Comparison · 10 tools · 12 criteria

Best AI Visibility Tools (2026): 10 Compared

An AI visibility tool tracks whether, how often and how favourably a brand appears in the answers that ChatGPT, Perplexity, Google AI Overviews, Gemini and Copilot generate for a fixed set of prompts, and which sources those answers cite. At minimum it must run prompts on a schedule across several engines, record mentions and citations per engine, and show how those numbers move against named competitors. The better ones also explain why: which of your pages and which third-party sources earn the citations, and what to change.

This page compares ten tools across twelve criteria, reviews each, and says which to shortlist. It is written by Asva AI, which appears in the comparison; the methodology section explains how we handled that.

Last updated: September 2026

How we evaluated these tools

Disclosure first. Asva AI publishes this page and is one of the ten tools in it. We are not a neutral party, and pretending otherwise would be worse than saying so. What we can do is hold every tool, ours included, to the same criteria, fill cells only from what a vendor documents publicly, and say “not publicly documented” rather than guess. Our own limits are marked as plainly as our strengths.

What this is not. We have not run a paid head-to-head benchmark; nothing here is a test result. Pricing is omitted for any tool that does not publish it, and star ratings are gone for the same reason: a number you cannot verify is worse than no number.

The criteria. We wrote the twelve criteria before filling the table, starting from the definition of AI visibility itself, then adding what a buyer needs once monitoring works: attribution, commerce readiness and the features agencies ask for.

Engines covered. Which answer engines prompts run on; per-engine reporting matters more than the count.
Mention tracking. Whether the brand appears in the answer, on a fixed prompt set, on a schedule.
Source / citation analysis. Which URLs and domains each answer cites.
Sentiment. How the brand is described: recommended, neutral, or hedged.
Share of voice. Mention rate against named competitors, per engine, over time.
Prompt discovery. Finding the prompts buyers actually ask, beyond your day-one list.
Reddit / community coverage. Tracking the community sources engines cite for opinion and comparison answers.
AI-traffic attribution. Connecting visibility to sessions, orders and revenue, including AI referrals GA4 files as direct.
Agentic-commerce readiness. Helping AI agents transact against your catalog (MCP, ACP, UCP, agents.json).
Free tools. No-login utilities or a usable free tier to evaluate before a sales call.
API / MCP. Programmatic access, or an MCP server your own agents and BI tools can query.
White-label. Client-branded reporting and multi-brand workspaces for agencies.

AI visibility tools comparison table

Ten tools, twelve criteria; scroll sideways on smaller screens. Cells reflect public documentation as of September 2026. Verify anything that will decide a purchase.

Tool	Engines covered	Mention tracking	Source / citation analysis	Sentiment	Share of voice	Prompt discovery	Reddit / community coverage	AI-traffic attribution	Agentic-commerce readiness	Free tools	API / MCP	White-label
Asva AI	8: ChatGPT, Perplexity, Gemini/AI Mode, Copilot, Grok, Meta AI, Rufus	Yes	Yes	Yes	Yes	Yes	Yes	Yes (DACT resolver)	Yes (MCP, ACP, UCP, agents.json)	Yes	Yes (MCP server)	Yes (agency plans)
Profound	Major engines	Yes	Yes	Yes	Yes	Yes	Not publicly documented	Partial (crawler and agent traffic reporting)	—	Not publicly documented	Yes	Not publicly documented
Scrunch AI	Major engines	Yes	Yes	Yes	Yes	Yes	Not publicly documented	Not publicly documented	—	Not publicly documented	Not publicly documented	Not publicly documented
Otterly.ai	ChatGPT, Perplexity, Google AI Overviews, Copilot, Gemini	Yes	Yes	Yes	Yes	Partial	Not publicly documented	—	—	Partial (free trial)	Not publicly documented	Not publicly documented
Peec AI	Major engines	Yes	Yes	Yes	Yes	Partial	Not publicly documented	Not publicly documented	—	Not publicly documented	Not publicly documented	Varies by plan
Promptwatch	Major engines	Yes	Yes	Partial	Yes	Yes	Not publicly documented	Not publicly documented	—	Partial (trial)	Not publicly documented	Not publicly documented
Evertune	Major engines and models	Yes	Partial	Yes	Yes	Yes	Not publicly documented	—	—	Not publicly documented	Not publicly documented	Not publicly documented
Goodie AI	Major engines	Yes	Yes	Yes	Yes	Yes	Not publicly documented	Not publicly documented	—	Not publicly documented	Not publicly documented	Not publicly documented
Waikay	Major engines	Yes	Partial	Partial	Yes	Partial	Not publicly documented	—	—	Partial (free report)	Not publicly documented	Not publicly documented
LLM Pulse	Major engines	Yes	Yes	Yes	Yes	Partial	Not publicly documented	Not publicly documented	—	Not publicly documented	Not publicly documented	Not publicly documented

Legend: Yes = publicly documented. Partial = a narrower form is documented. — = nothing found. Varies by plan = documented but tier-gated.

