LLM visibility tools are the measurement layer for generative engine optimization. Without one, the question "is this working?" cannot be answered with anything more rigorous than checking ChatGPT manually. With one, you can build a weekly reporting cadence that drives editorial decisions. Ten vendors, six recommended, four specialist. CTAIO Labs ran the field test; this guide is the selection grade.
Key takeaways
- Six recommended — Profound, Peec AI, Otterly, AthenaHQ, Evertune, and Scrunch earn a recommendation across the scoring rubric. Four others fit specific use cases.
- What to measure — Citation share by query class. Pick fifty to one hundred prompts that map to your highest-value pages and track citation rate weekly across at least ChatGPT, Perplexity, and Gemini.
- The freshness trap — Monthly-refresh vendors look cheaper on paper but produce trend data that's too lagged to drive editorial cycles. Weekly refresh is the operational minimum.
- Pricing axis — Per-query pricing scales with measurement depth; per-domain pricing scales with site count. Most teams under-buy on the dimension they need most.
What an LLM visibility tool actually does
The category is two years old in 2026, and the vendor pitches still vary widely. Underneath the marketing, every serious tool does the same four things:
- Run queries against each engine. A fixed query set executed on a schedule against ChatGPT, Perplexity, Gemini, Claude, and Bing Copilot.
- Parse the responses for citations. Extract which URLs the engine cited, in what position, and which spans the citation attaches to.
- Track share of voice over time. Per-domain, per-URL, per-query-class citation rates with trend lines.
- Compare against competitors. Same queries, multiple domains tracked, share of citation reported across the set.
Several vendors also analyse the response sentiment, suggest content changes that might lift citation rate, and integrate with editorial tooling like Notion or contentful headless CMS. Those features are useful but rarely the deciding factor. The four primitives above are what you are actually buying.
The four selection criteria
Vendors split on the same four axes year after year. Weight them by the shape of your programme.
- Engine coverage. The first filter. Every vendor covers ChatGPT and Perplexity well; the differentiator is Gemini and Claude. If your audience is enterprise engineering or professional services, Claude coverage starts to matter. If your audience is consumer or research-led, Gemini matters more.
- Citation attribution accuracy. The vendors that attribute citations at the span level (which paragraph, not just which domain) produce dramatically more actionable data. The ones that only do domain-level attribution work for reporting but not for editorial diagnosis.
- Freshness. Weekly refresh is the operational minimum for editorial cycles. Daily helps in launches and crisis response. Monthly does not move fast enough to be useful as an editorial input.
- Pricing model fit. Per-query or per-domain. Map your query inventory and your domain count before you talk to any vendor; the conversation goes faster.
Scored comparison
The scoring rubric: engine coverage (five surfaces), refresh cadence, citation attribution accuracy, custom query sets, API access, competitor benchmarking, content optimisation suggestions, starting price, and pricing model. Fourteen axes across the ten vendors.
A note on pricing. Starting prices below reflect public tiers as of May 2026. The category is in active price competition; "Starter" pricing has compressed roughly 30% across the leaders during the last twelve months. Always confirm with the vendor before signing, and ask for the volume discounts that are common but rarely advertised.
