⚖️ Fake Reviews and AI-Generated Testimonials: Demand Letters for Synthetic Defamation and Unfair Competition

Published: December 5, 2025 • AI, Demand Letters

A competitor doesn’t need a call center of paid shills anymore.

With a half-decent prompt and a cheap generative tool, they can spin up:

dozens of “customer stories” that never happened,
five-star “case studies” from fake personas, and
one-star attacks on your product across Google, Yelp, G2, Trustpilot, Amazon, LinkedIn, Reddit…

all in a weekend.

From a legal standpoint, the fact that AI wrote the review is irrelevant. The law sees:

defamation of your business and its owners,
false advertising / unfair competition under the Lanham Act if a competitor is behind it, and
violations of platform rules + FTC regulations that now explicitly ban fake and AI-generated reviews. (Federal Trade Commission)

This article is a practical roadmap for SaaS platforms, agencies, and other businesses that suspect a competitor is using generative tools to flood the internet with synthetic reviews and testimonials—and want to respond with targeted, evidence-heavy demand letters.

Contents

🧩 What counts as a “synthetic” review or testimonial?

Regulators and platforms are increasingly treating these as one bucket:

text, audio, or video created or heavily shaped by AI,
purporting to be from a real customer with real experience,
but the person, experience, or both are fabricated or materially misrepresented.

The FTC’s 2024 Final Rule on Fake Reviews and Testimonials squarely targets this:

Bans selling or purchasing “fake consumer reviews or testimonials,” including those from nonexistent people or people who never used the product, and explicitly includes AI-generated reviews in its examples. (Federal Trade Commission)
Works alongside the updated Endorsement Guides, which tell advertisers not to “procure, suppress, boost, or organize” reviews in ways that distort what consumers really think. (Federal Register)

Add to that recent FTC enforcement against:

a company selling AI-powered “testimonial and review” tools that generated deceptive endorsements, and
the AI writing tool Rytr, which agreed to end its fake-review feature as part of a settlement. (ftcattorney.com)

In other words, “AI did it” is not a defense. It’s just how the fraud is being scaled.

🧱 Legal frameworks you can use (and how they fit together)

🧾 Quick map

Legal hook	Used against	What it targets	Why it’s useful here
Defamation / trade libel	Individual reviewer, competitor directing them	False factual statements that harm your reputation or business	Powerful when the reviews accuse you of dishonesty, fraud, or incompetence and you can tie them to a competitor campaign. (White & Case)
Lanham Act § 43(a) (false advertising / unfair competition)	Competitor	False/misleading statements in “commercial advertising or promotion”	Strong federal cause of action when a competitor floods search results with fake negative reviews about you or fake positives about themselves. (Passle)
FTC rules & Endorsement Guides	Competitor, review vendors, AI tools	Sale, purchase, or orchestration of fake or misleading reviews	Creates regulatory risk and civil-penalty exposure on top of private suits—excellent leverage in a demand letter. (Federal Trade Commission)
Platform policies / ToS	Individual reviewers, business accounts	Violations of Google, Yelp, Amazon, G2, Trustpilot rules	Lets you go around the competitor and get accounts, listings, or reviews removed or suspended. (About Amazon)

Let’s unpack the big three: defamation, Lanham Act, and platform rules.

⚔️ Defamation and trade libel: synthetic but still actionable

A fake review is often textbook defamation or trade libel:

It asserts specific facts (“this company double-billed us,” “they expose client data,” “product broke and they refused refunds”).
Those facts are false.
They’re published to third parties (review platforms) and designed to damage your reputation, customer trust, SEO rankings, and revenue. (White & Case)

Courts and commentators have long recognized that fake online reviews can satisfy the elements of defamation and trade libel, particularly when they accuse a business of dishonesty or serious quality failures. (White & Case)

When you can plausibly link the reviews to a competitor-coordinated campaign—especially where the content repeats similar talking points, uses the same prompts or phrasing, or comes from obviously fabricated personas—that’s when defamation claims start to look much more compelling.

🏷️ Lanham Act and unfair competition: turning fake reviews into false advertising

If the source is a competitor, you don’t have to rely solely on defamation. You have federal false-advertising law.

Under Lanham Act § 43(a), a business can sue another business that, in commercial advertising or promotion, makes false or misleading statements of fact about its own or another’s goods or services, causing competitive injury. (DiTommaso Lubin, PC)

Courts have already applied this to manipulated review ecosystems:

In the Vitamins Online v. HeartWise/NatureWise litigation, the court found a supplement seller liable under the Lanham Act for manipulating Amazon reviews—including upvoting its positives and downvoting competitor reviews—to convey a false impression of product superiority. (Passle)

That case didn’t involve AI, but analytically it’s the same:

If a competitor uses AI to mass-generate five-star reviews for itself and one-star reviews for you, those reviews are part of its promotional strategy—and can be framed as false advertising/unfair competition.

In a demand letter, this lets you say:

“You’re not just defaming us; you’re engaging in a coordinated false advertising campaign in violation of federal law and state unfair competition statutes.”

