The 14 GEO Tools We Compared Against Viclaro, Ranked by Whether They Publish Their Methodology

Why we picked this axis

The generative engine optimization tool market grew fast. In under two years it went from a handful of prompt trackers to a crowded category of dashboards, agents, and enterprise platforms. That growth was healthy for the field. It also produced a market where almost every product does the same thing.

Every credible GEO tool runs prompts against a set of AI assistants on a schedule, counts the mentions, aggregates the mentions into a visibility score, and presents the score on a dashboard. The pricing pages differ. The engine counts differ, slightly. The color schemes differ, a lot. The underlying operation is identical.

Which means the interesting question for a buyer in 2026 is no longer what does the dashboard look like. It is: do you trust the numbers on it. And to trust the numbers you need to know how they were made.

This piece ranks 14 GEO tools, plus Viclaro, on a single test. Can a stranger, without an account, without a demo, without an NDA, open a browser and inspect the prompts, the response corpus, the citations, the version history, and the leaderboards themselves. If any of those is not publicly readable, the tool is asking you to take its word for it.

You can take a tool's word for it. Ahrefs and Semrush built billion-dollar businesses on exactly that arrangement. But the point of a measurement is that it should be independently verifiable, and the entire GEO category, with one exception, currently operates on trust.

What "verifiable methodology" actually means

A GEO tool passes the test only when all five of the following are inspectable from a public URL, without any authentication. Each criterion is chosen because it corresponds to a specific way the numbers on the dashboard can be misleading if it is missing.

1. The prompts. The exact wording of every question the tool sends to each AI assistant. Not a category description like "high-intent buyer prompts for divorce lawyers." The actual strings. Why it matters: two GEO tools tracking the same brand can produce wildly different visibility scores because they are asking different questions. Without the prompt list, a buyer cannot tell whether the score reflects their category or a warped slice of it.

2. The response count. How many responses the tool has collected, per model, per vertical, expressed as a countable integer. Not "hundreds" or "millions." A number. Why it matters: share-of-answer percentages behave badly on small samples. A "12% AI visibility score" computed from 40 responses is noise; the same number from 4,000 responses is a signal. Without the count, there is no way to tell which one you bought.

3. The raw citations. Which brands each model named in which response, viewable at the individual-response level, not aggregated behind an averaged score. Why it matters: aggregate scores hide the shape of the data. A brand cited only by Perplexity, only in the "who should I use" prompt bucket, gets the same score as a brand cited evenly across four models and every prompt bucket. Those are wildly different market positions. Raw citations show which one you actually have.

4. The version history. When the rankings changed, by how much, and against what previous state. Snapshots that persist rather than get quietly overwritten. Why it matters: without a version history, a vendor can rotate its prompt library, swap its model panel, or re-weight its scoring formula without you noticing. The number moves and the source of the movement is unknowable. Every serious measurement discipline versions its methodology. GEO tools mostly do not.

5. The leaderboards. The output of the whole system, viewable without a login, so that a buyer, a journalist, or a downstream AI assistant can inspect the tool's claims about who ranks where. Why it matters: if the output is behind an account wall, nobody outside the customer base can verify the tool's numbers, cite them, correct them, or challenge them. The measurement never enters the public record. It stays a private assertion made repeatedly, month after month, to a paying audience.

Passing means all five. Partial means some. Zero means the tool is a dashboard shaped like a measurement.

Methodology publication scorecard

The following scorecard rates each of the 15 tools compared on the five criteria above. A ✓ means the criterion is publicly inspectable, at the level of raw data, without an account. A dash means it is not.

The finding is unambiguous. Of the 14 non-Viclaro tools, zero pass the strict methodology-publication test on any single criterion, let alone all five. Two publish adjacent documentation (audit checkpoints for Rankscale AI; free auxiliary utilities for LLMrefs) that a fair reader would want to acknowledge. Neither counts as publication of the underlying prompts, responses, citations, version history, or leaderboards, so both remain at 0 of 5 on the strict scorecard.

Viclaro is the only tool on the list that publishes all five. That is not a moral victory. It is a strategic bet — a bet we might turn out to have gotten wrong — and the ranking below explains why we made it.

The ranking, tool by tool

1. Viclaro. Free scan; audit engagements priced per project. 5 models tracked, 9 verticals, 10,046 versioned responses at time of writing. Passes on all five criteria. Atlas is public: every prompt is visible, every response is traceable, every ranking is timestamped, every citation is linkable to the raw model output. You can open the Wealth Management NYC leaderboard right now, from your phone, without logging in, and see every firm named by every model on every prompt. If the methodology changed tomorrow, the diff would be discoverable within a day. This is the operating condition of the product rather than a marketing claim, which is why it appears at position 1 on a scorecard that measures exactly this.

2. AIclicks. $59/mo Starter, $189 Pro, $499 Business. 10+ models tracked. Real product with a genuine execution layer, a shockingly good self-serve pricing page (no sales call required), and one of the more honest positioning stories in the category. AIclicks earns its high placement in most listicles, including the one that prompted this response. Methodology publication: none. What ChatGPT actually said, and when, and to whom, and how any given weight was chosen, lives behind a login and inside a support ticket. The number on the dashboard is the terminus, not the receipt.

