Key takeaways
- Hallucination tracking is a different job from mention tracking. A tool that tells you "you were mentioned 40 times" says nothing about whether the 40 answers got your pricing right.
- LLMClicks is built around accuracy checks, with pricing and feature hallucination alerts. Profound and Promptwatch both have fact-checking features inside broader platforms. Otterly.AI is a low-cost visibility monitor, and I found no evidence of accuracy checking in its published material.
- Detection is half the problem. The useful question is which page the model learned the wrong claim from, and whether your fix got picked up.
- Treat vendor claims, including the ones in this article, as claims to test. Seed a few known-wrong facts and see which tool catches them.
- Verify every price on the vendor's pricing page. Several numbers below come from third-party roundups.
What brand hallucination actually means
A hallucination, in the brand sense, is any claim an AI answer makes about you that is false or out of date. Typical examples: a pricing tier you retired two years ago, a feature you never built, an integration you dropped, a founder who left, a competitor's weakness attributed to you.
There are two separate failure modes, and they need different fixes.
- The model invents it. Nothing on the web says this. The model filled a gap from training data or pattern-matching.
- The model found it. A real page, maybe yours, maybe a review site or an old comparison article, states the wrong thing, and the answer cites it.
The second is more common than people expect, and it is fixable. The first is mostly a content-coverage problem: give the model a clear, crawlable source of truth so it has less room to improvise.
One detail from Promptwatch Data matters here. In that report (dated September 2025, ChatGPT messages where web search was triggered), ChatGPT cited about 5 sources per answer, while Google AI Overviews and Perplexity cited about 10. With only five slots, one stale third-party page getting cited carries a lot of weight. That is my inference, not something the report states, but it is why citation-level visibility matters for hallucination work.
Mention tracking vs accuracy tracking
Most AI visibility tools started as mention trackers. They run a set of prompts, record whether your brand shows up, and score it. That is useful. It is not hallucination detection.
Accuracy tracking needs at least three more things:
- Extracting the individual claims in a response, not just the brand name.
- Comparing those claims against a source of truth you control.
- Pointing to where the wrong claim came from.
Tools differ a lot on how much of that they do. That is the whole comparison.
The four tools at a glance
| Tool | Accuracy or hallucination angle | Data collection | Entry price (verify on vendor site) | Best for |
|---|---|---|---|---|
| Promptwatch | Brand Narrative on every plan; Fact Check on Business and above, tied to the pages that state the claim | Real AI interfaces | $95/mo Essential; Fact Check from $579/mo Business | Teams that want to detect, trace and fix |
| LLMClicks | Core positioning: pricing and feature hallucination alerts, accuracy audit | Not verified | Reported $49 to $399/mo (third-party) | Accuracy-first monitoring on a budget |
| Profound | FactCheck agent flags inaccurate claims | Says it captures front-end interfaces | Custom for brands; 7-day trial | Enterprise teams needing many engines and compliance |
| Otterly.AI | Visibility, citations, sentiment; no accuracy feature found | Real web interfaces (vendor claim) | $29/mo Lite | Cheap multi-country visibility |
Promptwatch
Promptwatch approaches hallucinations as one step in a loop: monitor what AI says, trace where it got it, fix the source, then check whether the fix worked.
The relevant pieces:
- Brand Narrative pulls the individual claims AI makes about you and competitors out of every tracked response. Examples would be "affordable for small teams" or "its mobile app lacks features." It groups repeated claims, shows which models say each one, and files them under themes like Pricing, Features, Support and Reputation, with a 0-100 score per theme. It is on every plan.
- Fact Check lets you mark a claim Accurate or Inaccurate. It then reads the pages crawled from your site to find the ones that state the claim or its opposite, with quotes and a recommended next step. So you learn which page the model is learning the wrong thing from. It runs on Business, Agency Scale and Enterprise, and uses Agent Credits.
- Real interface data. Promptwatch monitors the actual UIs of ChatGPT, Google AI Overviews, AI Mode, Gemini, Perplexity, Copilot and others, per country and language. User-facing answers can differ from API outputs, which matters when you are checking what a buyer actually saw.
- Crawler logs and Page Tracker. IP-verified AI crawler logs show whether bots read your corrected page at all. Page Tracker shows whether AI cites it. That closes the loop on a fix.
- Content Agent and Action Board. Once a wrong claim is traced to a gap, the Action Board prioritizes the fix and the Content Agent (Business and above) can draft and publish the correcting page.
Pricing, per the vendor: Essential $95/mo, Professional $245/mo, Business $579/mo. Confirm on promptwatch.com/pricing. There is a 7-day free trial on monthly billing, and a free AI visibility report if you want a first look before committing.
The honest caveat: Fact Check starts at the Business tier, so a small team on Essential gets Brand Narrative (claim extraction) but not the page-level matching. For many teams, Brand Narrative alone surfaces the wrong claims, and you do the tracing by hand.
I found no Promptwatch research report that covers hallucination rates directly, so I won't quote a number for how often AI gets brands wrong.
LLMClicks
LLMClicks is the most hallucination-focused of the four. Its site positions it as a tracker and auditor for how ChatGPT, Perplexity, Gemini and Claude represent a brand, with pricing and feature hallucination alerts.