The ten tools, reviewed

Same shape for every review: what it is, strengths, limits, best for. Longer head-to-heads are linked where they exist.

1. Asva AI

AI visibility platform that tracks mentions, citations, sentiment and share of voice across eight AI channels, then continues into AI-traffic attribution and agentic commerce.

Strengths. A fixed prompt set runs separately on each engine and is reported per engine, never averaged. Citation intelligence includes the Reddit threads engines cite for comparison questions. The DACT resolver re-attributes the AI-driven sessions GA4 files under direct, with a confidence score per session, which makes the revenue number defensible with finance. An MCP server, ACP checkout and agents.json make the catalog buyable inside agents; free validators cover llms.txt, robots.txt and JSON-LD.

Limits. If you only need a light mention counter on one engine, the attribution and commerce modules are more than you will use. And as the publisher of this page we are not a neutral reviewer of ourselves: verify the claims above on a free scan.

Best for. Ecommerce, SaaS and agency teams that report revenue and pipeline, not only mentions. See the brand visibility tracker →

2. Profound

Enterprise-oriented AI visibility platform reporting mentions, citations and share of voice across the major answer engines, with an emphasis on prompt-volume data.

Strengths. Large prompt sets paired with estimates of how often prompts are asked, so topics can be prioritised by demand rather than gut feel. Thorough source and citation reporting, publicly described crawler and agent analytics, and a documented API.

Limits. Pricing is not published and the motion is sales-led, which puts it out of reach below enterprise scale. Crawler analytics are not session-level revenue attribution; Reddit coverage, white-label and free tools are not documented.

Best for. Enterprise SEO and digital teams that run a formal vendor process and want prompt-demand data. Read the full Profound vs Asva AI comparison →

3. Scrunch AI

AI search visibility platform that tracks how brands appear across AI answer engines and helps content teams optimise pages for them.

Strengths. Positioned around the monitoring-to-optimisation loop: which prompts surface you, how you are described, and what to change on-site. For a team that owns the content calendar that is the right shape, because the recommendation lands with the people who can act on it.

Limits. Stops at the dashboard and the content brief. Attribution to sessions or revenue is not documented, nor is anything on agentic commerce, API access, white-label or Reddit coverage.

Best for. Content-led teams that want a tight monitor-then-fix loop and no attribution or commerce requirement. Read the full Scrunch AI vs Asva AI comparison →

4. Otterly.ai

Self-serve AI search monitoring tool that tracks brand mentions and links across ChatGPT, Perplexity and Google AI results.

Strengths. One of the easier entries in the category: pick prompts, pick engines, and the first report arrives quickly. It tracks mentions and the links engines return, and reports sentiment, which for a small team is most of what they will act on. A trial lets you evaluate on your own prompts.

Limits. Source analysis is shallower than the enterprise tools, prompt discovery is limited, and attribution, agentic commerce, API and white-label are undocumented.

Best for. SMB brands and freelancers who need a clear, affordable mention and link monitor. Read the full Otterly.ai vs Asva AI comparison →

5. Peec AI

AI visibility analytics tool that tracks brand mentions and rankings across AI answer engines, with competitor benchmarking as a core view.

Strengths. Analytics-first. Share-of-voice and position views read at a glance, source analysis shows which domains carry each answer, and competitor benchmarking sits in the default view. Multi-client arrangements for agencies are described as varying by plan.

Limits. Prompt discovery is narrower than in tools that model prompt demand. Attribution, agentic commerce, a public API and free tools are not documented at the time of writing.

Best for. In-house marketing teams and agencies that need reporting-grade share-of-voice and source data. Read the full Peec AI vs Asva AI comparison →

6. Promptwatch

AI answer-monitoring tool that tracks how brands and prompts surface across generative AI engines, organised around the prompt as the unit of work.

Strengths. The prompt-centric layout goes from topic to exact question to exact answer and its sources in a few clicks. Prompt discovery is a strength, mention and citation tracking cover the core engines, and share of voice is available per prompt group. Approachable for a team new to this kind of monitoring.

Limits. Sentiment reporting is lighter than in tools that treat brand description as a first-class metric. Attribution, agentic commerce, API access and white-label are not publicly documented.

Best for. SEO and content teams who want prompt-level visibility without an enterprise contract. Read the full Promptwatch vs Asva AI comparison →

7. Evertune

AI brand-visibility analytics platform that benchmarks how often AI models recommend a brand across categories, by sampling responses at scale.

