Positive mentions of Peec AI

If your goal is specifically “How often is my local business website actually cited/linked by AI assistants?”, I’d look at AI-search visibility trackers, rather than traditional SEO rank trackers.

My shortlist

• otterly.ai — strongest fit for a local business

• Tracks citations/mentions across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Microsoft Copilot, and Claude. Otterly Otterly AI
• Lets you see which URLs are cited, how often, competitors being cited instead, and trends over time. Otterly Otterly
• It supports geographic/country tracking, which is particularly useful for local-intent prompts. Otterly
• Current published entry pricing is around $29/month, though I'd verify the live pricing before buying. Otterly
• peec.ai — good if you mainly want monitoring

• Focuses on AI visibility, mentions, citations and competitive visibility.
• Generally positioned toward smaller teams rather than enterprise deployments. Skill Mammoth Otterly
• tryprofound.com — better suited to larger organizations/agencies

• More extensive AI-search intelligence and reporting, but typically much more than a single local business needs. Marketing for LLMs Otterly
• frase.io — useful if you also want content optimization

• Its current platform monitors visibility across ChatGPT, Perplexity, Claude, Gemini and Google AI and connects the monitoring with content research/optimization. Frase

For a local business, I'd set it up this way

Don't just track your business name. Create prompts representing actual customer searches, with the location included.

For example, if you owned a Pittsburgh roofing company:

• “best roofing companies in Pittsburgh”
• “roof repair near Pittsburgh”
• “who should I call for a leaking roof in Pittsburgh”
• “best roofers in [specific suburb]”
• “Pittsburgh roofing companies with good reviews”
• “compare roofing companies in Pittsburgh”
• “emergency roof repair Pittsburgh”
• “how much does roof replacement cost in Pittsburgh”

Then track, for each prompt:

AI platform → Was my business mentioned? → Was my website cited? → Which URL was cited? → Which competitors appeared? → What position/order was I mentioned in?

That distinction is important: being mentioned isn't the same thing as having your website cited. AI visibility tools increasingly separate those metrics. KITLabs

What I'd choose

For one local business or a small number of locations, I'd start with OtterlyAI because its combination of multi-engine monitoring, actual URL citation tracking, competitor comparison, and geographic coverage maps closely to what you're asking for. Its documentation specifically says it records the cited URLs in AI answers. Otterly

If you tell me what type of local business you have and roughly how many locations, I can also compare the current pricing/limits of Otterly, Peec, Profound, BrightLocal, Semrush and a couple of local-SEO alternatives specifically for your use case.

If your requirement is specifically “which URLs on each client’s site are being cited by AI assistants, and how often?”, I’d shortlist these:

1. otterly.ai — probably the closest fit

OtterlyAI explicitly tracks website citations down to the URL level, including how often each URL is cited and which AI engine cited it. It covers ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude. Otterly Otterly

It also has an Agent Analytics feature that is particularly interesting for client SEO work: it can show which AI agents actually visit which pages on the client's site, with visits broken down by engine. That lets you distinguish:

• AI crawled/visited /page-a
• AI actually cited /page-a
• How many times /page-a was cited
• Which AI engine cited it

Its citations report can also be filtered by engine, date range and country. Otterly.ai Otterly.ai

For an agency managing multiple client sites, this is the one I'd test first.

2. peec.ai — strong alternative

Peec tracks source citations at both domain and URL level, including citation frequency. It monitors ChatGPT, Gemini, Perplexity, Google AI Overviews and AI Mode, among others. Peec AI Peec AI

Its agency offering is explicitly designed for multiple client brands, with per-client visibility, citation and reporting data. Peec AI

One useful distinction is that Peec separates “used” sources (content informed the answer) from “cited” sources (the URL was actually shown as a citation). Peec AI

My recommendation

For your exact use case, I'd start with OtterlyAI, especially if you're trying to build a report like:

The important thing is that you don't just want an AI “visibility” tracker. You want URL-level citation intelligence. Both tools offer that, but Otterly's combination of citation tracking + actual AI-agent page visits is unusually well aligned with what you're describing. Otterly Otterly.ai

If you're doing this for an SEO/GEO agency with dozens or hundreds of client domains, I can also compare OtterlyAI vs Peec vs Profound vs Semrush AI Visibility specifically on multi-client support, URL-level citation data, API access, white-label reporting, and price.