A HackMD comparison describes a 120-point AI accuracy audit and says it was the only tool in the author's test to catch all four seeded pricing hallucinations. Be careful with that. It is one reviewer's claim, and LLMClicks appears to be the author's own tool. It is a plausible test design, though, and you can copy it (see below).
The same source lists LLMClicks at $49 to $399/month, with a $149 Pro plan (full audit, competitor tracking, 500 prompts) and a $399 Agency plan (unlimited brands, white-label). I did not verify these. Check the pricing page.
Where it likely fits: small brands and agencies whose main fear is wrong pricing and feature claims. What I could not confirm: how it collects data (real interfaces vs API), and whether it traces a wrong claim back to the specific source page. Ask in a trial.
Profound
Profound
Profound is an enterprise AI visibility platform. Its Answer Engine Insights show how engines describe your brand, with citation analytics, sentiment and a FactCheck agent for inaccurate claims. According to a Surfer roundup, it says it captures answers from each engine's front-end interface rather than through API calls, and Enterprise covers up to 9 engines, plus API access, exports, SSO/SAML and SOC 2.
Pricing is not public for brands. A 7-day trial tracks 50 prompts daily in ChatGPT, Gemini and Google AI Overviews, and prompts cannot be customized on the trial, which limits how well you can test hallucination detection with your own seeded facts. In Promptwatch's own comparison, Profound has claim flagging in the fact-checking column; read that as a competitor's summary and test it yourself.
Where it fits: large organizations that need compliance paperwork and many engines, and are fine with a sales process. For deeper detail, see the Promptwatch vs Profound page, keeping in mind who wrote it.
Otterly.AI
Otterly.AI
Otterly.AI is the budget option and a good one for what it does. Per its own blog, it queries real web interfaces, tracks citations by domain and URL, covers 50+ countries and languages, and has an MCP server, API and workspaces. Per the Surfer roundup, Lite is $29/month for 15 prompts (AI Mode and Gemini $9 each as add-ons, Claude $29), Standard is $189 for 100 prompts, and Premium $489 for 400. A 7-day trial needs no card.
What I did not find: any accuracy or hallucination feature in the sources I reviewed. The HackMD reviewer calls it basic mention tracking without accuracy detection. It does give you citations and sentiment, so you can spot a bad source being cited, but you would be doing the claim-checking yourself. Fifteen prompts on the entry plan is also thin for checking pricing, features, integrations and competitor comparisons across engines.
Also note that a Growth Method analysis says Semrush acquired Otterly.ai. If that matters for your procurement, check the current ownership and roadmap.
How to test these tools yourself
Vendor claims in this space are cheap. A test costs an afternoon.
- Write a fact sheet. Ten to fifteen verifiable facts: current pricing tiers, three real features, two features you do not have, your HQ, founding year, key integrations.
- Build prompts that would surface each fact. "How much does X cost?", "Does X integrate with Y?", "X vs competitor for small teams."
- Seed known errors. Find or recall a few wrong claims already circulating, such as an old price. Note which ones appear in real answers.
- Run the same set in each tool's trial. Count how many wrong claims each tool flags, how many it misses, and how many false alarms it raises.
- Check the trace. For each flagged claim, does the tool tell you which page or source produced it? A flag with no source is a to-do you cannot act on.
- Compare to the real UI. Ask the same prompts by hand in ChatGPT and AI Overviews. Roughly 75% of brands named via APIs reportedly did not match what the real interfaces showed (per the Surfer roundup), so a tool that only uses APIs may miss what buyers see.
What to do when you find a hallucination
Detection is the easy part. A rough order of operations:
- Find the source. If a page states it, fix or remove the page. If it is third-party, update the listing or contact the publisher. Review and directory sites are frequent culprits.
- Publish a clear canonical page. A plain pricing page and a facts page with current numbers, in text, not inside images or JavaScript widgets.
- Check crawler access. If AI bots cannot read your corrected page, the fix does nothing. Crawler logs, whether from Promptwatch or your own server logs, settle this quickly.
- Re-run the prompts. Models update on different schedules. Expect search-based answers to change faster than training-based ones.
- Keep a log. Date, claim, source, fix, result.
For wider reading on tool categories, the GEO software directory at bestgeosoftware.com lists more options, and Promptwatch Data has the citation research behind how AI engines build answers.
Which one should you pick?
| If you are... | Start with |
|---|---|
| A small brand worried mainly about wrong pricing and features | LLMClicks trial, then test its claims |
| A team that wants to detect, trace the source, publish the fix and confirm it | Promptwatch (Business for Fact Check) |
| A large enterprise with compliance requirements | Profound, via a sales demo |
| Watching budget and need multi-country visibility first | Otterly.AI, accepting that accuracy checks are manual |
My own view: the tool matters less than whether it connects a wrong claim to a page you can change. A dashboard that says "3 hallucinations detected" and stops there will not move anything. Pick the one whose trial shows you the source.
Gaps in this comparison
I could not verify current prices for any of these tools, and I did not find first-party documentation of hallucination features for Otterly.AI or full details for LLMClicks. Profound's FactCheck is described in secondary sources. Run the test above before you spend money.