| Feature | Profound | Peec AI | AthenaHQ | Otterly | Scrunch | Evertune | Rankscale | Bluefish | Semji | Goodie AI |
|---|---|---|---|---|---|---|---|---|---|---|
| Engine coverage | ||||||||||
| ChatGPT (with search) | Yes, native | Yes, native | Yes, native | Yes, native | Yes, native | Yes, native | Yes | Yes | Yes | Yes |
| Perplexity | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Limited | Yes | Yes |
| Gemini | Yes | Yes | Yes | Partial | Yes | Yes | Partial | Not yet | Partial | Partial |
| Claude with web search | Yes | Partial | Yes | Roadmap | Partial | Yes | Roadmap | Not yet | Not yet | Partial |
| Bing Copilot | Yes | Yes | Partial | Partial | Yes | Partial | Not yet | Not yet | Partial | Not yet |
| Data quality | ||||||||||
| Refresh cadence | Daily on Pro, weekly on Starter | Weekly | Weekly | Weekly | Weekly | Weekly | Weekly | Bi-weekly | Bi-weekly | Monthly |
| Citation attribution accuracy | High; cited-source spans | High | High | High; per-URL granularity | High | High | Domain-level mostly | Domain-level | High | Domain-level |
| Custom query sets | Yes, unlimited on Pro | Yes | Yes | Yes | Yes | Yes | Yes | Limited on free tier | Yes | Yes |
| Workflow fit | ||||||||||
| API access | Yes | Yes | Yes | Yes | Yes (Enterprise) | Yes | Beta | Roadmap | Yes | Not yet |
| Competitor benchmarking | Yes, native | Yes | Yes | Yes | Yes | Yes | Manual setup | Not yet | Yes | Manual setup |
| Content optimisation suggestions | Light | Yes | Yes | Light | Yes | Light | Not yet | Not yet | Yes (Semji's core) | Yes |
| Pricing | ||||||||||
| Starting price | $499/mo | $249/mo | Free tier; $199/mo paid | $129/mo | Custom enterprise only | Custom enterprise only | Free tier; $49/mo paid | Beta access only | $390/mo | Free tier; $99/mo paid |
| Pricing model | Per-domain + per-query | Per-domain tiers | Per-query | Per-domain tiers | Custom | Custom | Per-query, very granular | Free in beta | Per-content piece | Per-query |
The radar verdict
Same data, organised by recommendation tier. CTAIO Labs' Season 3 Episode 1 field test on three real brand portfolios is the empirical layer underneath this verdict; this version is the procurement-grade summary.
Recommended
- Profound. The premium pick. Best engine coverage (all five), strongest citation-attribution at the span level, native API, daily refresh on Pro. Tax: starting price is steep for small teams; reporting UX assumes a dedicated GEO ops person.
- Peec AI. Best ratio of capability to price in the category. Solid engine coverage, weekly refresh, content optimisation suggestions that actually feed back into editorial. The default pick for mid-market.
- Otterly. The pragmatist's pick. Per-URL citation granularity, friendly UI, $129/mo starter, the lowest serious entry point in the category. Coverage gaps on Gemini and Claude are the trade.
- AthenaHQ. Best free-to-paid ramp. Free tier produces real data, paid tier scales cleanly. Strong on Claude coverage where most others are partial. Pricing transparency is unusual in the category.
- Evertune. Enterprise-grade. Custom pricing only, but the only vendor in the recommended set with content-optimisation suggestions that read like a strategic adviser rather than a heuristic. Right for enterprise GEO programmes with a 7-figure content budget.
- Scrunch. Enterprise-only, but the strongest on multi-brand portfolios. The pick when you are tracking visibility for ten or more brands and need cross-portfolio analytics.
Specialist or watching
- Rankscale. Free tier and $49/mo paid make it the cheapest entry point in the category. Coverage is narrower (no Claude, partial Gemini) and citation attribution is mostly domain-level rather than URL-level. Right for solo operators and side projects.
- Semji. Not quite the same product as the others. Semji is content-optimisation-first, with visibility tracking as a complementary feature. The right pick if your primary workflow is editorial planning rather than measurement reporting.
- Goodie AI. Newer entrant, faster product cycle than most. Worth a free-tier evaluation; the recommendation status will shift as coverage gaps close.
- Bluefish. Beta-stage. Capable on the engines it covers, but ChatGPT-only at the moment is too narrow for production reporting. Re-evaluate at general availability.
How to pick (decision tree)
- If you are a solo operator or single-domain team with a tight budget, start with the free tier of AthenaHQ or Goodie AI, or Rankscale at $49/mo. Upgrade to Otterly at $129/mo when the query set passes 50 prompts.
- If you are an in-house team with one or two flagship domains, Peec AI at $249/mo is the default pick. Profound at $499/mo is the upgrade when you need daily refresh or span-level attribution.