For a small marketing agency, I’d shortlist Peec AI and OtterlyAI. Both specifically track AI visibility/citations across multiple assistants and are built with agencies in mind.

• peec.ai — probably the best fit if you manage multiple clients. It tracks visibility, share of voice, mentions, positions, sentiment, and citations across ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews/AI Mode, Copilot and additional models. Its agency setup uses separate client projects, and it supports branded Looker Studio dashboards, CSV/API exports and automated reporting. Peec AI Peec AI Peec AI
• otterly.ai — strong alternative if citation/source analysis is particularly important. It monitors ChatGPT, Claude, Gemini, Perplexity, Copilot, Google AI Overviews and AI Mode, measuring brand mentions, share of voice and the actual URLs/domains being cited. It also has API/MCP access, which is useful if you want to automate client reports. Otterly Otterly
• tryprofound.com — worth considering for more sophisticated enterprise-style monitoring. Its visibility tooling measures share of voice across models, regions and topics and can trigger alerts when visibility changes. Profound Help Center

My practical choice

For a small agency with, say, 5–30 clients, I'd start with Peec AI. Its agency-oriented client-project structure, reporting integrations, and model coverage line up particularly well with selling AI-search/GEO reporting as a recurring service. Peec AI Peec AI

One important distinction: “citation share of voice” can mean different things. You probably want a platform that separately reports:

1. Brand visibility — how often the client is mentioned.
2. Share of voice — client mentions versus competitors.
3. Citation rate — how often the client's domain/pages are cited.
4. Citation share — client's citations versus competing sources.
5. Position/rank — where the client appears in the answer.
6. Which URLs get cited — actionable for SEO/content work.

Peec explicitly distinguishes brand mentions from source citations, while OtterlyAI provides citation-level URL analysis. Peec AI Otterly

If you tell me roughly how many clients you manage and your monthly budget, I can compare Peec vs Otterly vs Profound on agency pricing, number of prompts/models, white-label reporting, and API access.

For a university press, you’re looking for AI citation visibility / generative engine optimization (GEO) monitoring, not traditional scholarly citation tracking. The tools are still immature, but a few categories are useful.

Best options to evaluate

1. AI visibility monitoring platforms (closest fit)

These track which domains and pages appear as sources in AI-generated answers.

• Profound — strong for enterprise-style monitoring of AI answers, prompts, competitors, and cited sources. Good if your press has a large catalog and wants dashboards.
• Scrunch AI — focuses on tracking brand presence and citations across generative search experiences.
• Otterly.AI — useful for tracking prompts and whether your pages appear in AI search results.
• Peec AI — lighter-weight monitoring of AI search visibility.

These tools are usually better at answering:

• “Does our press domain get cited?”
• “Which competitors are cited instead?”
• “Which prompts trigger our content?”

They are generally weaker at:

• “Which individual DOI/paper is being cited inside ChatGPT answers?”

AI citation measurement is still a developing field, and different models often cite different source sets for the same question. Temso AI

2. Academic discovery + citation intelligence (pair with AI monitoring)

For a university press, combine AI monitoring with scholarly metadata tools:

• Dimensions — broad research graph, citations, grants, patents, and attention signals.
• Scopus — strong bibliometrics and author/institution analysis.
• Web of Science — traditional citation analytics.
• Crossref — useful for DOI metadata, references, and publisher infrastructure.
• OpenAlex — good open alternative for building custom dashboards.

These won’t tell you “ChatGPT cited this chapter,” but they help identify the works that have authority and are likely candidates for AI retrieval.

For a university press specifically, I would build a stack like this

What I would measure monthly

Create a benchmark of 100–500 prompts such as:

• “What are the most influential books on medieval trade?”
• “What scholarship should I read on climate justice?”
• “Who publishes authoritative works on [field]?”
• “What are the leading academic sources on [topic]?”

