate
• average rank
• sentiment
• competitors shown
• sources cited

This becomes a location-specific AI visibility score.

3. Capture actual AI traffic

In GA4, look for referrers from services such as:

• chatgpt.com
• perplexity.ai
• gemini.google.com
• copilot.microsoft.com

Also inspect server logs and Cloudflare logs, since some AI traffic won't show cleanly in analytics.

4. Build your own monitoring system

If you're an agency, this is probably what I'd build.

Components:

• Scheduled prompts
• APIs from OpenAI, Anthropic, Gemini, Perplexity (where available)
• Store responses
• Extract:
  • business name
  • location
  • phone
  • URL
  • citations
• Compare over time
• Dashboard by client and location

Then report metrics like:

Location: Tampa

ChatGPT:
Appeared: 82%

Gemini:
Appeared: 71%

Claude:
Appeared: 48%

Perplexity:
Appeared: 90%

Most cited source:
- Yelp
- BBB
- Client website
- GBP

Competitors:
ABC Roofing
XYZ Roofing

This is much more valuable than simply reporting rankings.

5. Attribute mentions to specific locations

If you have multiple locations under one brand, use consistent location identifiers across your ecosystem:

• Dedicated location pages
• Unique LocalBusiness schema for each location
• Individual Google Business Profiles
• Consistent NAP information
• Location-specific review content

When an AI response mentions "ABC Plumbing – Brandon," you can reliably attribute that mention to the Brandon location.

What I'd recommend for an agency

If you're managing dozens or hundreds of local businesses, I'd combine:

• Profound or Peec AI for AI visibility monitoring.
• GA4 plus server or CDN logs for measuring incoming AI traffic.
• A custom prompt-testing pipeline that runs city- and service-specific prompts on a schedule and attributes every mention to a specific Google Business Profile or location page.

That combination gives you both visibility (are we being recommended?) and performance (are users actually clicking through?).

If your end goal is to productize this for clients, there's also an opportunity to build an "AI Local Rank Tracker" that goes beyond traditional local SEO tools. Existing platforms generally focus on brand-level AI visibility; relatively few provide robust, location-by-location attribution for agencies managing multi-location businesses.

If your goal is to know when ChatGPT, Gemini, Claude, Perplexity, Copilot, etc. mention a client's business or location and attribute those mentions to a specific branch, you're solving an emerging problem often called AI visibility or LLM citation tracking.

There isn't a perfect "Google Analytics for AI assistants" yet, so most agencies combine multiple approaches.

Here's what I'd recommend.

Option 1: Build your own (my recommendation for agencies)

If you manage dozens or hundreds of local businesses, I'd build a system instead of relying entirely on one vendor.

Architecture:

• Scheduled prompts
  • "Best plumber in Pittsburgh"
  • "Emergency dentist near Cranberry Township"
  • "HVAC company in Wexford"
• Run them against
  • ChatGPT
  • Gemini
  • Claude
  • Perplexity
• Capture
  • businesses mentioned
  • ranking/order
  • citations/URLs
  • maps shown
  • confidence
  • timestamp
• Store everything in Postgres or BigQuery.

Then map every citation to:

Business
Location ID
GBP ID
City
Prompt
Assistant
Citation URL
Mentioned?
Position
Date

Now every location gets its own history.

Option 2: Use an AI visibility platform

Some good options are:

• Profound
• Peec AI
• Goodie AI
• Otterly AI
• Scrunch AI

These are great if your clients are enterprise or multi-location brands.

The downside is that most are designed for large brands rather than agencies with hundreds of SMB locations.

Option 3: Track incoming AI traffic

AI assistants increasingly send referral traffic.

In GA4, look at referrals from:

• chatgpt.com
• perplexity.ai
• gemini.google.com (when available)
• copilot.microsoft.com

You'll know:

• which landing pages receive AI traffic
• conversions
• revenue

But this doesn't tell you why the assistant cited the business.

Option 4: Citation extraction pipeline (what I'd build)

Since many AI assistants now include citations, you can extract and analyze them automatically.

For each response:

Prompt
↓

LLM Response
↓

Extract citations

↓

Normalize URLs

↓

Determine source
    Google Business Profile
    Yelp
    Facebook
    BBB
    Local news
    Chamber
    Website
    Reviews

↓

Match to location

↓

Store

After a few thousand prompts you can answer questions like:

• Which locations are cited most?
• Which directories generate the most AI citations?
• Which city performs best?
• Which assistant favors Yelp?
• Which assistant favors GBP?
• Which assistant cites Reddit most?

