Case study: Gumlet turned ChatGPT mentions into 20% of inbound revenue. Read it →
The best AI brand monitoring tools for ChatGPT, Gemini and Perplexity in 2026 are Profound, Peec AI, Ahrefs Brand Radar, Semrush AI SEO, Otterly.AI, Scrunch AI, Evertune, Rankscale and Conductor. DerivateX shortlists them on 7 criteria, not feature count: prompt economics, engine and model controls, citation depth, sentiment, competitor sets, exports and who acts on the data.
Nine products are worth a shortlist in 2026. DerivateX groups them by the job they are genuinely built for, because the category has split into types that get sold as one: prompt trackers, citation and source analyzers, enterprise brand intelligence platforms, and crawlability monitors that look at how AI agents read your own site. Buying the wrong type is the most common and most expensive mistake in this purchase.
Read the table as a shortlist organized by job to be done, not as a benchmark. Each row reflects how the vendor positions itself on its own site, which is linked so you can check the current product and pricing yourself.
| Tool | How it positions itself | Type | Best fit | Ask this on the demo |
|---|---|---|---|---|
| Profound | Enterprise AI visibility and answer engine analytics platform | Enterprise brand intelligence | Teams with a dedicated analyst and budget for depth | What is the all-in annual cost at my prompt volume, and how much history do I get on day one? |
| Peec AI | AI search visibility tracking for marketing teams and agencies | Prompt tracker | Lean marketing teams that need a usable weekly number | Are prompts, engines and markets billed separately or bundled? |
| Ahrefs Brand Radar | AI mention and citation tracking inside the Ahrefs platform | Citation and source analyzer | SEO teams already paying for Ahrefs | Is Brand Radar included at my current plan tier or priced on top? |
| Semrush AI SEO | AI visibility reporting inside an existing search suite | Citation and source analyzer | Companies consolidating vendors | How often are prompts re-run, and can I control the model version? |
| Otterly.AI | Simple prompt monitoring across AI search engines | Prompt tracker | First-time buyers proving the problem exists | What happens to my data if I downgrade or pause? |
| Scrunch AI | Monitoring of how AI crawlers and agents experience a brand’s own site | Site and agent crawlability | Teams that suspect their own site is unreadable to AI crawlers | Which user agents do you log, and can I see the pages they failed to read? |
| Evertune | Model-level brand analysis across large language models | Enterprise brand intelligence | Brand and research teams, not just demand gen | Is this sampled at the model level or scraped from live product interfaces? |
| Rankscale | AI search visibility auditing and tracking | Prompt tracker | Agencies running many small accounts | Can I white-label reports and export raw rows by client? |
| Conductor | Organic marketing platform with AI visibility added | Citation and source analyzer | Enterprise SEO programs with content workflow needs | Does AI visibility sit on the same contract and seat count as the core platform? |
Two roundups worth reading alongside this one, because they organize the category differently and will catch things we did not: the Refine list of AI visibility tracking tools and the Marqeable comparison of AI visibility tools. Reading three sources beats reading one, especially in a category where product pages change every few weeks.
DerivateX evaluates these products against the same buyer-prompt set we run for client programs, which means we care about nine columns and ignore feature lists. Here is the honest disclosure first: DerivateX has not run a controlled, published accuracy test across all nine platforms, so this article is a buying framework and a set of verification questions rather than a scored head-to-head. Each platform samples differently, and a score produced under our sampling would not reproduce under yours. What we can do is hand you the worksheet and tell you which columns decide the outcome.
Take the table below into every demo and fill the middle column with the vendor’s answer in writing. The point is not to compare marketing pages. The point is to leave with nine confirmed answers you can hold a vendor to at renewal.
| Column to verify | What a usable answer looks like | How to confirm it in the demo |
|---|---|---|
| Engine coverage | Named engines with per-engine data kept separate, not blended into one score | Ask them to filter the live dashboard to Perplexity only, then to ChatGPT only |
| Prompt limits | A stated cap on prompt runs per month, with engines and markets counted explicitly | Give them your real prompt count, engine list and market list, ask for the tier and price |
| Historical data | Whether anything is backfilled at signup, plus the retention limit in months | Ask what the chart will show on day one and what disappears at month 13 |
| Citations | Source URLs returned per response, not just a brand mention flag | Request a sample export containing prompt, response text and citation URL |
| Sentiment | A documented scoring method plus access to the raw response behind a score | Pick one negative score on screen and ask to see the underlying answer |
| Competitor tracking | Number of competitors included, and whether the set can change mid-contract | Ask what a change order costs when your category shifts in month four |
| Exports and API | CSV at row level and an API on the tier you can afford, not only on enterprise | Ask which tier the API unlocks and request the API documentation link |
| Reporting and CRM | Scheduled reports a non-specialist can read, plus a webhook or warehouse connection | Ask to see the report a CFO would receive, not the analyst dashboard |
| Price | All-in annual cost at your workload, including overage rates | Ask for the invoice you would receive in month one and in month twelve |
One row sits above the other nine, and buyers skip it. Most software companies that churn off an AI visibility tool do not churn because the data was wrong. They churn because nobody had time to turn a weekly delta into a change on the website, in the docs, or on the third-party sources the models actually quote. If you cannot name the person who will own the Monday morning review before you sign, buy the diagnostic instead of the subscription.