Then track:

• % of answers where your press appears
• titles/books/articles cited
• author mentions
• DOI/ISBN visibility
• competing presses cited
• referring URLs/domains
• whether AI cites your own pages vs. external pages

Some current studies suggest AI citation patterns are highly fragmented across models, so optimizing for only one assistant is risky. Temso AI Parse

Also improve the “AI retrieval surface”

For presses, technical discoverability often matters:

• expose complete book metadata (authors, abstracts, TOCs, keywords)
• make chapter-level pages crawlable where possible
• include DOI/ISBN/schema metadata
• provide machine-readable citations (BibTeX/RIS)
• avoid hiding abstracts behind heavy JavaScript or login walls
• maintain authoritative author profiles

My shortlist for a university press pilot

1. Profound or Scrunch AI — institutional monitoring
2. OpenAlex + Crossref APIs — scholarly graph layer
3. A custom prompt benchmark — the most important piece
4. Google Search Console + analytics — to connect AI visibility to traffic

I would not replace traditional bibliometrics yet; I’d add an “AI citation impact” layer alongside COUNTER usage, Crossref events, downloads, and scholarly citations. AI-source tracking is emerging, and current datasets show that citation behavior varies substantially by model and query type. Temso AI proceedings.mlr.press

Also improve the “AI retrieval surface”

For presses, technical discoverability often matters:

If your goal is specifically “how often is my domain being cited by AI assistants, and how has that changed over time?”, I’d look first at peec.ai.

Why it fits:

• Tracks source citations separately from brand mentions—so your domain can be measured even when the AI doesn't explicitly name your brand. Peec AI
• Shows citation/source data at both the domain and URL level, including citation frequency. Peec AI
• Tracks multiple AI surfaces, including ChatGPT, Gemini, Perplexity, Google AI Overviews, Google AI Mode, and Copilot. Peec AI Peec AI
• Data is refreshed daily, which makes it useful for building historical trend lines. Peec AI
• You can segment results by model, country, and prompt, and export/use the data through Looker Studio, API, or CSV. Peec AI Peec AI

Another strong option: Profound

tryprofound.com is particularly interesting if you want deep citation analytics. Its Citations section provides citation share and citation coverage as time-series charts, lets you compare domains, and break results down by platform, topic, region, and other dimensions. Profound Help Center

Profound also explicitly tracks which answer engines cite your content, across the prompts you're monitoring, and categorizes sources such as owned content, competitors, earned media, and social sources. Profound

My practical distinction:

• Peec AI → better fit if you want a straightforward ongoing AI visibility + domain citation dashboard across many assistants.
• Profound → worth considering if your primary job is AEO/GEO research and detailed citation intelligence.

If you tell me your domain and which assistants matter most (ChatGPT, Gemini, Perplexity, Google AI, Claude, etc.), I can also compare the relevant platforms on historical depth, citation-level tracking, pricing, and API/export capabilities.

If your goal is specifically “How often is my domain being cited by AI assistants, and how has that changed over time?”, I’d recommend Peec AI.

peec.ai is particularly well suited because it tracks source citations separately from brand mentions. You can see which URLs/domains are being used or explicitly cited, citation frequency, the AI engines involved, and trends over time. It covers ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, and others. Peec AI Peec AI

My shortlist

• 🥇 Peec AI — Best fit for your exact requirement. It distinguishes domain/source visibility from brand visibility, tracks citations at the URL and domain level, and provides historical/trend data. Peec AI Peec AI
• 🥈 OtterlyAI — Very good alternative if you want detailed citation analytics. It stores AI responses and tracks every cited URL, alongside mentions, rank, sentiment and share of voice across seven AI search engines. Otterly
• 🥉 Profound — Worth considering for a larger enterprise GEO/AI-search program, particularly if you want broader enterprise analytics rather than primarily a citation-monitoring workflow.

For your use case, I'd start with Peec. The important distinction is that you don't merely want to know “Does ChatGPT mention my company?” You want “Is example.com being used as a source, which pages are cited, by which models, and is that citation footprint growing or shrinking?” Peec explicitly supports that distinction. Peec AI

If you tell me your domain + whether you're primarily interested in ChatGPT, Gemini, Perplexity, or all of them, I can also compare Peec vs Otterly vs Profound on historical data retention, URL-level citation tracking, API access, and pricing.