That's actionable for local SEO.

Option 5: Create an "AI Citation Score"

For each location, calculate something like:

Visibility Score

40% Mention frequency

20% Average ranking

20% Citation authority

10% Review source diversity

10% Website citation frequency

That gives every location a measurable KPI similar to Local Pack rankings.

If you're building a SaaS

A compelling stack would be:

• Backend: Python (FastAPI) or Node.js
• Database: PostgreSQL
• Queue: Redis + Celery or BullMQ
• LLM orchestration: OpenAI Responses API plus provider APIs where appropriate
• Browser automation (where permitted): Playwright
• Dashboard: Next.js
• Charts: Recharts or Apache ECharts
• Analytics: BigQuery or ClickHouse for large-scale prompt history

The differentiator I'd focus on isn't just "does ChatGPT mention my client?"—it's location-level attribution. Imagine a dashboard like:

BrightSmile Dental
├── Pittsburgh
│   AI Visibility: 82
│   ChatGPT: #2
│   Gemini: #1
│   Claude: Not Mentioned
│   Top Citation: Google Business Profile
│
├── Cranberry
│   AI Visibility: 63
│   ChatGPT: #5
│   Gemini: #3
│   Claude: #4
│
└── Wexford
    AI Visibility: 91
    ChatGPT: #1
    Gemini: #1
    Claude: #2

That location-specific view is something many existing AI visibility tools don't emphasize, making it a strong value proposition for agencies serving multi-location businesses.

If your goal is measuring when AI assistants (ChatGPT, Gemini, Claude, Perplexity, etc.) mention a client's business location and attributing that to a specific location, there isn't a single off-the-shelf analytics platform that does this perfectly yet. Most agencies are building a stack from several components.

A good architecture looks like this:

If you're an agency serving local businesses

I'd build something like this:

AI Prompt Monitor
        ↓
Collect responses
        ↓
Extract:
- business mentioned
- address
- phone
- URL
- citation source
- ranking
- confidence
        ↓
Normalize to Location ID
        ↓
Database
        ↓
Client Dashboard

For example:

Client:
ABC Dental

Locations

#102
Dallas

#205
Plano

#330
Frisco

If ChatGPT answers:

"ABC Dental in Plano is highly rated..."

your parser maps that to:

Location ID 205

Assistant:
ChatGPT

Prompt:
best dentist plano tx

Rank:
2

Citation:
Google Business Profile

Date

Now you have location-level reporting instead of business-level reporting.

For capturing AI citations

The difficult part isn't storing them—it's generating and parsing the responses.

Most people use:

• Scheduled prompt testing
• Headless browser automation
• APIs where available
• LLM extraction to parse responses into structured JSON

Extract fields like:

assistant
prompt
response
mentioned_business
location
address
phone
website
citation_urls
knowledge_source
position
timestamp

If you need location attribution

Don't rely on business name alone.

Instead create a canonical location record:

location_id

business_id

name

street

city

state

zip

gbp_cid

google_place_id

lat

lng

Then use fuzzy matching on:

• address
• phone
• city
• website
• Google Place ID
• GBP CID

This is much more reliable than matching names, since many multi-location businesses reuse the same brand name.

Referral attribution

One challenge is that AI assistants often don't send traditional referral data. To improve attribution:

• Use unique landing pages for each location.
• Include UTM parameters in links where possible.
• Analyze server logs for known AI user agents where applicable.
• Correlate spikes in AI mentions with traffic to location-specific pages.

Keep in mind that not every AI assistant exposes outbound click or citation data, so attribution will often be probabilistic rather than exact.

If you're building a SaaS

I'd structure it around three core services:

1. Discovery Engine
   • Runs thousands of local prompts.
   • Captures responses from multiple AI assistants.
   • Identifies cited sources and mentioned businesses.

2. Entity Resolution Engine
   • Matches mentions to canonical business locations.
   • Resolves duplicates and ambiguous names.
   • Tracks changes over time.

3. Reporting Engine
   • Visibility score by location.
   • Citation sources by location.
   • Prompt rankings.
   • Competitor comparisons.
   • Historical trends and alerts.