DerivateX uses this section to separate what each product is genuinely good at from what it is not built for. Every product below is described by its own public positioning or by how the two roundups above categorize it, and every limitation is a trade-off rather than a flaw. Verify pricing and engine coverage on each vendor’s own page on the day you evaluate, because in this category a figure quoted in a blog post is out of date within a quarter.
Refine’s 2026 roundup puts Profound in the deepest enterprise analytics bucket, alongside Athena HQ, and that grouping matches how the product presents itself: conversation-level depth rather than a single visibility percentage. It is the strongest choice when you have an analyst who will live inside the data and a budget that treats this as a research line rather than a tool line. Depth costs money and attention, though, and a two-person growth team will use a fraction of what they pay for. Ask what the all-in annual number looks like at your real prompt volume across every market you sell into, then divide by the number of decisions you expect to make each month.
Peec AI positions itself as AI search visibility tracking for marketing teams and agencies, with an interface built to be read rather than interrogated. It fits lean teams at $5M to $50M ARR who need a defensible weekly number for a board deck and do not have an analyst to spare. The limitation is the mirror of Profound’s strength: fewer levers to pull when you want to understand why an answer changed. Confirm on their own site whether prompts, engines and markets are bundled or metered separately, because that single answer moves the effective monthly cost more than any discount you negotiate.
If your team already holds an Ahrefs seat, Brand Radar is usually the cheapest credible way to get a baseline, because it sits inside a platform your SEO team already trusts for backlink and keyword data. Two things decide whether it is enough. First, whether the feature is included at your current plan tier or priced on top, which the Ahrefs Brand Radar product page and your account manager can settle in a single email. Second, whether prompt-level control and per-engine granularity are sufficient for you, because they are narrower than in purpose-built prompt trackers and that gap widens as your prompt set grows past a few dozen. Also check how far back the data goes for your specific domain before you treat it as a baseline.
Marqeable’s comparison of AI visibility tools describes Semrush AI SEO as pairing an AI Visibility Index with an AI SEO dashboard inside the wider Semrush suite, which is exactly the shape of product procurement teams like. It works well when vendor consolidation pressure is real and you would rather extend an existing contract than start a new one. The honest limitation is the same as any suite feature: the roadmap is shared with a large product, so specialist depth arrives later than it does at focused vendors. Ask how often prompts are re-run and whether you can control which model version answers, because refresh frequency determines whether your chart shows a trend or a series of coin flips.
Think of Otterly.AI as the evidence-gathering purchase. A founder who typed the category into ChatGPT, did not see the company, and needs something concrete for a leadership meeting this week can get a first result quickly and cheaply. Simple tools stay simple, which is the trade: the ceiling arrives once you want multi-market tracking or model-level control, and at that point you are migrating. Before you commit, ask what happens to your history if you pause or downgrade, because losing the baseline is worse than never having had it.
Scrunch AI comes at the problem from the crawlability angle, monitoring how AI crawlers and agents encounter a brand’s own properties. Refine’s roundup separates it from the pure tracking platforms for that reason, naming it the pick for site and agent crawlability and describing it as a useful complement to a tracking tool rather than a replacement for one. That framing is the buying advice. If your suspicion is that models cannot read your site properly, this is the diagnostic. If your question is what models say about you against three rivals, this is not that product, and most buyers end up running it alongside a prompt tracker. Ask which user agents it logs and whether you can see the specific pages an agent failed to render.
Evertune frames the problem as brand research rather than search reporting, analyzing what models have absorbed about a company at the model level. It earns its place when the buyer is a brand or insights team asking what models believe, not just how often the name appears in a list. The limitation follows directly from the method: model-level analysis answers a different question from live prompt tracking, so you may still want a tracker alongside it. Ask whether measurements are sampled at the model level or captured from live product interfaces, because the two approaches can disagree and you need to know which one you are quoting to an executive.