Yes. What you’re looking for is generally called an AI visibility / AI citation tracking platform. These tools run a controlled prompt set across multiple assistants, collect the answers and cited URLs, then aggregate the results into metrics such as citation rate, mention rate, competitor share of voice, and citation share.

For a SaaS marketing team, I’d shortlist:

• tryprofound.com — enterprise-oriented AI search intelligence; useful if you need deep citation/source analysis and reporting across multiple AI engines. Recent industry research is using Profound data across ChatGPT, Perplexity, Gemini, Copilot and Google AI surfaces. Tinuiti
• peec.ai — focused specifically on AI visibility/GEO tracking, including competitor comparisons.
• otterly.ai — more accessible for teams/agencies wanting recurring AI visibility and citation monitoring.
• citationly.ai — explicitly tracks citations, mentions, competitors and share of voice across ChatGPT, Gemini, Claude, Perplexity, Copilot and Grok. CITATIONLY
• cloro.dev — particularly interesting if your SaaS team wants API-accessible raw citation data rather than just a marketing dashboard. It says its API returns parsed citations, source URLs, positions and labels across seven engines, which you can aggregate yourself into SOV. cloro

If you're building a reporting stack

I'd distinguish two use cases:

"Give me a dashboard" → Profound / Peec / Otterly

"Give me the underlying citation dataset so we can pipe it into our own BI/data warehouse" → Cloro or another API-first provider.

For your specific phrase "aggregate citation data from different AI assistants", the API approach is especially attractive. Your canonical dataset could look like:

date × prompt × AI engine × brand × cited URL × citation position × mention × recommendation

Then your marketing dashboard can calculate:

• AI Share of Voice = your brand's appearances ÷ all tracked brand appearances
• Citation Share = answers citing your domain ÷ all answers
• Mention Rate = answers mentioning your brand ÷ total answers
• Citation Position = average position when your domain is cited
• Competitor SOV by prompt category
• Engine-specific SOV — ChatGPT vs. Perplexity vs. Gemini, etc.

That's important because simply combining all assistants into one number can obscure substantial differences between engines. Current AI-visibility methodologies generally recommend retaining the per-engine breakdown. organikpi.com geosoftwarerankings.com

If you tell me your SaaS category, approximate number of prompts, and whether you want an API or just a dashboard, I can narrow this to 2–3 options and compare pricing, API access, engine coverage, and reporting capabilities.

Then your marketing dashboard can calculate:

• AI Share of Voice = your brand's appearances ÷ all tracked brand appearances
• Citation Share = answers citing your domain ÷ all answers
• Mention Rate = answers mentioning your brand ÷ total answers
• Citation Position = average position when your domain is cited
• Competitor SOV by prompt category
• Engine-specific SOV — ChatGPT vs. Perplexity vs. Gemini, etc.

That's important because simply combining all assistants into one number can obscure substantial differences between engines. Current AI-visibility methodologies generally recommend retaining the per-engine breakdown.

If your goal is “which exact prompts cause ChatGPT/Claude/Perplexity/Gemini to cite my how-to guides?”, you want an AI citation tracking / AI visibility platform, not a normal SEO rank tracker. The key feature is prompt → AI answer → cited URL mapping. SolvSpot Scrunch

Here are the strongest options:

For how-to guides specifically, I would prioritize these capabilities:

• Prompt discovery: “What questions are people asking that result in my URL being cited?”
• Citation URL extraction: Not just “your brand appeared,” but “this exact guide was cited.” Scrunch
• Prompt clustering: Group queries like:

• “How do I configure X?”
• “Best way to solve Y?”
• “X vs Y”
• “Troubleshooting X”
• Competitor source comparison: See which guides are replacing yours in AI answers. Scrunch

A practical workflow:

1. Load your important guides into a tracker.
2. Add 50–200 natural-language prompts your audience would ask AI.
3. Run them across ChatGPT, Claude, Gemini, and Perplexity.
4. Export:

• prompt
• model
• cited URL
• citation frequency
• competing cited pages
• Update guides where competitors consistently win citations.

