AI Visibility Tools, Compared

01What is an AI visibility tool, and what does it actually do?

The category exists because winning Google is no longer the same as winning AI. A buyer now describes a problem to ChatGPT, Perplexity, Gemini or Google's AI answers and gets back a shortlist assembled from sources they never see and you do not control. Classic rank tracking cannot observe any of it, there is no blue-link SERP to scrape, so a new instrument had to appear. This guide profiles seven of them in depth: Profound, Peec AI, AthenaHQ, Scrunch AI, Otterly, Semrush and SE Ranking.

Every product here runs the same three-step loop: store a prompt set, query one or more answer engines on a cadence, and parse each answer for your brand, the cited domains, and the rival brands named around you. The differences that decide which one is right for you are narrow but consequential: how the tool collects the data, how many engines it covers, how sound its sampling is, its use case and ideal customer, its real per-tier price, what its customers actually report, and the job it leaves unfinished. We take them in that order.

02How do these tools actually get their data?

An API call is cheap, fast and stable, but the API is not the product your buyer uses. API responses frequently strip the citations, source links and formatting that appear in the real chat interface, and an API call made without the web-search tool enabled returns the model's parametric memory, not a live retrieved answer, so the citation data is either missing or synthetic. Measure that and you are measuring a different product than the one your customer sees.

UI scraping drives a real browser session against the actual front end, so it captures what a human would: the rendered answer, the citation chips, the sources panel, the follow-up context. It is slower, more fragile and more expensive to run at scale, but it is the only method that reflects reality. The practical tell when evaluating a tool is simple: ask whether its numbers come from the API or the UI, and be sceptical of any dashboard that will not answer.

This is why two tools pointed at the same brand, same prompts, same day can report different numbers. Before you compare scores across tools, confirm you are comparing the same collection method.

03What can the numbers get wrong?

Generative models invent sources, or subtly alter a real domain, and legacy tools that scrape the answer text and regex out URLs will log those as real citations, producing false positives. The defence is cross-model overlap: a source that appears for the same query across several engines is far more likely to be genuine than a one-off that may be a hallucination. Then there are ghost citations, where a domain is cited but the brand is never named in the text, only reliably caught by UI-level detection. Sentiment scoring, which several tools sell, is only around 70 to 85 percent accurate because AI prose is full of hedged language classifiers misread. And the same prompt returns different answers on different runs, which is why one run per prompt is measurement theatre and five to ten runs is the floor.

04How do you evaluate an AI visibility tool?

The reference for the methodology axis is the Share of Model measurement discipline: a defensible prompt portfolio, multiple runs per prompt, and every number reported with its prompt set, engine and date attached. Hold any tool you trial to that bar.

05The seven tools at a glance

06Profound: the enterprise-depth pick

Use case: Enterprise-grade AI visibility analytics and governance across the widest set of answer engines, with automation (MCP) for technical teams.
Target audience / ICP: Mid-market to enterprise brands with a genuine AI-search business case and budget; over-built for a solo marketer.

What customers say. G2 reviewers (4.6/5) consistently praise the depth of analytics, the reporting value, the rapid product updates and strong support. The recurring criticisms are that pricing is high enough to exclude smaller teams and that the breadth carries a learning curve; most reviewers feel the value lands for mid-market and enterprise brands but is hard to justify for a small team without a clear AI-search case.

07Peec AI: the clean mid-market analytics

Use case: Clean measurement and reporting of brand mentions, answer position and sentiment across the major engines, with source-level citation data for outreach.
Target audience / ICP: Mid-market B2B marketing teams and in-house SEOs who want a dashboard to show leadership, not an optimization engine.

What customers say. Reviewers praise the clean interface, the suggested-prompts feature that saves setup time, the source-level citation data, and that sentiment is bundled into the mid tier where rivals charge a premium or omit it; unlimited seats and a no-card trial make it easy to start. The two consistent gripes: paying for broad engine coverage adds a few hundred euros on top of the €205 headline (the most common billing surprise), and Peec is a monitoring tool, it shows what is happening, not how to act on it.

08AthenaHQ: analytics with a nudge toward action

Use case: AI visibility analytics across eight engines with an 'Ask Athena' assistant and implementable recommendations, for a single brand and country by default.
Target audience / ICP: Mid-market in-house teams wanting more direction than a pure monitor; less suited to agencies because per-brand cost scales fast.