That tends to get attention.

🏛️ FTC rules and platform policies: your “regulatory” leverage

FTC: fake reviews and AI are now explicit targets

The FTC’s Final Rule on Fake Reviews and Testimonials (effective October 21, 2024) does a few things that matter for your letters: (Federal Trade Commission)

Prohibits selling, purchasing, or disseminating consumer reviews or testimonials that are materially misleading – including reviews from nonexistent individuals or those without actual experience.
Allows civil penalties against knowing violators.
Squarely contemplates AI-generated fake reviews and bots as part of the problem set.

On top of that:

The updated Endorsement Guides warn against “procuring, suppressing, boosting, organizing, publishing, upvoting, or downvoting” reviews in ways that distort what consumers really think. (Federal Register)
The FTC has already brought enforcement against an AI testimonial generator and against Rytr, an AI-writing tool that enabled users to create fake product reviews. (ftcattorney.com)

You may never actually file an FTC complaint. But referencing these rules in your letter tells the competitor (and their counsel):

“This is not just a private dispute; you’ve wandered into civil-penalty territory.”

Platform terms: Google, Amazon, Yelp & friends

Major platforms have all been dragged into fake-review enforcement:

Amazon: has sued fake review brokers, seized domains, and publicly reports blocking hundreds of millions of fake reviews; it recently secured a major ruling against operators of 75+ review-selling websites. (About Amazon)
Google and Amazon both reached commitments with the UK Competition and Markets Authority (CMA) to intensify their efforts against fake reviews, under new UK powers that can impose fines directly. (theguardian.com)

These platforms’ terms typically:

ban reviews written in exchange for undisclosed compensation or incentives;
prohibit posting reviews for a competitor’s product to manipulate ratings;
allow suspension or termination of business accounts that coordinate fake or incentivized reviews.

So your enforcement toolkit includes:

letters to platforms with evidence of coordinated synthetic reviews, and
invoking the platforms’ own policies as an independent reason to act against your competitor.

🧪 Building the evidence pack before you send anything

Before you send a demand letter, you want your pattern clear. Things worth pulling together:

Screenshots and URLs of the worst reviews, with timestamps and star ratings.
Patterns in language: identical phrases, structure, or “AI telltales” across many different profiles.
Account details: reviewer handles that appear only once, newly created accounts, or clusters that review only your competitor and you.
Timing: sudden waves of reviews over a short window, aligned with a competitor’s launch, campaign, or dispute.
Any connections you can tie: reviewers linking back to a competitor’s domain, staff, or marketing accounts, or appearing in their own testimonials.

If you have server logs, email metadata, or marketplace intelligence linking the reviewers to a competitor or its agencies, that moves you from suspicion to allegation with teeth.

✉️ Anatomy of a synthetic-review demand letter

Think of your letter as doing three jobs at once:

Defamation notice – preserving claims and demanding removal / retraction.
Commercial false advertising notice – warning of Lanham Act and state unfair competition exposure.
Platform + regulatory framing – signaling that you’re ready to go to platforms and, if necessary, regulators.

🧱 Table – Core building blocks of your letter

Section	Purpose	What you actually say/do
Intro: who you are and what’s happening	Anchor the story.	Identify your company, industry, and the platforms affected (e.g., Google Business Profile, G2, Capterra, Amazon, Yelp). Briefly state that you’ve identified a pattern of clearly false reviews and testimonials targeting your business.
Fact pattern and examples	Show this is a campaign, not a one-off crank.	Summarize key facts: dates, platforms, volume, themes. Include a short table or appendix listing sample reviews (URL, date, username, star rating, short quote). Highlight ways in which they are demonstrably false (e.g., refer to non-existent transactions, misdescribe your pricing model, describe issues technically impossible with your product).
Attribution to competitor / agency	Tie it to the right defendant.	Without over-pleading, lay out the indicia that the campaign is tied to them: overlapping language with their marketing, reviewers linking to them, shared IP or contact details where you have them, or whistleblower information. Make clear you reserve the right to amend your understanding as you obtain more data.
Legal characterization: defamation and trade libel	Put them on notice.	Explain that the reviews contain false statements of fact about your business, designed to dissuade customers from using your services, and constitute defamation / trade libel under applicable law. You don’t need to brief the elements; you just need to be explicit that this is not just “negative feedback”—it is false factual advertising. (White & Case)
Legal characterization: Lanham Act / unfair competition	Elevate it to competitor misconduct.	If they’re a competitor, spell out that flooding review platforms with fabricated negatives about you (and/or fabricated positives about them) is false advertising and unfair competition under 15 U.S.C. § 1125(a) and parallel state-law doctrines. Note that courts have treated manipulated review ecosystems as actionable false advertising. (Passle)
Regulatory context (FTC + platform rules)	Turn up the heat.	Briefly cite the FTC’s Final Rule banning fake reviews—including AI-generated reviews and testimonials—and note that using AI tools to generate fictitious customer experiences may expose them and any vendors they use to civil penalties and enforcement. Also point out that their activities clearly violate review-platform policies and could lead to suspensions or bans. (Federal Trade Commission)
Demands (concrete, verifiable)	Give a clear cure path.	Common demands: (1) immediately cease creating, directing, or paying for fake reviews about your company; (2) identify all platforms where they or their vendors have posted or solicited reviews related to you or your products; (3) instruct their employees, agents, and vendors to request removal of the identified reviews; (4) preserve and, if appropriate, provide relevant logs and communications (instructions to agencies, invoices, prompts provided to AI review tools, etc.).
Preservation notice	Protect future discovery.	Demand that they preserve all documents and electronically stored information relating to reviews, AI tools, agencies, and instructions or prompts used in connection with reviews of your or their products. This includes emails, Slack/Teams messages, contracts with agencies, and account access logs.
Escalation & timeline	Make next steps predictable.	Give a short but reasonable window (e.g., 7–14 days) for them to confirm in writing what remediation steps they’ve taken. State that if they fail to do so, you’ll (1) submit detailed policy-violation reports to the relevant platforms; and (2) consider filing suit for defamation, false advertising, and unfair competition in a specific forum. No bluffing: name the statutes and the likely venue.