This gives agencies reports like:

ABC Dental

Dallas
Visibility: 82%

Plano
Visibility: 61%

Frisco
Visibility: 37%

Top citation sources:
- Google Business Profile
- Yelp
- Healthgrades
- Practice website

Competitor overtakes:
+3 this month

For agencies managing dozens or hundreds of local businesses, this location-centric model scales much better than treating each brand as a single entity. The key is assigning every AI mention to a canonical location ID and storing the supporting citation data, so you can report accurately across prompts, assistants, and time.

If your goal is to know when an AI assistant (ChatGPT, Gemini, Claude, Perplexity, etc.) recommends one of your clients' locations and tie that mention back to a specific business location, you're really solving two different problems:

1. AI citation/mention monitoring
2. Location-level attribution and reporting

There isn't a single product that does both perfectly today, but here's what I'd recommend.

Best overall architecture

For an agency managing multiple local businesses:

• Data warehouse: BigQuery or Postgres
• Crawler/monitor: Your own prompt-testing system
• LLM visibility platform: Profound, Goodie AI, or Peec AI
• Dashboard: Looker Studio, Metabase, or Power BI

This gives you location-level reporting instead of just domain-level reporting.

Option 1: Build your own (my recommendation)

Run scheduled prompts for every location.

Example:

best plumber in Nashville

emergency plumber near Franklin TN

HVAC near Brentwood

best dentist in Murfreesboro

Capture:

• assistant
• date
• prompt
• business mentioned
• rank
• citations
• URLs cited
• confidence
• location

Your schema might look like:

This gives you historical tracking over time.

Option 2: Profound

One of the strongest enterprise AI visibility platforms.

Pros:

• tracks AI search visibility
• monitors prompts
• competitor comparisons
• citation monitoring
• enterprise reporting

Cons:

• expensive
• focused more on brands than thousands of SMB locations

Good if you're managing 20–100 locations.

Option 3: Peec AI

Good for:

• GEO (Generative Engine Optimization)
• AI search rankings
• prompt monitoring
• citation tracking

Better pricing than Profound.

Option 4: Goodie AI

Built specifically around AI search optimization.

Useful for:

• monitoring prompts
• seeing which sources AI assistants cite
• identifying missing citations

Option 5: Nightwatch + custom AI layer

If you're already tracking:

• Google Business Profile
• local rankings
• Maps rankings

you can add an AI monitoring service alongside it and correlate the data yourself.

If you're managing hundreds of locations

I'd build a pipeline like this:

Location Database
       ↓
Generate Local Prompts
       ↓
Query ChatGPT API
Query Gemini API
Query Claude API
Query Perplexity API
       ↓
Extract:
- business names
- citations
- URLs
- rankings
       ↓
Normalize
       ↓
Store in SQL
       ↓
Dashboard

Then every client location gets reports such as:

Location: Nashville

ChatGPT:
✓ Mentioned
Position #2

Perplexity:
✓ Mentioned
Position #1

Gemini:
Not mentioned

Claude:
Mentioned

Top cited domains:
cityofnashville.gov
yelp.com
bbb.org
clientsite.com

Attribution to specific locations

The challenge is that AI assistants often mention a brand rather than a specific branch.

To attribute correctly, you'll need entity resolution based on signals like:

• city in the response
• address
• ZIP code
• phone number
• Google Business Profile URL
• location page URL
• service area

For example:

ABC Plumbing - Franklin
ABC Plumbing - Nashville
ABC Plumbing - Murfreesboro

The parser should map:

"ABC Plumbing on Cool Springs Blvd"

to

location_id = 48392

rather than simply

brand = ABC Plumbing

This location resolution layer is what makes reporting useful for multi-location businesses.

My recommendation

If you're building this as an agency product rather than just buying software, I'd use:

• Promptfoo or a custom prompt runner to execute standardized prompts across multiple AI assistants.
• OpenAI, Anthropic, Google, and Perplexity APIs (where available) to collect responses programmatically.
• A lightweight extraction pipeline (using an LLM plus deterministic matching) to identify businesses, cited URLs, and resolve each mention to a specific location ID.
• Postgres or BigQuery for storing prompt runs and historical data.
• Metabase or Looker Studio for client-facing dashboards.

That approach gives you a defensible dataset with historical trends, competitor comparisons, and location-level attribution—capabilities that current off-the-shelf GEO platforms only partially address for large local-business portfolios.