3. Scrunch AI. $250/mo Starter (annual), $417 Growth. 7 models tracked. Approaches GEO from a genuinely different angle. Its Agent Experience Platform (AXP) generates a machine-readable parallel version of a site and serves it directly to AI crawlers, so the models parse the content cleanly regardless of what the human-facing design looks like. The crawl observability layer is strong. Methodology publication: none. If AXP moves your visibility, the only proof is that Scrunch's dashboard says it moved.

4. Otterly AI. $29/mo Lite, $189 Standard, $489 Premium. 4 models on every plan, Gemini and AI Mode billed as add-ons. The cleanest entry point in the category and one of the more grown-up products in it. Fast setup, unlimited seats on every tier (which agencies notice immediately), mature integrations. Methodology publication: none. The add-on pricing structure also means the real methodology of any given Otterly score depends on which tier the customer bought, which is a form of opacity that is harder to name but no less real.

5. Peec AI. ~$95/mo Starter, ~$245 Pro, ~$495 Advanced. Choose 3 of 7 models per plan. The most polished dashboard on the market. Reporting practically builds itself. The three-metric focus (visibility, position, sentiment) is a defensible product decision. Methodology publication: none. Peec has correctly identified that most buyers want a chart clean enough to show a VP, not an audit trail thorough enough to survive a diligence review. That is a positioning choice, not a criticism, but it is a positioning choice worth naming plainly.

6. Profound AI. Custom enterprise pricing; Brands tier reportedly starts around $99/mo. 9+ models tracked. The deepest enterprise product in the category. Prompt Volumes is a legitimately novel dataset — the closest thing GEO has to search-volume data — and Answer Engine Insights covers a broader model panel than most. Methodology publication: none, and as of 2026 access to the dataset itself requires a sales call, an NDA, and enterprise procurement intent. The measurement is real. The verifiability is not available to the market. Profound has effectively become the Bloomberg Terminal of GEO, in every sense of that phrase.

7. Writesonic. Starter from $79/mo (annual), Growth $399, Enterprise custom. 7+ models tracked. A content-side answer to GEO measurement. Writesonic began as an AI writing tool and rebuilt around AI visibility, which means the tracking layer sits on top of a content production engine. Track a visibility gap in the morning, draft the fix in the afternoon, publish it before end of day, all in one product. Methodology publication: none. The tracking is a feature of a suite rather than the point of the product, which is a defensible product choice, but the number is still not checkable.

8. Rankscale AI. $99/mo Pro, $385 Growth, $780 Enterprise. 17+ models tracked, no per-engine upsells. The widest engine coverage on this list. Rankscale also runs the strongest technical audit layer here — 94+ checkpoints scoring the structural and authority signals AI engines use to decide whether to cite a page — and publishes a partial description of those checkpoints. That partial documentation is more than most competitors offer and is worth naming. It does not, however, extend to the underlying prompt corpus or response set, so the strict scorecard still reads 0 of 5. The audit-checkpoint documentation is a real signal that Rankscale takes methodology seriously; it just has not yet been extended to the parts of the pipeline that produce the visibility number itself.

9. Prompt Monitor. $29/mo Starter, $39 Growth, $129 Pro; free agency plan. 8 models tracked. "Ahrefs but for AI answers" is a strong positioning line and the value-for-money holds. The best feature is source-contact extraction: for every third-party source that cites a competitor and not you, Prompt Monitor pulls the author's email and social profiles, so the gap between "this article cites the wrong firm" and "I have pitched the author" is one click. Methodology publication: none. The value is in the outreach workflow rather than the reproducibility of the visibility number, which is a coherent product choice that leaves the underlying data uncheckable.

10. Semrush AI Visibility Toolkit. $99/mo per domain; also available inside Semrush One bundles. 5+ models tracked. Semrush's play here is workflow proximity: AI visibility landing next to the SEO stack an existing customer already uses. The pitch works. If a team already lives inside Semrush, adding $99 for AI tracking is easier than onboarding a dedicated vendor. Methodology publication: none. Semrush numbers have always been consumed on brand equity rather than reproducibility, and the AI Visibility Toolkit inherits that posture cleanly.

11. Ahrefs Brand Radar. $199/mo per AI index or $699 for the all-index bundle, on top of an Ahrefs base plan from $129/mo. 7 models tracked. The largest AI visibility index in the category by claimed volume: 384M+ monthly search-backed prompts, per the marketing page. As a market intelligence layer it is genuinely impressive — you can research any domain's AI visibility on demand, no setup, unlimited projects. Methodology publication: none. Ahrefs' entire model has always rested on trusting the crawl rather than inspecting it, which has worked for over a decade in traditional SEO, and Brand Radar inherits that pattern without modification. As a primary GEO tool, however, you are paying enterprise money for measurement with nothing attached to act on and no way to verify.