Agencies have a different buying problem from in-house teams, and Rankscale is built around it with an audit-first workflow across many accounts. White-labeling, per-client separation and raw exports are the three things that decide whether a tool survives in a portfolio, and this is the class of product designed for them. Audit-led tools optimize for breadth rather than depth on a single account, so your largest client may outgrow it before your smallest does. Confirm export format and whether report branding is available on the tier you can actually afford across every account you run.
Conductor is an organic marketing platform that has added AI visibility to an existing enterprise workflow, including content guidance and governance. It is a sensible pick for enterprise SEO programs where the constraint is workflow and approvals rather than measurement alone. The limitation is contractual as much as technical: enterprise platforms come with enterprise procurement cycles, and a 90-day question does not fit a 12-week purchase. Ask whether AI visibility sits on the same contract and seat count as the core platform, and what the shortest available term is.
Others worth a look depending on your situation include Signum.AI for LLM presence tracking, Nightwatch for teams that want AI visibility alongside rank tracking, and Athena HQ, which Refine groups with Profound for enterprise analytics depth. DerivateX maintains a wider list in our roundup of LLM visibility trackers for B2B SaaS, which covers products that did not make this nine.
Almost every tool in this category prices on prompt runs, seats, or a combination, and two products with near-identical sticker prices can produce very different invoices once your real workload is applied. SaaS pricing pages rarely show that multiplier, so you have to compute it yourself. The arithmetic is simple and nobody puts it on a pricing page:
Monthly prompt runs = prompts × engines × markets × refreshes per month.
Work a normal example. A B2B SaaS company tracking 150 buyer prompts across four engines, in two markets, refreshed weekly, needs 150 × 4 × 2 × 4, which is 4,800 prompt runs per month. A plan advertised around 500 prompts is not close. The same company tracking 40 prompts, on two engines, in one market, monthly, needs 80 runs and will be fine on an entry tier. Run your own number before you look at a single price, because the number decides which tier you are shopping in, and the tier decides whether the price you were quoted was real.
| Pricing model | How it bills | Where it bites | Best for |
|---|---|---|---|
| Prompt-metered | Per prompt run, with engines and markets multiplying | Cost scales fast when you add a second region | Focused prompt sets in one market |
| Seat-based | Per user, prompts bundled to a cap | Sharing access with sales or leadership gets expensive | Lean teams with a single owner |
| Suite add-on | Bundled into an existing search platform contract | Tier gating, and depth trails specialist vendors | Vendor consolidation |
| Enterprise quote | Annual contract, custom volume, SSO and support included | Long procurement, annual lock-in | Multi-brand organizations |
One more cost most buyers miss. Historical depth is usually not backfilled, so the month you sign becomes your baseline forever, and a tool bought in month six of a program cannot tell you what happened in months one to five. If board reporting matters to you, start tracking earlier than you think you need to, even on a cheap tier, purely to own a baseline. DerivateX decides which prompts deserve a paid slot by ranking them on buyer intent and deal size first, then filling the run budget from the top down.
Nearly all nine claim coverage of the major engines, so DerivateX treats engine count as a screening question rather than a deciding one. The real question is whether the data stays separated by engine, because the engines do not agree with each other. Only 11% of domains are cited by both ChatGPT and Perplexity, and 80% of URLs cited by ChatGPT and Perplexity are not in Google’s top 100. An averaged visibility score across four engines hides exactly the thing you need to act on.
Three engine-level controls change the quality of the data more than coverage breadth:
Source mix matters too. Around 46.7% of Perplexity’s top sources come from Reddit, which tells you that a Perplexity gap and a ChatGPT gap often need different work: one is a community and third-party presence problem, the other is usually a corroboration problem across review sites, comparisons and documentation. DerivateX sees those differences hold across client programs, and they are large enough to change what you build first.
Reporting quality is not dashboard design, and DerivateX judges it on one thing: can you get raw rows out and join them to pipeline. A platform that shows a clean visibility trend but cannot export prompt-level responses with source URLs and timestamps will not survive a budget review, because 64% of marketing leaders are unsure how to measure AI search and the ones who solve it do so by putting AI-sourced sessions next to demo requests in their own warehouse.
