Answers that name a competitor

If you mean “what % of AI-generated answers mention/recommend my brand vs. competitors?”, you want an AI visibility / AI search analytics platform, not traditional SEO rank tracking.

My shortlist

• otterly.ai — probably the best straightforward fit. It tracks share of voice, brand mentions, sentiment, citations, and competitors across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot, and Claude. Otterly Otterly
• semrush.com — good if you're already using Semrush. Its Brand Performance reporting calculates AI SoV using both mention frequency and position, and benchmarks you against competitors. Semrush
• Profound — worth considering for enterprise-grade monitoring, especially if you need granular prompt, source, citation, and competitive analysis. Independent 2026 benchmarking also places it among the leading platforms. Benchline Reports Tomek Sikora
• Peec AI — particularly interesting if competitive share-of-voice analysis is the main objective; a 2026 benchmark specifically identified it as strong on competitive SoV. Benchline Reports

What I'd measure

Don't use just “mentions.” I'd build an AI SoV dashboard around:

AI Share of Voice → Brand Coverage → Recommendation/Position → Sentiment → Citations → Competitor Gap

For example:

Across 1,000 category prompts, Brand A appears in 31% of answers, Competitor B in 24%, Competitor C in 18%, etc.

Then break that down by AI engine, prompt/topic, country, competitor, and time period. This is important because AI answers are dynamic, so a one-off manual test isn't a reliable measurement. Semrush Cognizo

If I had to pick one to test first: OtterlyAI. It is unusually explicit about the exact metric you're asking for—share of AI answers naming your brand versus competitors—and provides the underlying mention/citation data rather than just a generic “AI visibility” score. Otterly

If you tell me your industry + 3–5 competitors + whether you're SMB/mid-market/enterprise, I can also give you a side-by-side comparison of the best 3 tools for your specific use case.

Tools in this category are usually called AI visibility / Generative Engine Optimization (GEO) / AI share-of-voice platforms. They don’t “crawl” private assistant chats; instead, they run controlled prompt sets against AI assistants, capture outputs, and calculate brand mentions, rankings, citations, and competitor share. Visibility.so Foglift

Common options:

• Profound — tracks brand presence in AI-generated answers and measures competitive visibility. Foglift
• Seerly — runs prompts across ChatGPT, Perplexity, Claude, and others to show visibility and share-of-voice trends. Seerly
• Visibility.so — monitors how brands appear in AI answers and compares competitors. Visibility.so
• Profound, Otterly, and similar tools are often used for AI-search monitoring benchmarks. Foglift

For voice channels (Alexa, Siri, Google Assistant, in-car assistants), the tooling is less mature. You typically need a combination of:

• speech assistant testing frameworks,
• conversation analytics platforms,
• your own synthetic-query harness that records assistant responses,
• NLP classification to score brand mentions and recommendations.

For an enterprise setup, the usual architecture is:

1. Build a prompt/query library from customer intents.
2. Run queries across ChatGPT, Claude, Gemini, Perplexity, voice assistants, etc.
3. Store responses.
4. Extract entities, sentiment, ranking position, citations, and recommendations.
5. Calculate share of voice:

brand mentions ÷ total competitor mentions within the tracked answer set.

If you mean monitoring your own customers’ real voice-assistant interactions (call center/IVR/agent conversations) rather than synthetic AI answers, that is a different tool category.

Yes. This is an emerging category usually called AI Search Visibility, Generative Engine Optimization (GEO), Answer Engine Optimization (AEO), or AI Share of Voice tracking. These solutions measure which brands/content are being surfaced, cited, and recommended in AI-generated answers instead of just tracking traditional search rankings. slatehq.com

The better platforms can answer questions like:

• Which competitors appear when buyers ask AI tools category questions?
• Which prompts are they winning that we are losing?
• Which competitor URLs/content assets are being cited by AI models?
• What topics, claims, reviews, comparisons, or authority signals are causing them to win?
• What is our AI share of voice vs. competitors over time? www.cyted.appwww.tryprofound.comwww.pingaura.ai

Some solutions in this space:

A typical workflow looks like this:

1. Build a prompt universe
   • “best CRM for startups”
   • “alternatives to [category leader]”
   • “who provides enterprise [solution]”
   • “compare X vs Y”

2. Run those prompts across AI engines
   • ChatGPT
   • Gemini
   • Perplexity
   • Google AI Overviews
   • Copilot

3. Capture:
   • brands mentioned
   • recommendation position
   • sentiment
   • cited domains
   • cited URLs
   • content themes

4. Create a competitive gap map

Example:

The important distinction: the winning content is often not the highest Google-ranking page. AI systems may favor content with stronger evidence, clearer entities, reviews, citations, structured information, and third-party mentions. www.pingaura.ai

If you are evaluating this for a marketing/SEO team, I would look for a tool that provides prompt-level competitor analysis + citation URL discovery, not just a percentage “AI visibility score.” The actionable question is not only “who is winning?” but “which exact sources and content patterns are causing them to win?” www.cyted.appwww.tryprofound.comwww.pingaura.ai