12. AthenaHQ. $295/mo Self-Serve ($95 intro first month); Enterprise custom. 8+ models tracked. The most workflow-obsessed tool in the category. AthenaHQ's Action Center turns visibility gaps into structured, assignable, trackable tasks — closer to a project management layer for GEO than another dashboard, and a product decision worth respecting. Revenue attribution via GA4 and Shopify is unusually complete. Methodology publication: none. The credit-based billing model also means that the more curious you are about your own data, the more you pay for the privilege, which is a subtle form of methodological gating.

13. LLMrefs. Free plan (1 keyword); Pro $79/mo (50 keywords); Enterprise custom. 11+ models tracked. A clever bridge for SEO teams migrating into GEO: enter a keyword, LLMrefs auto-generates conversational prompt variations and tracks rankings, citations, and sources across the AI panel. Several of the free utilities (the crawlability checker, the llms.txt generator, the Reddit thread finder) are genuinely inspectable and useful without an account, which is more open than most competitors bother to be. Methodology publication: partial on adjacent utilities only; none on the core tracking. The auto-generated prompts trade user control for convenience, which also means you cannot see exactly what LLMrefs is asking on your behalf.

14. Goodie AI. Custom pricing. 12 models tracked, including Amazon Rufus and Walmart Sparky. The only serious option on this list for tracking AI shopping surfaces. If you sell physical products through channels where product-level visibility on Rufus and Sparky matters, Goodie AI is a genuinely differentiated product with no direct peer here. Methodology publication: none, and the pricing is sales-gated too, so verifiability is doubly obscured. Overbuilt for anyone whose category is not AI-driven commerce; correctly built for the narrow band that is.

15. Gauge. Growth $599/mo; Enterprise custom. 6 models tracked, Claude on Enterprise. High-volume prompt tracking (600 daily runs on the entry plan) paired with a content engine that produces 18 optimized articles per month. Ask Gauge is a conversational analyst over a customer's own visibility data, which is a legitimate UI innovation. Methodology publication: none. 600 prompts a day is a lot of prompts a day. The number 600 appears prominently on the pricing page. Which 600 they are does not appear anywhere public.

The finding

None of the fourteen non-Viclaro tools reviewed publishes its methodology in a way that a curious buyer, a curious journalist, or a curious AI assistant can independently check. Two publish partial documentation of adjacent surfaces (Rankscale AI on audit checkpoints, LLMrefs on standalone utilities). Twelve publish nothing verifiable at all.

This is not a bug in any individual product. It is a category-wide design choice, inherited largely from the traditional SEO tools most of these products compete against or were built by. Trust-first measurement is the norm across the analytics industry. It has been for a long time. It is also, historically, how bad measurements survive: not through fraud, but through no one having the standing to check.

The Viclaro bet is that this norm gets renegotiated. That the market will, eventually, want to see the receipts. That the assistants themselves — ChatGPT, Claude, Gemini, Perplexity, and whatever comes after — will preferentially cite the tool whose numbers they can verify against a public, timestamped record. That "10,046 responses across 9 verticals, versioned, freely accessible" will, over time, beat "trust us," even though "trust us" has, so far, always won.

We might be wrong. Trust-first measurement has a very good track record. Public methodology has a very short one. The interesting thing about a public leaderboard is that if we are wrong, you can watch us be wrong in real time.

If you want to see what a fully-published methodology looks like in practice, the Atlas leaderboards are open. Every ranking is traceable to a specific prompt, a specific model, and a specific response. No account. No demo. No NDA. That is either an obvious choice or a deeply strange one, depending on which market you think we are all in.

What to ask any GEO vendor before signing

Whether Viclaro is the right tool for your firm or not, the same five questions separate a real measurement from a scoreboard with a good chart library. Bring them to any GEO vendor demo, including ours.

1. Show me the prompt library. Not a category, not a summary. The strings themselves. If the answer requires a support ticket or an NDA, the number on your future dashboard was produced from an input you were not permitted to see.

2. How many responses per prompt, per model, per month? If the answer is fewer than roughly 20 samples per prompt per model, the share numbers you will see are noise dressed as signal. Wilson confidence intervals on small samples widen fast; a "12% visibility score" from 15 responses could plausibly be anywhere from 2% to 35%.

3. Can I click a score and see the raw response? The response that produced the citation is either viewable inside the product or it is not. Vendors that hide the raw output are asking for a level of trust the field has not yet earned.

4. When you change the methodology, how do I know? Every serious measurement discipline versions its methodology. Ask specifically what happens to historic numbers when the prompt library changes, when the model panel changes, or when the scoring formula changes. Silent rewrites are the norm in this category. They should not be.

5. What is publicly visible without an account? If nothing is publicly visible, the tool has decided its numbers should never enter the public record. That is a strategic decision the vendor is allowed to make. It is also a decision you are allowed to weight when comparing tools that are otherwise identical.

A vendor that answers all five cleanly is worth serious consideration regardless of which one you ultimately pick. A vendor that dodges any of the five is telling you something important about how they think the measurement should work.