An LLM visibility score is only as useful as the observations behind it. Before choosing a tool, find out which answering surface it collects, how it selects questions and whether you can inspect failures as well as successful answers. Two dashboards can disagree without either describing the same experiment.
How we compared
We publish August. This guide compares vendors’ published product information, checked September 10, 2026. It is not a hands-on accuracy benchmark. The shortlist advice is our judgment; links let you inspect the underlying features and current plans.
What are you actually measuring?
A direct model API response, a search-enabled API response and an answer collected from a consumer search product are different observations. Model choice, retrieval, country, language, account context and conversation history can affect the result. Do not accept “tracks ChatGPT” as a complete collection method.
OpenAI’s bot documentation distinguishes search, training and user fetches. Google’s documentation describes its own AI search eligibility. These distinctions are a reason to ask for specific surfaces, not to assume every AI product retrieves the same evidence.
Nine tools to evaluate
| Tool | Published scope | Plan detail to check |
|---|---|---|
| August | Monitoring, reviewed drafts and follow-up evidence | Starter $49/month; Pro $99/month. Monthly billing. |
| Profound | Answer monitoring plus Agents for content work | Self-serve Starter and Growth; enterprise plans also available. |
| Peec AI | Daily monitoring with Actions recommendations | Check prompt, project and additional-model allowances. |
| Otterly.AI | Daily tracking, citations, GEO audits and recommendations | Lite lists $29/month on monthly billing; some engines are add-ons. |
| Evertune | Large prompt allowance, brand measurement and content features | Pro lists $800/month; inspect the included collection scope. |
| Rankscale | Multi-engine tracking, citations and recommendations | Check the credits needed for your prompt and engine schedule. |
| ZipTie | Multi-engine monitoring with configurable collection | Price depends on prompts, engines and checking frequency. |
| Scrunch | Monitoring, site auditing and Agent Experience Platform | Confirm monitoring and AXP scope separately. |
| Semrush | AI visibility research, prompt tracking and readiness audits | AI Visibility Toolkit has its own subscription and domain allowance. |
Ask for the collection record
| Field | Why you need it |
|---|---|
| Exact prompt | A changed question can explain a changed answer. |
| Engine and surface | An API run is not automatically a reproduction of a consumer app. |
| Time, location and language | Compare observations collected under relevant conditions. |
| Full answer and sources | Verify what the brand mention or citation actually meant. |
| Success or failure status | A timeout must not become a zero-visibility answer. |
| Sampling and aggregation method | Understand whether the score reflects one answer, repeated answers or a larger index. |
Match the shortlist to your measurement question
For a small, recurring prompt set, inspect August, Peec AI and Otterly.AI first. Ask each to show the underlying answers and how the report changes when a run fails. This is a suggested evaluation path, not a claim that the three products have identical collectors or reliability.
For larger research requirements, inspect Profound, Evertune and Semrush. Ask which insights come from your custom questions and which come from the vendor’s broader data. A large data pool is useful only if its topics, countries and collection method fit the decision you are making.
Rankscale and ZipTie are worth inspecting when collection configuration and usage budgeting matter. Model coverage, credits and frequency should be priced together. Scrunch is worth including when your measurement question also concerns how agents access and receive website content.
A practical evaluation you can repeat
- 1.Write ten fixed questions covering your category, specific use cases and competitor comparisons. Keep directly branded questions in a separate group.
- 2.Choose the country, language and answering surface you care about. Record anything the tool does not let you configure.
- 3.Run the same list at more than one time. Keep individual answers instead of only the average score.
- 4.Manually label whether each answer mentions the brand, recommends it, links to its website or cites another source about it.
- 5.Inspect disagreements with the dashboard. Check aliases, ambiguous names, negative mentions and failed runs.
- 6.Export the evidence and repeat after one clear site change. Treat the result as an observation; other sources and engine behavior may also have changed.
There is no universal number of repeats that makes a measurement reliable. Three answers can reveal variation, but they do not by themselves establish statistical confidence. Ask how the vendor handles uncertainty and what decision the sample is sufficient to support.
Read the denominator before the score
Suppose a brand is recommended in four of ten successful answers. That is 40% of that answer set. It is not 40% of all AI users, searches or category demand. If two scheduled runs failed, report the missing observations separately rather than counting them as negative answers. This is an illustrative calculation, not a measured result from these tools.
Compare branded questions separately from discovery questions. “What does Acme sell?” tests knowledge about Acme; “Which inventory tools suit a bicycle shop?” tests discovery. Mixing them can make a brand look more discoverable without changing how often it appears on a buyer’s shortlist.
Where August fits
August preserves answers and citations so customers can inspect the basis for a finding, prepare a reviewed draft and compare a later measurement. The initial public report uses one sample per prompt and should be read as an initial snapshot. Our collection paths are not a promise to reproduce every signed-in consumer experience.
Get started with August or review the plans. For day-to-day operation after choosing a collector, see AI search monitoring tools.
Questions, answered.
Does an API answer show exactly what a customer sees?
No. Collection surface, search settings, location and account context can differ. Ask the tool to identify the observation it actually records.
Is more sampling always better?
More relevant observations can reveal variation, but question quality, collection method and failure handling still matter. There is no universal repeat count that proves accuracy.
Can we compare scores from two tools directly?
Only after checking the question set, denominator, surface, conditions and scoring definitions. Otherwise the same percentage may describe different things.
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