Strengths. Approaches the problem the way a research firm would: sample many responses across many phrasings, and report recommendation rates and perception with statistical footing. That suits brand teams who need a defensible index for a board meeting, and categories where the answer is a recommendation rather than a citation.

Limits. Less oriented toward page-level citation fixes, so an SEO team will find it further from the work. Sales-led, pricing unpublished; attribution, API and white-label undocumented.

Best for. Consumer brand and insights teams who want measurement rigour more than a task list. Read the full Evertune vs Asva AI comparison →

8. Goodie AI

AI visibility and generative engine optimisation platform that tracks how brands appear in AI answers and helps teams produce content engines are more likely to surface.

Strengths. Combines the monitoring layer with content generation aimed at the gaps monitoring finds, so recommendations are not carried from one tool into another. Mention, citation and share-of-voice reporting cover the core engines, and prompt discovery is part of the workflow.

Limits. Generated content still needs an editor who knows the category, and the platform is only as valuable as the share of that content you publish. Attribution, agentic commerce, API and white-label are undocumented.

Best for. Marketing teams with a content engine that want GEO recommendations and drafts in one place. Read the full Goodie AI vs Asva AI comparison →

9. Waikay

AI brand-visibility reporting tool that shows how AI models describe, rank and recommend a brand in response to category questions.

Strengths. Report-oriented. The output reads like a briefing: how you are described, where you rank against named alternatives, and which claims models make about you. A free report lowers the barrier to a first look, and the framing suits people who do not live in SEO dashboards.

Limits. Not positioned as a continuous, large-prompt-set monitoring platform, so a team that needs weekly per-engine trend lines on hundreds of prompts will outgrow it. Source analysis and sentiment are lighter; attribution, API and white-label are undocumented.

Best for. Early-stage brands who want a first diagnosis rather than an always-on monitor. Read the full Waikay vs Asva AI comparison →

10. LLM Pulse

AI visibility monitoring tool that tracks brand mentions, citations and sentiment across the major answer engines.

Strengths. Covers the core of the category: mentions, citations, sentiment and share of voice across the main engines, reported over time. A reasonable default for a team that wants the numbers and does not yet need attribution or commerce integration.

Limits. Public documentation is thinner than for the larger platforms, so several table cells are honestly unknown rather than absent. Confirm engine list, refresh frequency, API and agency features with the vendor.

Best for. Teams that want a straightforward monitor and will confirm details in a call.

How to choose an AI visibility tool for your situation

Most teams need only the three or four criteria that map to the question their boss will ask. Start from the situation, not the feature list; the solutions overview lists use cases by role, industry and company size.

You run SEO and are being asked about AI search

Weight per-engine citation analysis and prompt discovery above everything else: your lever is the page and the question is which pages engines cite. The SEO and AEO teams page covers the workflow and the metrics that replace rank position.

You own brand or PR and care how you are described

Sentiment, share of voice against named competitors and community coverage matter most: a hedged description in a ChatGPT answer is a reputation issue before it is a traffic issue. The PR and brand teams page describes the cadence and alerting that fit.

You sell software and need pipeline attribution

Attribution is what separates a dashboard from a business case. If your CRM cannot see that a demo request started in Perplexity, the AI visibility budget is the first thing cut. See the SaaS page.

You sell products and AI agents are starting to shop for your customers

Add Amazon Rufus and ChatGPT shopping to the engine list, and weight agentic-commerce readiness: whether your catalog becomes buyable inside an agent, not only visible. The ecommerce and retail page covers product-level visibility and checkout readiness.

You are an agency reporting to clients

White-label reporting and multi-brand workspaces are table stakes; the criterion that improves retention is attribution, because a revenue number survives a budget review and a share-of-voice chart does not. See the agencies page.

What AI visibility tools cannot do

Every tool here, ours included, shares these limits. A vendor who does not mention them is selling harder than they should.

They sample; they do not observe. No tool sees the answers real users receive. They run their own prompts from their own accounts, so the numbers estimate visibility rather than log it. Personalisation, memory and location change what a buyer sees.
Answers are non-deterministic. The same prompt returns different answers on repeated runs. Good tools sample several times and average; even so, a small week-on-week movement is usually noise. Treat a trend as real only when it persists across runs and more than one engine.
Most stop at the dashboard. Share of voice does not tell you what it produced. Attribution needs a connection to your analytics and a method for the sessions GA4 files as direct. Few tools attempt it, and any that do should show a confidence score, not a single confident number.
They cannot change what a model already believes. Mentions drawn from training data persist regardless of what you publish this month. Retrieval-based engines respond within days; model-memory mentions change on the model’s schedule, not yours.
They do not fix the technical blockers. If robots.txt blocks OAI-SearchBot or PerplexityBot, or product pages carry no structured data, no amount of monitoring helps. Those fixes are yours, which is why the free validators below come before any paid tool.
Engine coverage lags reality. New surfaces arrive faster than tools add them. Ask each vendor how quickly they added the last new engine and what the refresh frequency is on each.