You can vary the tone—some clients want very “we’re still open to resolving this,” others want “this is your last warning”—but the structure stays roughly the same.

🌐 Parallel track: letters to platforms and, if necessary, regulators

In many cases, your fastest relief is not a court order; it’s platform enforcement.

For platforms, your notice should:

attach or link to the suspicious reviews;
explain why they’re inauthentic (patterns, impossibilities, connections to a competitor);
quote the platform’s own policies on fake or incentivized reviews; (Search Engine Land)
flag the FTC rule and any local consumer-protection rules, showing the platform that ignoring your report is not risk-free for them either.

For regulators:

An FTC complaint (or state AG complaint) is sometimes appropriate when there is a systemic pattern and significant consumer harm—not just your B2B pissing match.
You can reference their Operation “AI” enforcement actions—including the case against Rytr’s fake-review feature—as evidence that this is precisely the conduct they said they’d pursue. (Reuters)

You don’t have to threaten regulatory complaints in every demand letter. But knowing you could is valuable leverage.

❓ Frequently asked questions: synthetic reviews and legal strategy

Are AI-written reviews always illegal?

No. AI-assisted reviews can be legal if:

they reflect a real customer’s actual experience, and
any material connections (payment, free product, affiliate status) are properly disclosed as endorsements. (Federal Register)

The problem is when competitors use AI to:

impersonate customers who never existed, or
describe experiences with your business that never happened.

That’s where defamation, false advertising, and FTC rules all converge.

Do I have to prove exactly which AI tool they used?

No. It can help, but it’s not required.

Your claims don’t turn on whether they used “Tool X” or “Tool Y”; they turn on the false statements themselves and the competitive scheme. The AI angle matters because:

it explains the volume and similarity of the reviews;
it ties into contemporaneous FTC enforcement against AI-enabled fake review services; (ftcattorney.com)
it helps you argue this is deliberate systematized misconduct, not random angry customers.

Can I sue the review platform itself?

Usually that’s a tough path:

In the U.S., platforms are often shielded by Section 230 from liability for user-generated content, including reviews, so you typically focus on (1) the competitor, and (2) getting the platform to enforce its own rules.
Non-U.S. jurisdictions vary; some place more direct duties on platforms once they are notified.

Practically, your first goal with platforms is removal and account enforcement, not damages.

What if I can’t prove it’s my competitor behind the reviews?

You still have options:

If individual reviewers can be identified (names, domains, LinkedIn), you can treat them as defendants in defamation/trade libel if the harm is severe enough.
For lower-level harm, a platform-report approach may be more cost-effective: highlight the pattern, let the platform’s fraud team and algorithms do the heavy lifting. (Thrive Internet Marketing Agency)

But if you can’t tie the campaign to anyone with resources, a full lawsuit may not pencil out. In those cases, good documentation + platform enforcement is often the realistic ceiling.

🧭 Big picture for SaaS platforms and B2B businesses

Generative tools have made it trivial for bad actors to manufacture consensus at scale. The law hasn’t fully caught up, but the pieces are already there:

Defamation and trade libel when synthetic reviews falsely accuse you of serious quality failures, dishonesty, or regulatory violations. (White & Case)
Lanham Act and unfair competition when a competitor uses those reviews as a de facto ad campaign. (Passle)
FTC rules and platform terms that now explicitly call out fake and AI-generated reviews as prohibited practices. (Federal Trade Commission)

The practical work is in:

assembling a clean evidentiary record,
drafting tight, fact-driven demand letters to the right targets, and
using platform and regulatory channels in parallel where that makes sense.

If you’re dealing with an AI-driven fake review campaign and want a letter that does more than shout into the void, this is exactly the kind of matter where a carefully structured defamation + Lanham Act + platform-policy approach can make a real difference.