Reporting features worth paying for, in order:
This is also where the payoff sits. AI-sourced visitors convert 4.4x higher than other channels, which means a modest number of AI-referred sessions can carry real pipeline weight. If your tool cannot separate AI-referred traffic in a form your analytics can consume, that argument stays unprovable, and unprovable arguments lose at the next review. There is also the harder-to-see half of the picture: 28% of ChatGPT-cited pages have zero organic Google visibility, so a report that only cross-references your ranking pages will understate what is actually earning citations.
For multi-brand or multi-region organizations, DerivateX would shortlist Profound, Evertune and Conductor, and would judge them on governance rather than on charts. Refine’s roundup also names Athena HQ alongside Profound for enterprise analytics depth, which makes it worth a demo slot. Scrunch AI belongs on the same evaluation list in a different role, as the crawlability and agent-access check rather than the brand intelligence layer. The features that decide an enterprise purchase are unglamorous: single sign-on, role-based access, workspace separation by brand and region, data retention length, audit access to raw responses, invoicing terms and a security review the vendor can actually pass.
Three enterprise-specific traps show up repeatedly:
If you are at the smaller end and an enterprise platform feels oversized, it probably is. A team of three at $8M ARR does not need workspace governance, and paying for it delays the only thing that matters, which is publishing and earning the evidence that models quote.
DerivateX takes a position here rather than hedging. For a B2B SaaS company at $5M to $50M ARR with one product and one or two markets, start with Peec AI or Otterly.AI if you need speed and a readable weekly number, or with Ahrefs Brand Radar if you already hold an Ahrefs seat and want the cheapest credible baseline. Move to Profound when you have an analyst whose job includes this data, not before.
The reasoning is about capacity, not features. At that revenue band the constraint is almost never data volume, it is hours. You will get 3 to 4 brands recommended per AI category query, so the whole game is being one of them on the prompts your buyers actually type. A tool that produces 40 usable prompt rows you review every Monday beats a platform producing 4,000 rows nobody opens. Choose the tool your team will still be using in week nine.
One caveat worth stating plainly. If your category is contested by companies with far larger content and PR budgets, measurement alone will not move your position, and the tool will simply document the gap with increasing precision. That is the point at which the decision stops being a software decision.
DerivateX is a managed SEO and GEO service, not a product, which is why we are not a row in the shortlist table above. The distinction is worth being precise about, because buyers routinely purchase measurement when they needed execution, or the reverse.
| AI brand monitoring tool | Managed SEO and GEO (DerivateX) | |
|---|---|---|
| What you get | Prompt tracking, mentions, sentiment, citations, dashboards | Prompt research, content and corroboration work, off-site source building, weekly citation tracking, reporting to pipeline |
| Who does the work | Your team | DerivateX, with your team reviewing |
| Cost | Software subscription, scales with prompt runs | From $6,000 to $6,200 all in per month, to $14,000 to $15,000 all in for the largest tier |
| Time to first signal | Days, for measurement | Weeks to months, for position change |
| Buy it when | You have people and content capacity, and need a number | You have the number and no capacity to close the gap |
DerivateX pricing is public: Rank and Get Found is a $5,000 retainer plus $1,000 to $1,200 of off-site budget, Own Your Category is $8,000 plus $1,500 to $2,000, and Market Leader is $12,000 plus $2,000 to $3,000 on a six-month minimum. The first two run as 90-day pilots with no lock-in afterwards. There is also a $3,500 one-time diagnostic delivered in two weeks that credits in full against month one if you convert within 30 days, and a free AI visibility audit with 48-hour turnaround. Current figures are on the DerivateX pricing page, accurate as of September 2026.
Here is where a tool is the better purchase, said plainly. If you have an in-house content team shipping consistently, a Head of SEO with time, and your only gap is measurement, buy Peec AI or Profound and keep the retainer. You do not need an agency to tell you what to write when you already know. DerivateX is the wrong call for companies below roughly $5M ARR, for teams that want a one-off audit rather than an operating cadence, and for anyone hoping a vendor will guarantee citations. Nobody can guarantee what a model returns next quarter.
What DerivateX commits to is process, cadence and measurement. Citation Engineering is the methodology DerivateX uses to make language models recommend a brand deliberately, by building the evidence and corroboration those models draw on rather than by trying to trick a ranking. A Citation Surface Map is the inventory of every source a category’s answers currently pull from, which tells you where to earn presence next. The AI Visibility Score, or AVS, is the single tracked number DerivateX reports weekly so a marketing leader has something defensible to take to a board review. Those are operating tools, not promises about outcomes.
