Best AI Visibility Tools in 2026, Compared

The four criteria that actually separate these tools

Almost every vendor in this space will show you a share-of-voice chart and a sentiment score. Those are table stakes and they are easy to fake, because a percentage with no artefact behind it is just a number in a database. Use these four criteria to cut through the demos.

1. Assistant coverage, and how the answers are obtained. ChatGPT, Google's AI experiences, Perplexity, Claude, Copilot and Grok behave differently: they retrieve from different indexes, weight different sources, and disagree with each other constantly. A tool that covers two assistants gives you a partial view of the same question. Ask specifically which surfaces are queried, whether Google AI Mode and AI Overviews are treated separately, and whether results are geolocated — because the answer to “best CRM for law firms” is not the same in Berlin and Boston.

2. Evidence trail. This is the criterion buyers under-weight and regret. When a tool tells you a competitor was recommended and you were not, can you open the actual answer, read it, see the cited URLs, and export it? Without that, you cannot diagnose why you lost, you cannot verify the tool is not hallucinating its own report, and you cannot put the finding in front of a sceptical executive.

3. Prompt-level tracking you control. Auto-generated prompt sets are useful for coverage, but the prompts that matter to your revenue are the ones your buyers actually type — with their industry, their constraints, their region and their comparison shortlist. You need to add, edit, group and version prompts, and you need mention rate reported per prompt, not just as one blended average that hides your worst-performing category.

4. Price and the shape of it. Compare on total cost for the prompt volume and seat count you need, not on the headline tier. The usual variables are tracked prompts, refresh frequency, number of assistants, competitor slots, workspaces and API access. Vendors change plans often, so check the vendor's own pricing page on the day you buy.

Two secondary criteria decide the winner once those four are level: whether the tool converts findings into prioritised work, and whether it separates what you can change (your pages, your presence on third-party sources assistants cite) from what you cannot (the model's training data).

• Assistants queried, and whether Google AI Mode, AI Overviews and Perplexity are distinct surfaces
• Stored raw answers, citations and timestamps you can export
• Editable, grouped, per-prompt reporting with country-level control
• Cost at your real prompt and seat volume, plus what triggers an upgrade
• Output that names the next action, not just the metric

Growli — evidence-first tracking with a prioritised fix list

Disclosure: Growli is our product. Treat this section as a vendor description, and verify it in a trial.

We built Growli around the two things clients kept asking for after their first AI visibility report: proof and instructions. Growli runs your prompt set across ChatGPT, Gemini, Claude, Perplexity, Copilot and Grok, records each response with its citations and timestamp, and reports mention rate, recommendation rate, sentiment and competitor share per prompt rather than only in aggregate. Every data point opens to the underlying answer, so a claim like “Grok recommends three competitors before you on comparison prompts” can be read, checked and pasted into a deck.

The second half is the gap-to-action layer. Growli groups losses by cause — you are absent from the sources assistants cite for that question, a competitor owns the comparison page they retrieve, your own page does not answer the question in extractable form, or the assistant is working from stale information about you — and orders the fixes by how many tracked prompts each one touches. That ordering is the point: the raw list of things you could do is always longer than the quarter.

Prompts are yours to define. You import them, cluster them by funnel stage or product line, and set market and language per group. Competitor tracking is explicit — you name the rivals you want scored beside you, rather than accepting an inferred set.

Who should not buy it: if what you need is server-side analysis of AI crawler hits and assistant-referred sessions at enterprise scale, a platform built around log and traffic pipelines will serve you better. Current plans and prompt allowances are on our pricing page, and the conceptual background is in our primer on AI visibility.

Profound — the enterprise analytics end of the market

Profound is positioned for large brands and enterprise teams, and its centre of gravity is data breadth: answer monitoring across assistants combined with analysis of how AI crawlers and agents interact with your site. If your question is not only “what do assistants say about us” but “what is happening to our owned properties as assistants consume them, and what does that traffic do afterwards”, that combination is the differentiator.

It suits organisations with the internal capacity to act on wide datasets — a team that can take a large surface-area report and route the work across content, PR, web and analytics owners. Enterprise-oriented platforms typically involve sales-led onboarding and contract pricing rather than self-serve checkout, so build procurement time into your plan and confirm current packaging directly with the vendor.