- If you are an enterprise GEO programme with a multi-brand portfolio, evaluate Scrunch (multi-brand portfolio specialty) and Evertune (content-optimisation depth) in parallel. Both are custom-priced; expect a 4-week procurement cycle.
- If your primary workflow is editorial content optimisation, Semji is the differentiated pick. Treat visibility tracking as a secondary feature rather than the core product.
- If you only care about ChatGPT and you want the cheapest serious tool, Otterly at $129/mo. ChatGPT-and-Perplexity-only programmes can drop a tier from any of the vendors above.
Field evidence from CTAIO Labs
CTAIO Labs is the practitioner surface of our network. The Season 3 Episode 1 test ran all ten vendors against three real brand portfolios with disclosed methodology, scoring rubric, and per-vendor coverage scorecard. Use it as the empirical layer underneath the recommendation tiers above.
Related reads
Frequently asked questions
What is an LLM visibility tool?
A platform that measures how often your pages appear as cited sources inside generative engines like ChatGPT, Perplexity, Gemini, Claude with web search, and Bing Copilot. The standard workflow: define a set of queries that map to your high-value pages, run them against each engine on a schedule, parse the citations, and report on share of voice, brand mentions, and per-URL citation rate. Some vendors also analyse the responses for sentiment, recommend content changes, or compare against named competitors.
Which engines should I track first?
ChatGPT with search and Perplexity are the two highest-volume citation surfaces for most B2B and B2C content in 2026, and every vendor in this guide covers both well. Add Gemini next; it is increasingly material as Google AI Overviews and Gemini chat converge. Claude with web search is a strong fourth for engineering and professional audiences. Bing Copilot is the smallest meaningful surface but matters for some Microsoft-ecosystem queries. If you have to pick one engine to measure, pick ChatGPT.
How often should the data refresh?
Weekly is the operational minimum. Daily refresh is useful for high-stakes monitoring (launches, executive comms, crisis response) but rarely necessary for steady-state measurement. Monthly refresh is too slow to drive an editorial cycle; by the time you see the trend, three publishing cycles have passed. Profound is the only vendor in this guide that offers daily refresh as a standard tier feature; most others are weekly with bi-weekly fallbacks on entry tiers.
What does each vendor actually cost?
Starting prices in May 2026: Rankscale from $49/mo, Goodie AI free tier and $99/mo paid, Otterly $129/mo, AthenaHQ free tier and $199/mo paid, Peec AI from $249/mo, Semji from $390/mo, Profound from $499/mo, Bluefish in free beta, Evertune custom enterprise only, Scrunch custom enterprise only. The CTAIO Labs head-to-head includes per-vendor pricing notes including the specific tier they tested at; check there before signing a contract.
Per-query vs per-domain pricing, which scales better?
Depends on your shape. Per-query scales with measurement depth: each additional prompt costs more, but you can run as few or as many domains as you want. Per-domain scales with site count: a fixed monthly per-domain fee with usually a generous query allowance. Agencies and multi-brand operators prefer per-domain; in-house teams with one or two flagship domains and deep query sets prefer per-query. Profound and AthenaHQ offer per-query; Peec AI and Otterly offer per-domain.
Do these tools actually predict revenue impact?
Only at the aggregate level, and only when you build the connection yourself. Citation share by query class is the closest leading indicator to AI-referred conversions, but the conversion side has to be measured separately in GA4 with channel groupings for the major AI domains. Treat the visibility tool as the upstream measurement and your analytics as the downstream; the two together produce a usable funnel, but neither tool predicts revenue alone.
How does this guide differ from the CTAIO Labs S3E1 test?
CTAIO Labs ran the head-to-head with real budget on three real brand portfolios and published the methodology, the per-vendor coverage scorecard, and the freshness deltas. That is the empirical layer. This guide formalises the same scoring for vendor-selection use: the verdict tiers, pricing comparison, and the buyer-side criteria you would put in front of finance to approve a contract. The two are designed to be read together; CTAIO is the field test, this guide is the procurement document.