The practice these tools serve is answer engine optimization: making pages and sources engines can retrieve, trust and cite. Monitoring shows whether it is working; it is not a substitute for doing it.

Free tools first

Before paying for continuous monitoring, remove the reasons an engine cannot cite you at all. These are free, need no login, and take minutes.

robots.txt validator — checks whether the retrieval agents (OAI-SearchBot, ChatGPT-User, PerplexityBot, ClaudeBot) are allowed, separately from the training crawlers. Blocking the wrong one is the commonest self-inflicted visibility problem.
llms.txt validator — confirms the file parses and points at the pages you want engines to prioritise.
JSON-LD validator — validates the structured data on any URL, which is how engines confirm what a page is about before citing it.
Agentic readiness check — for ecommerce, scores whether an AI agent could discover, understand and transact against your catalog.

Once those pass, monitoring has something to measure. AI search monitoring and citation intelligence describe how Asva runs that layer; pricing lists the plans.

Frequently asked questions

What is an AI visibility tool?+

An AI visibility tool runs a fixed set of prompts through answer engines such as ChatGPT, Perplexity, Google AI Overviews, Gemini and Copilot on a schedule, and records whether your brand is mentioned, which sources are cited, how you are described and how that changes over time. It replaces rank position with mention rate, citation share and sentiment, per engine.

What is the best AI visibility tool?+

There is no single best tool; it depends on whether you need monitoring only, monitoring plus content recommendations, or monitoring plus attribution and commerce. Asva AI publishes this page and is built for the third case. For content-loop work Scrunch or Goodie fit; for a first snapshot Waikay or Otterly are quick; for enterprise procurement Profound and Evertune are the usual shortlist.

How is an AI visibility tool different from an SEO rank tracker?+

A rank tracker records a position for a keyword on a results page. An AI visibility tool records whether you are named inside a generated answer, which sources the engine cited, and how you were described. There is no stable position to track, answers vary between runs, and citations often come from Reddit, review sites and publishers rather than your own domain.

Which AI engines should an AI visibility tool cover?+

ChatGPT, Perplexity, Google AI Overviews and AI Mode, Gemini and Microsoft Copilot are the core set for most categories; ecommerce brands should add Amazon Rufus and ChatGPT shopping. More important than the count is that each engine is reported separately, because the same prompt produces different mentions and citations on each.

How much do AI visibility tools cost?+

Most vendors in this category do not publish pricing, and the ones that do change it often, so this page does not list prices. Broadly there are self-serve tools with monthly plans and trials, and enterprise platforms sold on annual contracts. Asva AI publishes its plans on the pricing page; for others, ask for a written quote before shortlisting.

Can free tools replace a paid AI visibility platform?+

Free validators for llms.txt, robots.txt and JSON-LD fix the technical reasons an engine cannot cite you, and a free scan gives a one-off snapshot. They cannot run hundreds of prompts weekly, track share of voice against competitors, or attribute AI traffic to revenue. Fix the basics with them first, then decide whether continuous monitoring is worth paying for.

How do these tools measure share of voice in AI answers?+

Share of voice is the proportion of answers on a prompt set that mention your brand, against the proportion mentioning each named competitor, reported per engine and over time. Because engines produce different answers on repeated runs, tools sample each prompt several times and average, so a small week-to-week change is often noise rather than a trend.

Do AI visibility tools track Reddit and other community sources?+

Some do, and it matters more than it looks: for comparison and opinion questions, engines cite Reddit threads, review sites and forums far more than brand pages. Asva AI tracks the subreddits and community sources engines cite as part of citation intelligence. For the other tools here, Reddit coverage is not publicly documented, so ask each vendor to show it on your prompts.

Can an AI visibility tool attribute AI traffic to revenue?+

Only if it connects to your analytics and handles the referrals GA4 files as direct. Most tools stop at the dashboard. Asva AI resolves those sessions with its DACT resolver and reports AI-attributed orders and revenue with a confidence score. Profound documents crawler and agent traffic reporting, which is related but not session-level attribution. The rest are undocumented here.

How often should prompts be re-run?+

Weekly is the practical default: frequent enough to catch a competitor gaining ground or a citation being lost, slow enough that run-to-run noise does not dominate. Daily runs make sense during a launch, a PR event or a reputation issue. Whatever the cadence, keep the prompt set fixed for at least a quarter so the trend line means something.

See your own numbers before you shortlist

Run your buyer prompts through eight AI channels and see who is mentioned, who is cited and what it produced. No sales call required to look.

Get a free visibility scan See pricing