Where it may be more than you need: a five-person marketing team that wants a weekly answer to “are we being recommended, and what do we fix first” will get to that answer faster with a lighter tool.

Otterly.AI — lightweight monitoring for smaller teams

Otterly.AI was one of the earlier entrants aimed squarely at SEOs, consultants and small marketing teams, and it kept that shape: define the prompts that matter, watch brand mentions and links in AI search results, get scheduled reporting without a long implementation.

The strength is time-to-first-insight. You can be tracking a real prompt set the same day, at a price point that suits a single brand or a handful of client accounts. For a consultant who needs to show a client whether they appear in assistant answers at all, that is often the entire job.

The trade-off is scope. A tool built to be simple is intentionally not an enterprise data platform, so confirm against your own checklist which assistants and which locales are included on the plan you are considering, and how far the historical data goes back.

Peec AI — competitive share of voice with a clean interface

Peec AI emerged from the European market and built a reputation for interface quality and speed of setup, with competitive comparison as the organising idea: your brand alongside a named competitor set, tracked across assistant answers, with visibility and citation reporting over time.

That framing fits teams whose internal conversation is comparative — “are we ahead of these four rivals in the questions our buyers ask?” — and marketing leads who need a chart that survives contact with a board meeting. Agencies also tend to like it for the reporting surface.

As with any tool where competitor benchmarking is the headline, check how competitors are added and counted on your plan, and whether the prompt set you can maintain is large enough to cover your product lines separately rather than blending them into one score.

Scrunch AI — brand presence and correctness for larger organisations

Scrunch AI targets enterprise brands and frames the problem as customer experience inside AI interfaces: not only how often you are mentioned, but whether what assistants say about you is accurate, and how your properties present themselves to AI agents that arrive to read them.

That emphasis matters most for regulated, multi-market or multi-brand organisations where a wrong claim about pricing, eligibility, coverage or product availability is a compliance issue rather than a marketing one. Teams in financial services, healthcare and large B2B service businesses tend to feel that pain first.

Expect an enterprise engagement model. If you are a single-brand company with a straightforward product and a small team, the correctness machinery will likely exceed the problem you have today.

How to run a fair two-week trial

Assistant answers vary between runs, so two tools pointed at different prompt sets will always disagree, and you will learn nothing about either. Fix the variables instead.

Write 20 prompts before you talk to any vendor: five discovery (“how do I solve X”), five category (“best tools for X”), five comparison (“A vs B for X”), five objection (“is A worth it for a small team”). Load the identical 20 into every tool you are evaluating, set the same country, and run for two weeks.

Then score the vendors on the same five questions, and buy the one that answers all five in writing.

• Do the reported mention rates for these prompts agree with what I see when I ask the assistant myself?
• Can I open the raw answer and the cited URLs behind any number in the dashboard?
• Does per-prompt data show me which category I am losing, or only a blended average?
• Does the tool name a next action, and can I tell who on my team owns it?
• What does this cost at twice my current prompt count, and what forces an upgrade?

Which tool for which team

There is no single best AI visibility tool, only the right fit for how your team works and what it can act on. From the criteria above, the honest mapping looks like this.

In-house marketing team of two to ten, one or two brands: you need prompt control, an evidence trail you can quote internally, and a short ordered fix list. Growli is built for that job; Otterly.AI and Peec AI are strong choices if your primary need is monitoring and reporting rather than remediation planning.

Enterprise brand with analytics and web engineering capacity: Profound for breadth across answers, crawler behaviour and downstream traffic; Scrunch AI if factual correctness and multi-market brand presentation are the board-level concern.

Agency or consultancy managing many clients: prioritise per-client workspaces, white-labelled or exportable reporting, and per-account prompt limits that do not force you into an enterprise tier at your fifth client. Otterly.AI and Peec AI are common picks at the lighter end; Growli fits agencies that bill for implementation, because the output is a scoped work list rather than a chart.

Whatever you choose, own the prompts. Tooling changes, vendors get acquired, plans get repackaged. A maintained, versioned prompt set that reflects how your buyers actually ask questions is the asset that survives all of it — and it is what makes any of these platforms worth its licence fee.

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