What is the one mistake most teams make with these tools?
Buying the wrong tier. Most LLM visibility programmes underbuy on the query-set dimension and overbuy on the domain dimension. A serious editorial team running one domain with a 200-query reporting set will get more value from Profound Pro than from Peec AI Enterprise with three domains and 50 queries each. Map your query inventory before you talk to sales; that one prep step changes the procurement conversation.
What is an LLM visibility tool?
A platform that measures how often your pages appear as cited sources inside generative engines like ChatGPT, Perplexity, Gemini, Claude with web search, and Bing Copilot. The standard workflow: define a set of queries that map to your high-value pages, run them against each engine on a schedule, parse the citations, and report on share of voice, brand mentions, and per-URL citation rate. Some vendors also analyse the responses for sentiment, recommend content changes, or compare against named competitors.
Which engines should I track first?
ChatGPT with search and Perplexity are the two highest-volume citation surfaces for most B2B and B2C content in 2026, and every vendor in this guide covers both well. Add Gemini next; it is increasingly material as Google AI Overviews and Gemini chat converge. Claude with web search is a strong fourth for engineering and professional audiences. Bing Copilot is the smallest meaningful surface but matters for some Microsoft-ecosystem queries. If you have to pick one engine to measure, pick ChatGPT.
How often should the data refresh?
Weekly is the operational minimum. Daily refresh is useful for high-stakes monitoring (launches, executive comms, crisis response) but rarely necessary for steady-state measurement. Monthly refresh is too slow to drive an editorial cycle; by the time you see the trend, three publishing cycles have passed. Profound is the only vendor in this guide that offers daily refresh as a standard tier feature; most others are weekly with bi-weekly fallbacks on entry tiers.
What does each vendor actually cost?
Starting prices in May 2026: Rankscale from $49/mo, Goodie AI free tier and $99/mo paid, Otterly $129/mo, AthenaHQ free tier and $199/mo paid, Peec AI from $249/mo, Semji from $390/mo, Profound from $499/mo, Bluefish in free beta, Evertune custom enterprise only, Scrunch custom enterprise only. The CTAIO Labs head-to-head includes per-vendor pricing notes including the specific tier they tested at; check there before signing a contract.
Per-query vs per-domain pricing, which scales better?
Depends on your shape. Per-query scales with measurement depth: each additional prompt costs more, but you can run as few or as many domains as you want. Per-domain scales with site count: a fixed monthly per-domain fee with usually a generous query allowance. Agencies and multi-brand operators prefer per-domain; in-house teams with one or two flagship domains and deep query sets prefer per-query. Profound and AthenaHQ offer per-query; Peec AI and Otterly offer per-domain.
Do these tools actually predict revenue impact?
Only at the aggregate level, and only when you build the connection yourself. Citation share by query class is the closest leading indicator to AI-referred conversions, but the conversion side has to be measured separately in GA4 with channel groupings for the major AI domains. Treat the visibility tool as the upstream measurement and your analytics as the downstream; the two together produce a usable funnel, but neither tool predicts revenue alone.
How does this guide differ from the CTAIO Labs S3E1 test?
CTAIO Labs ran the head-to-head with real budget on three real brand portfolios and published the methodology, the per-vendor coverage scorecard, and the freshness deltas. That is the empirical layer. This guide formalises the same scoring for vendor-selection use: the verdict tiers, pricing comparison, and the buyer-side criteria you would put in front of finance to approve a contract. The two are designed to be read together; CTAIO is the field test, this guide is the procurement document.
What is the one mistake most teams make with these tools?
Buying the wrong tier. Most LLM visibility programmes underbuy on the query-set dimension and overbuy on the domain dimension. A serious editorial team running one domain with a 200-query reporting set will get more value from Profound Pro than from Peec AI Enterprise with three domains and 50 queries each. Map your query inventory before you talk to sales; that one prep step changes the procurement conversation.
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