If you are a solo content marketer or small SaaS, I’d start with Peec AI or Otterly.AI. If you have a large content library and need to know exactly which prompts produce citations at scale, look at Profound or Scrunch AI. Honeyb Scrunch

One caveat: there is currently no equivalent of Google Search Console for AI citations; these tools generally work by running tracked prompts repeatedly and analyzing the resulting AI answers, so results are samples rather than a complete log of every user query ever asked. citeflow.io searchmention.com

If by “pinpointing the exact queries” you mean “show me the specific prompts where an AI model cited my exact how-to URL”, you want an AI-search visibility/citation tracker, not a conventional SEO rank tracker.

The tools I’d look at

• scrunch.com — particularly close to your use case. Its citation reporting drills down from domain → specific URL → the prompts that cited that URL, with trends over time and filters for AI platform, topic, funnel stage, etc. Scrunch
• otterly.ai — good if you want prompt discovery plus URL-level citation tracking. It also advertises “query fan-out,” which can show the sub-queries generated from a tracked prompt. Otterly
• tryprofound.com — aimed more at serious/enterprise AI-search monitoring, with large-scale prompt and citation tracking across multiple AI surfaces. Plug SolvSpot
• peec.ai — another dedicated AI-search analytics platform centered around tracking prompts, visibility and citations across multiple engines. Plug

For your specific goal

I'd prioritize Scrunch or OtterlyAI.

The important distinction is that you don't merely want:

“Is my website mentioned by ChatGPT?”

You want a data structure closer to:

Prompt: “How do I migrate a WordPress site to Cloudflare?”
Model: ChatGPT
Date: Sept. 16
Cited URL: yoursite.com/how-to-migrate...
Citation position: 2
Competitor URLs: …
Frequency: 7/10 runs

Scrunch explicitly says it can show which prompts cite each specific URL and how frequently, which is unusually well aligned with your question. Scrunch

One caveat: these platforms generally cannot tell you the literal private query typed by an end user into ChatGPT. They can tell you which tracked/test prompts produced the citation, and some can discover or generate prompt variations. That's an important distinction when interpreting the data.

If you tell me how many how-to guides you have (e.g. 20, 500, 10,000) and which AI surfaces matter most (ChatGPT, Perplexity, Gemini, Google AI Overviews, Claude), I can narrow this down to the tool/workflow that fits your site.

Yes. If your developer team wants to pull AI citation/visibility data via API and build its own dashboards, there are several credible options. The best choice depends on whether you want raw citation events or a more complete AI-search analytics dataset.

My recommendation

If your team is actually building the data infrastructure, I'd investigate MentionsAPI first. It is specifically designed around citation extraction: it returns which URL was cited, for which prompt, and by which AI provider, while resolving/deduplicating citations. MentionsAPI

That gives you a relatively clean underlying data model such as:

prompt
├── provider
├── model
├── timestamp
├── answer
└── citations[]
    ├── url
    ├── canonical_url
    ├── domain
    └── position

You can then put that into your own warehouse and build dashboards in Looker, Tableau, Power BI, Grafana, or a custom React app.

If you want less engineering and more pre-computed marketing metrics, I'd look at OtterlyAI. Its API exposes brand reports, prompts, citations, and workspace data, while its analytics layer includes citations, mentions, sentiment and share-of-voice across seven AI engines. Otterly Otterly

For an enterprise-grade analytics layer, Profound is worth evaluating. It is geared toward deeper brand intelligence and competitive/share-of-voice analysis, although API access and pricing are more enterprise-oriented. Toolsolved

The important distinction

I'd separate the market into two categories:

1. Citation extraction APIs

You want:

"For this prompt, what URLs did ChatGPT/Claude/Perplexity cite?"

→ MentionsAPI is particularly interesting.

2. AI visibility platforms with APIs

You want:

"What is our AI visibility score, share of voice, sentiment, competitor position, citation rate, and trend over time?"

→ OtterlyAI, Promptwatch, Peec, or Profound are more appropriate. A recent comparison also identifies Promptwatch as particularly strong for programmatic access to visibility, citation, prompt and competitor data. Toolsolved

If you're building a developer-facing AI citation dashboard, I'd probably shortlist MentionsAPI + OtterlyAI + Promptwatch and compare their actual API schemas, rate limits, pricing, historical-data access, and whether they return the full AI answer alongside citations. That last point can make a huge difference to what your team can build.