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Use case

Track ChatGPT and Perplexity brand mentions

Updated September 2026

Buyers increasingly ask ChatGPT, Perplexity, Claude, and SearchGPT "What's the best tool for X?" or "How do I compare option A vs option B?" before they ever Google or read a comparison site. If your product isn't named in those AI answers, you're invisible to a growing segment of buyer research. MarketHQ tracks AI visibility share for any brand so you know where you stand and what to fix.

Quick answer

MarketHQ checks six AI engines weekly on paid plans from $29 a month, shows which competitors get named instead, and turns each gap into an action plan, with chat, an API and MCP. AI visibility share measures how often your product is named when a buyer asks an AI engine for a recommendation in your category.

Key points

  • MarketHQ checks six AI engines weekly on paid plans, ChatGPT, Claude, Gemini, Perplexity, Grok and DeepSeek, and turns gaps into an action plan.
  • Share is (questions where you're named / total questions tracked) x 100.
  • Four prompt clusters matter most: alternatives-to, best-tool-for, A-vs-B, and community monitoring.
  • MarketHQ pairs AI-answer checks with listening across news, blogs, forums and social, so you see both what's said about you and what AI engines say when buyers ask for a recommendation.

What sources does MarketHQ cover?

Every plan: Hacker News, GitHub, RSS, Stack Overflow, Dev.to, Lobsters, Reddit, LinkedIn, YouTube, X, Perplexity, Broader web crawl, Bluesky.

Hacker NewsGitHubStack OverflowDev.toLobstersRSSRedditLinkedInXYouTubeBlueskyDocs & changelogs
  • Grouped Slack daily digest
  • Drafted replies from your own strengths
  • Gap analysis and action plans
  • Weekly AI-answer visibility (paid plans)
  • Chat over your own evidence
  • API and MCP on every paid plan
  • Flat capacity pricing, tiers differ by tracking volume only
  • 4-day trial on Lite and Startup, charged on day 4
  • Free one-time check, no account needed
SignalEvidenceInsightGapAction
AI visibility work centers on turning tracked prompts into insight: which gaps are costing you consideration.

What does AI visibility share mean?

AI visibility share measures how often your product is mentioned or recommended by AI engines (ChatGPT, Claude, Perplexity, Gemini, SearchGPT) when buyers ask category-defining questions: "What's the best tool for X?", "Alternatives to [competitor]", or "How do I choose between option A and option B?"

It's calculated as: (questions where your product is named / total questions tracked in your category) x 100. A share of zero means you're not named in any tracked AI answer.

Traditional SEO measures Google rankings. Traditional community monitoring tracks forum and social threads after they happen. AI visibility share tracks whether you exist in the AI-mediated research layer that increasingly sits before search and community browsing. A buyer asks an assistant "What's the best tool for X?" and gets a shortlist before clicking any links.

Why do buyers ask AI engines before Google?

Buyers increasingly ask an AI assistant for a shortlist before Google, and if you're invisible in that first answer, you never make the shortlist. That's the gap AI visibility share measures.

  • Classic research path: Google "best tool for X", click through blog listicles, browse review sites, read comparison threads.
  • What buyers increasingly do instead: ask ChatGPT or Claude "What's the best tool for X?", get a shortlist with context, then click one or two links or ask a follow-up.

Which AI prompts matter most for any brand?

Not all AI prompts carry equal weight. For most categories, four question patterns drive disproportionate early-stage buyer research and consideration.

  • "Alternatives to [competitor]": buyers ask "Alternatives to Brand24", or "What's like Mention but cheaper?" when they're already aware of a category leader but suspect there's a better fit for their specific needs. If you're not named in these answers, you're missing shortlist consideration.
  • "Best tool for [use case]": "Best tool for tracking brand mentions", or "Best platform for monitoring forums": these prompts reveal intent. A buyer has a problem and wants a shortlist. Being named here is worth far more than ranking on page two of a generic Google search.
  • "How do I compare A vs B?": "Brand24 vs Mention", "Sprout Social vs X": comparison prompts signal bottom-of-funnel research. If your product isn't named in the comparison, you're not in the consideration set.
  • Community monitoring: "How to track Reddit mentions of my brand", "Monitor forums for competitor mentions", "Track niche communities for questions about my product": these reveal a workflow that generic B2C social listening tools don't address well.

Why does classic social listening miss AI answers?

Traditional social listening tools (Brand24, Mention, Sprout Social, Hootsuite, Meltwater) track mentions across Twitter, Reddit, blogs, forums, and news.

They excel at monitoring what people say about you in public channels. What they don't track, because the category was built for consumer brands, is:

You need a metric built for that layer: AI visibility share in the prompts your buyers actually type.