On results, three are worth naming because they are the only ones we publish. REsimpli became the most cited and recommended real estate CRM for investors in ChatGPT within 90 days. Gumlet attributes more than 20% of monthly inbound revenue to AI discovery. Verito moved from an average position of 40 on Google to the first page, and is cited and recommended on ChatGPT and Google AI Overviews for 40 of their commercial hosting queries. Those are targets we work toward for new clients, not guarantees we extend.
DerivateX will be direct about the ceiling here, because the disappointment pattern is predictable. These tools measure. They do not publish, they do not earn third-party mentions, they do not fix a comparison page that models skip, and they do not decide which of your twelve competitors to contest first. A dashboard that reports the same gap every week and changes nothing is the single most common outcome of this purchase, and it is not the vendor’s fault.
The backdrop explains the urgency. Around 40% of Google queries now show AI Overviews, click-through drops by about 61% when they appear, and 73% of B2B sites lost significant traffic between 2024 and 2025. With more than 200 million weekly ChatGPT Search users, the buyer research that used to start on a results page now often ends in an answer. Monitoring tells you where you stand in that answer. Standing somewhere different is separate work, and it usually involves content, documentation, review presence, and the third-party sources that engines treat as corroboration.
AI visibility is whether and how often a brand is named, cited or recommended in ChatGPT, Gemini, Perplexity and Google AI Overviews answers to buyer prompts. You see it by running a fixed prompt set on a schedule and logging mentions, citations and competitor names per engine, either manually or through a tracking tool.
Profound, Peec AI, Ahrefs Brand Radar, Semrush AI SEO, Otterly.AI, Scrunch AI, Evertune, Rankscale and Conductor. Enterprise teams should start with Profound, Evertune or Conductor. Lean B2B SaaS teams should start with Peec AI, Otterly.AI or Ahrefs Brand Radar. Verify pricing and engine coverage on the vendor’s own page before buying.
Most bill on prompt runs, seats, or as an add-on to an existing search suite, and enterprise tiers are quoted annually. Calculate prompts multiplied by engines multiplied by markets multiplied by refreshes per month before comparing prices. A 150-prompt, four-engine, two-market, weekly setup needs 4,800 prompt runs monthly, which rules out most entry tiers.
Most of the nine claim coverage of ChatGPT, Gemini, Claude, Perplexity and Google AI Overviews, so engine count is a poor differentiator. Engine separation matters more, because only 11% of domains are cited by both ChatGPT and Perplexity. Ask about model version pinning, region control and whether sampling comes from APIs or live interfaces.
Buy the tool if you have content capacity and only need a number. Buy managed SEO and GEO if you already know the gap and have nobody to close it. DerivateX runs 90-day pilots from $6,000 to $6,200 all in per month, plus a $3,500 diagnostic that credits against month one if you convert within 30 days.
Some return source URLs alongside brand mentions, others report mentions only, and the difference decides whether you can act on the data. Ask for a sample export with prompt, engine, response text, citation URL and date. Without URLs you learn that visibility changed, but not which source or competitor caused it.
Decide who owns the Monday review. Every other question in this purchase, engine coverage, prompt limits, historical depth, sentiment scoring, export format, follows from that one answer, because a tool is a measurement instrument and an instrument with no operator produces reports rather than pipeline. If the owner is a capable in-house team, buy the tool that fits your prompt workload and keep your budget for content. If there is no owner and the gap is already visible in ChatGPT, software is the wrong line item and you are buying execution, not dashboards.
Book a discovery call and DerivateX will run your prompt set across ChatGPT, Gemini, Claude and Perplexity, show you where you are cited today, and tell you honestly whether you need a tool or a team: book a discovery call with DerivateX.
Apoorv Sharma is the co-founder of DerivateX, a B2B SaaS SEO and Generative Engine Optimization agency that engineers AI citations in ChatGPT, Perplexity, Claude, and Gemini and connects them to demo bookings and revenue pipeline. He is the author of the 2026 AI Visibility Benchmark Report and the Citation Engineering methodology. He's also the brain behind "Found On AI" and has sold 2 of his companies previously
Shivanshi Bhatia is the co-founder of DerivateX, a B2B SaaS SEO and Generative Engine Optimization agency that engineers AI citations in ChatGPT, Perplexity, Claude, and Gemini and connects them to demo bookings and revenue pipeline. She runs operations and delivery, which means every audit, content brief, and published page ships through a system she built. She owns the client relationship from kickoff through reporting, so clients spend their time on decisions instead of chasing updates. She has worked in SaaS since 2019 and reviews client work before it goes live.
