Short answer: AI search visibility is measured across four layers: query-set monitoring, citation tracking, share of voice, and attribution. There is no Google Analytics for AI search yet. The 6 tools worth considering in 2026 each close part of the picture. A free spreadsheet method works for the first 8 weeks.
89% of brands now appear at least once in AI-generated answers. Only 14% of those brands have any tracking in place. That is the central marketing-analytics gap of 2026, and it is wider than the "we don't track social mentions" gap was in 2014.
The problem is structural. There is no Google Analytics for AI search yet. ChatGPT, Perplexity, Claude, and Gemini don't expose API access to historical citation data. They don't pass referral headers the way Google does. They don't share basic visibility data with the brands they're citing. So marketers who want to know "are we showing up in AI answers" have to assemble that picture themselves.
This is the practical companion piece. Four measurement layers, six tools worth shortlisting, and a no-cost DIY method that gets you most of the way there.
The 4 measurement layers
1. Query-set monitoring
The foundation. Define the 30–150 queries your buyers actually ask AI engines about your category and your brand. "Best Korean snail mucin," "is Beauty of Joseon legit," "Korean dermatology clinic Seoul," etc. Then run those queries through each AI engine on a regular schedule (weekly or biweekly) and capture the answers.
2. Citation tracking
Within each answer, which URLs does the AI cite? Which subreddits? Which competitor brands appear? Track citations as structured data (URL, source, position in answer, sentiment, query that triggered it). Over time, this becomes your visibility dataset.
3. Share of voice
Out of the 30–150 queries, in how many does your brand appear at all? In how many does it appear in the top 3 citations? What percentage of brand mentions in your category go to you vs to competitors? Share of voice in AI search is the closest equivalent to brand-tracking surveys, but with weekly granularity instead of quarterly.
4. Attribution
The hardest layer. When an AI answer mentions your brand, does that translate into a visit, a search, a click? Most AI engines don't pass referral headers reliably, so attribution requires indirect signals: branded search volume in Google Trends, direct traffic spikes, and AI-referral UTM parameters where available.
The 6 tools worth comparing in 2026
The category is new enough that no one has won yet. Below are the six we'd shortlist if we were budgeting in Q2 2026. None is perfect. The right pick depends on which layer you're trying to close first.
Profound. The most established. Strong on query-set monitoring and citation tracking across ChatGPT, Perplexity, Claude, Gemini, Copilot. Best for enterprise teams that want a single dashboard. Enterprise-oriented pricing.
Brandlight. The data leader. Their citation index is the source most marketing data reports cite. Best if you want to benchmark your category at large, not just your own brand. Mid-market pricing.
AthenaHQ. Strongest on share-of-voice scoring across competitor sets. Good if you're trying to convince a CMO with a single visualization. Subscription-based.
Goodie AI. Faster setup, lighter weight, less customization. Good for early-stage brands testing whether they should invest more deeply. Lower entry price.
Otterly.ai. European-built, strong on attribution signals and brand sentiment tracking. Cleanest UI of the group.
Peec.ai. Newer entrant with aggressive pricing. Worth piloting if budget is tight.
The free DIY method
If you have no budget for a tool yet, the free version of AI-visibility tracking takes about 4 hours to set up and 30 minutes per week to maintain.
Step 1. Build your query set. 50 queries minimum, covering brand searches, product searches ("best X for Y"), comparison searches ("X vs Y"), and condition or use-case searches.
Step 2. Set up a Google Sheet with columns: Query, AI Engine, Date, Answer Summary, Cited URLs, Cited Reddit Thread, Brand Mentioned (Y/N), Brand Position (1–5), Sentiment, Action Required.
Step 3. Pick one AI engine to start (Perplexity is easiest because it cites sources visibly). Run all 50 queries, paste the answers and citation lists into the sheet.
Step 4. Repeat weekly. After 4 weeks, you have a trendline. After 8 weeks, you can detect movement.
Step 5. Expand to the second AI engine once the first is running smoothly. ChatGPT second, Claude third, Gemini fourth.
This won't scale past one analyst. But it costs nothing and answers the question "are we showing up at all" within 4 weeks.
KPIs that actually matter
Forget vanity counts. The four KPIs worth presenting to a CMO:
- Citation rate. % of relevant queries where your brand appears in the top 3 citations.
- Sentiment-weighted citations. Citation count adjusted for whether the mention is positive, neutral, or negative.
- Share of category citations. Of all brand mentions in your category, what % go to you vs your top 5 competitors.
- AI-referred traffic delta. Direct + AI-referred traffic growth vs the same period last year, controlled for paid spend.
FAQ
What is AI search visibility?
AI search visibility is how often a brand appears in answers generated by ChatGPT, Perplexity, Claude, Gemini, and other AI engines for relevant buyer queries.
What is an AI citation?
An AI citation is a source, URL, or referenced page that an AI engine uses to support its answer.
How often should brands track AI search visibility?
Weekly or biweekly tracking is usually enough for most brands. Fast-moving categories may need weekly monitoring.
Can I track AI visibility without a tool?
Yes. The free spreadsheet method described above can take you through the first 8 weeks of measurement. After that, manual tracking gets expensive in time and a tool becomes worth the cost.
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