What customers say. One reviewer reported "immediate positive impacts" from implemented recommendations inside the first month, and the analytics are well regarded. The consistent caution is the credit model: the $295 headline understates real spend because monitoring and Ask Athena share one credit pool, per-brand pricing scales faster than most agency retainers, and there is no free trial, so you commit sight-unseen.

09Scrunch AI: monitoring plus agent analytics

Use case: AI visibility monitoring plus agent-experience analytics, how AI crawlers and agents fetch and use your pages, across the major engines at high prompt density.
Target audience / ICP: Mid-market to enterprise teams who care about both citation share and how agents interact with the site; not a fix-it tool.

What customers say. Reviewers call Scrunch a category leader in visibility monitoring and note its engine density beats most direct competitors on a per-engine basis. The limits are the familiar ones: actionable insight stops at monitoring (it surfaces gaps but does not fix them), the price is high, and there is only a 7-day trial with no free tier, so it is hard to adopt as a standalone solution.

10Otterly.ai: the budget and agency pick

Use case: Affordable AI visibility tracking plus per-prompt GEO auditing and agent analytics, built to scale across many client workspaces.
Target audience / ICP: Solopreneurs and consultants on Lite; marketing and AEO agencies on Standard and up, where reviewers call it essential for client reporting.

What customers say. G2 reviewers are mostly small businesses and agencies: the $29 Lite plan suits solopreneurs tracking one brand, while the $189 Standard plan is cited by marketing and advertising reviewers as essential for client reporting and AEO service delivery. The catch is that some engines you will want (Claude, AI Mode, Gemini) are add-ons, so the real cost for full coverage runs above the headline.

11Semrush AI Visibility Toolkit: the incumbent suite

Use case: AI brand and competitor visibility tracking plus an AI-readiness site audit, inside the wider Semrush SEO platform.
Target audience / ICP: SEO teams already paying for Semrush who want to extend into AI answers without a new vendor or login.

What customers say. The appeal in reviews is convenience: one login, one report, no new contract, and a credible AI-readiness audit. The limitation reviewers note is depth, fewer engines on the self-serve tiers (Claude, Copilot and DeepSeek require Enterprise AIO), and an AI module that is broader but shallower than a dedicated specialist's.

12SE Ranking (SE Visible): the affordable incumbent

Use case: Affordable AI visibility and prompt tracking with competitor research across Google AI Overviews, AI Mode, ChatGPT and Perplexity.
Target audience / ICP: Agencies and SMBs wanting low-cost AI tracking, and existing SE Ranking customers extending their suite.

What customers say. Reviewers like the price-to-coverage ratio, unlimited AI-source tracking and prompt tracking on every plan, and the convenience for existing SE Ranking users. The common note is that the headline entry price is misleading: meaningful AI coverage requires the add-on or SE Visible, which pushes the real monthly cost to $150-$240+, and engine coverage is narrower than the specialists'.

13Who else is worth knowing about?

Evertune is the notable omission from the deep profiles because it is demo-led and priced for enterprise (published figures range from $800 to $3,000+ a month), but its consumer-panel methodology, AI Brand Index, model-version tracking and new shopping-intelligence layer make it the serious option above Profound for large brands that want perception data, not just citation counts. The long tail, Frase (content plus visibility), Bloomiro, ZipTie, Writesonic and others, each optimise one axis, price, content grading, or a niche engine, and are worth a look only after the six-axis framework in section 4 tells you which axis you actually care about.

14What do none of these tools actually do?

Read the seven reviews together and one phrase repeats: shows what is happening, not how to act on it. That is not a knock on any single vendor, it is the shape of the category. A clean dashboard number will not tell you why a technically healthy page still gets zero citations, will not write the answer block, clear the retrieval blocker, or build the comparison page that moves the number. Reporting the gap and closing it are two different jobs.

15How is RawMktg's approach different, and who is it for?

The trackers in this guide answer one question well: am I cited, how often, and versus whom. RawMktg is built for the question that comes next, why am I not, and what exactly do I change. It fetches each page as OAI-SearchBot or PerplexityBot would, computes the Content Visibility Ratio (how much of the page survives without JavaScript), runs roughly fifty checks across retrievability, answer structure and markup, and returns findings located to the exact page and element and weighted by impact, alongside the generated fix. It measures Share of Model the way a tracker measures visibility, but it does not stop at the number.