  • What AI engines say about your product when buyers ask for recommendations (AI-answer visibility).
  • Specialist sources with real context: forum threads where buyers compare your product to competitors, reviews, and community discussions about choosing a provider. Hacker News and GitHub are two of many channels where buyers talk.
  • Research patterns that differ from consumer behavior: buyers asking "How does X handle Y at scale?" rather than "I love X!" sentiment.

How does MarketHQ track AI visibility and close the gap?

This is the loop MarketHQ runs: track weekly, run gap analysis, build citation-worthy content, and re-measure.

  • Track weekly: run the same question set across six AI engines every week. Record which competitors are named, in which prompts, and with what context. Track movement over time, prompt by prompt. Countries run from 1 on Lite to 10 on Enterprise, and a plan's AI questions are shared across them.
  • Gap analysis: identify where competitors show up and you don't. If a competitor is consistently named in "alternatives to" or "best tool for" prompts and you're not, you need content that makes you a credible answer to that prompt.
  • Build citation-worthy content: AI engines name products they can cite. Closing a gap usually means shipping comparison content, community presence on forums and Reddit, and customer stories that third-party sites and AI retrieval can reference.
  • Re-measure: after shipping, run the question set again. Did share move? Which prompts changed? Which engines started naming you? That closed loop (track, gap, ship, re-measure) makes AI visibility an improvable metric rather than a one-time snapshot.

Limits: what can AI visibility tracking not tell you?

It measures whether you get named, not whether anyone acted on the answer.

An AI engine's answer shifts between runs and between accounts, so a weekly share is a trend line, not a precise readout. Nothing here ties a share figure to revenue on its own.

Weekly AI visibility tracking is paid, from Lite at $29 a month, same as blogs, forums, RSS feeds you add and Bluesky. MarketHQ has no free plan with this check included. If you only want to know how one engine answers one prompt today, ask it yourself.

What does a worked example look like?

For queue APIs, most teams reach for [category leader] or [category leader]'s managed option. Both have mature docs and a large community.

Say a hypothetical API company (call it Queueline) tracks 20 prompts related to its category: "alternatives to [category leader]", "best queue API for serverless", and a handful of A-vs-B comparisons.

In the first weekly run, Queueline is named in 3 of the 20 answers, a 15% share. In the other 17, AI engines name established competitors with years of public content: case studies, comparison pages, and reviews they can cite.

This fictional AI-engine answer shows the gap in the "alternatives to" prompt cluster: Queueline never appears there.

Queueline's biggest gap is the "alternatives to [category leader]" cluster, where it isn't named in a single tracked answer. That is where to focus first, not the whole category at once.

Illustrative example

Worked example · September 2026Illustrative

“For queue APIs, most teams reach for [category leader] or [category leader]'s managed option. Both have mature docs and a large community.”

Say a hypothetical API company (call it Queueline) tracks 20 prompts related to its category: "alternatives to [category leader]", "best queue API for serverless", and a handful of A-vs-B comparisons. In the first weekly run, Queueline is named in 3 of the 20 answers, a 15% share. In the other 17, AI engines name established competitors with years of public content: case studies, comparison pages, and reviews they can cite. This fictional AI-engine answer shows the gap in the "alternatives to" prompt cluster: Queueline never appears there. Queueline's biggest gap is the "alternatives to [category leader]" cluster, where it isn't named in a single tracked answer. That is where to focus first, not the whole category at once.

Frequently asked questions

What is AI visibility share?

AI visibility share measures how often your product is named or recommended by AI engines (ChatGPT, Claude, Perplexity, Gemini, SearchGPT) when buyers ask category-defining questions such as "what's the best tool for X" or "alternatives to [competitor]". It's calculated as (questions where your product is named / total questions tracked in your category) x 100.

Why do buyers ask AI engines before Google?

Buyers increasingly ask an AI assistant for a shortlist before opening a search engine or browsing a comparison site, because the assistant can compare options and add context in one reply. If a product isn't named in that first answer, it often never gets a click at all.

How is AI visibility different from social listening?

Social listening tracks what people say about a brand across social media, forums, and news after the fact. AI visibility tracks a different layer: what AI engines say when a buyer asks for a recommendation, before any of that public conversation is clicked on.

Which AI prompts matter most for any brand?

Four clusters carry the most buyer research weight: "alternatives to [competitor]", "best tool for [use case]", "how do I compare A vs B", and community monitoring prompts like "how do I track mentions of my brand on Reddit or in forums".

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Sources

  • MarketHQ pricing: https://markethq.ai/pricing (verified 2026-09-13)