The benefit of closing the loop

A tracker's dashboard tells you the temperature is falling; it does not tell you the window is open. Because RawMktg diagnoses and remediates in one workflow, the output is not a trend line but a prioritised worklist: this page fails gate 1 on a stray noindex, that section has no answer block, this comparison page is missing schema, ranked by the citations each fix is likely to move. The path from measure, to why, to fix lives in one place, which is the difference between knowing you have a problem and having it solved. It closes exactly the gap every tracker's own customers name in their reviews: monitoring-only.

Who this approach is for

Diagnosis-and-remediation fits teams that have already looked at a tracker's number and asked 'now what': in-house B2B marketers who need to move the number rather than watch it; SEO and content teams who want located fixes instead of a score; and agencies who need to show clients the change, not just the chart. It is less relevant if all you need today is a broad multi-engine monitor, in which case a tracker above, paired with RawMktg when you are ready to act, is the right stack.

Here is how the approaches stack up. The trackers lead on breadth of engine monitoring, which is their job; RawMktg leads on the audit-to-fix axis, which is a different job. Read it as complementary, not head-to-head.

The honest summary: if the question is 'where do I stand across the AI engines', buy a tracker. If the question is 'why am I not cited and what do I change', that is the audit-and-fix job RawMktg is built for, and you can start with a free audit of 25 pages. Most serious programs end up wanting both.

16What actually drives the price?

Work it backwards from methodology. A defensible program tracks, say, 100 buyer prompts, five runs each, across five engines, 2,500 queries per cycle before competitors. That instantly rules out entry tiers: Profound Starter (50 prompts, one engine), Otterly Lite (15 prompts), and Semrush Base (25 prompts) are proof-of-problem plans, not measurement programs. Query allowances are the hidden meter, Peec Pro includes ~3,500 monthly queries and the moment your set or run count grows you move up a tier.

monthly_queries  =  prompts  x  runs_per_prompt  x  engines  x  cycles_per_month

  100 prompts  x  5 runs  x  5 engines  x  1 cycle       =   2,500 / month
  + 3 competitors tracked on the same prompt set         =  ~10,000 / month

  Match this to each tool's query/credit allowance, not its headline tier.

17Which AI visibility tool should you buy?

18How do you actually run one once you have it?

Whichever tool you pick, the rules do not change: build the portfolio from real buyer questions across the buying stages, run each several times, and read the cited-domains list as your outreach and competitive map. If you want to understand the machinery before you pay for it, the loop is about a dozen lines of code, and it shows exactly why the search tool must be enabled.

# minimum-viable AI visibility tracker: run a prompt set, log who gets named.
# The paid tools automate this across engines; the logic itself is not complicated.
import itertools, csv, re, datetime as dt
from openai import OpenAI            # swap in Perplexity / Gemini / Anthropic clients too

client  = OpenAI()
BRAND   = "YourBrand"
PROMPTS = open("prompts.txt").read().splitlines()   # the questions your buyers actually ask
RUNS    = 5                                          # one run per prompt is noise, see Share of Model
URL_RE  = re.compile(r"https?://([^/\s)]+)")

rows = []
for prompt, run in itertools.product(PROMPTS, range(RUNS)):
    # IMPORTANT: enable the web-search tool, or you measure parametric memory, not real citations.
    r = client.responses.create(model="gpt-5", tools=[{"type": "web_search"}], input=prompt)
    text = r.output_text
    rows.append({
        "date":    dt.date.today().isoformat(),
        "engine":  "chatgpt",
        "prompt":  prompt,
        "named":   BRAND.lower() in text.lower(),        # the binary that matters
        "domains": sorted(set(URL_RE.findall(text))),    # your outreach + competitive map
    })

csv.DictWriter(open("visibility.csv","w"), fieldnames=list(rows[0])).writerows(rows)
# Answer Share = share of (prompt x run x engine) rows where named == True.

For the full measurement stack, prompt design through GA4 attribution and Looker reporting, see prompt-to-citation tracking, and for the standard the numbers should meet, the RawMktg methodology.

19Method and honest limits

Pricing tiers, query allowances, engine coverage and features in this category change monthly, and several vendors quote enterprise pricing only on request, so confirm current details with each vendor before you buy. Customer sentiment is summarised from public reviews on G2, Capterra and independent write-ups and reflects reviewers' experiences, not ours. RawMktg builds a diagnosis-and-remediation product and is not a neutral party on the measurement-versus-remediation point in section 14; the tool comparison itself is kept even-handed and sourced. Where a call is a judgement (ideal customer, shape), that is our read.