Gemini Is Making Up Things About Your Business — And There Is No Way to Fix It

By Notice Me Senpai Editorial

An ecommerce store owner posted on Reddit's r/ecommerce over the weekend with a problem that doesn't have a solution yet. Google's Gemini has been telling potential customers their business is involved in lawsuits and product safety violations. None of it is true. The hallucinations were specific enough that a customer raised the issue directly.

There's no correction form. No dispute process. No "report brand misinformation" button anywhere in Google's ecosystem. And 35% of brands now report that AI chatbot hallucinations have damaged their reputation.

Thirty-five percent of brands have already been damaged by AI hallucinations. The other sixty-five percent just haven't checked yet.

Google has no brand correction mechanism. That's the actual problem.

If Google Maps listed your restaurant as permanently closed, you'd file a correction and it would be fixed within days. Google has spent years building dispute resolution for its search products. But Gemini, which reaches 750 million monthly users, has none of that infrastructure for brand accuracy.

The store owner who posted on Reddit tried the obvious things. Reported the issue through Google's general feedback channels. Nothing happened. And honestly, it's hard to even know what "fixing" would look like here, because the hallucination isn't a static listing. It's generated fresh each time someone asks. The wrong answer yesterday might be a different wrong answer tomorrow.

This isn't unique to Google. Every major LLM hallucinates about brands. But Gemini's hallucinations carry more weight because they're attached to Google Search, which means they have distribution that other AI tools don't. When Perplexity makes something up about your brand, a relatively small audience sees it. When Gemini does it, the answer potentially surfaces inside Google's own search experience.

From what I've seen working with brands on this, the reaction cycle is pretty consistent. First: surprise that AI is saying anything about them at all. Second: frustration at the inaccuracies. Third: the slow realization that there's no established process to fix any of it.

Reddit is writing your brand story for the AI

Here's the part that caught me off guard when I first dug into the data. LLMs pull over 60% of their brand information from Reddit threads and editorial sites. Not from your corporate website. Not from your press page. Not from the About section your team spent three weeks getting right.

Your meticulously controlled brand narrative? The AI mostly skips it.

Instead, it's pulling from a Reddit thread where someone complained about your shipping times two years ago, or from a mid-tier blog post that got your founding date wrong. And because LLMs present information with confidence regardless of source quality, the hallucinated version gets delivered with the same authoritative tone as verified facts.

This creates a specific kind of problem for marketers. Traditional reputation monitoring tools track social media mentions, press coverage, and review sites. They don't track what AI chatbots are saying about you. A whole category of "AI brand monitoring" is emerging to fill this gap, but most brands haven't adopted any of it yet.

In one account we advise, we ran a brand audit across four major AI tools last month and found incorrect founding dates, phantom product lines, and a fabricated controversy. All served with full confidence to anyone who asked. The brand had no idea until we looked.

A 20-minute audit that tells you more than you'd expect

The practical first step is straightforward. Open ChatGPT, Gemini, Claude, and Perplexity. Search your brand name plus your top three products. Ask each one: "What do you know about [brand name]?" and "Has [brand name] been involved in any controversies?"

Screenshot every wrong answer. You'll want documentation if you need to escalate, or if the legal landscape shifts. And it probably will.

Then look at your Schema markup. Structured data on your website increases AI citation accuracy by roughly 3x, according to recent audits. That's not because Schema is a silver bullet. It's because LLMs are slightly more likely to pull from structured, machine-readable data when it's available. It's the fastest fix that currently exists, not because it's great, but because the alternative is hoping the AI figures it out on its own.

Specifically: implement Organization schema with your founding date, headquarters, key people, and official social profiles. Product schema on your product pages. FAQ schema where it's relevant. These are the fields AI models are most likely to reference accurately when the markup exists.

And while you're auditing, check what Reddit actually says about your brand. Remember, that's where most of the AI's information is coming from. If there are inaccurate threads ranking well, you can't delete them, but you can participate in the conversation with corrections. The AI will eventually ingest those too.

The traffic shift that changes why this matters

Google Gemini now sends 29% more traffic to websites than Perplexity, according to SE Ranking's analysis of over 101,000 sites. In the U.S., the gap is 41%.

What makes this interesting: in August 2025, Perplexity was sending roughly three times more traffic than Gemini. That's a 115% reversal in about two months, correlating with Google's rollout of the Gemini 3 model family. ChatGPT still dominates AI referral traffic at around 80% of the total, and combined AI traffic only represents about 0.24% of global web traffic. So the numbers are still small in absolute terms.

But the trajectory suggests that if you've been thinking about AI referral traffic at all, you've probably been thinking about the wrong platform. Check your GA4 referral sources for gemini.google.com. If it's not there yet, it probably will be soon.

This connects back to the brand reputation issue directly. The more traffic AI platforms send to your site, the more your brand's AI-generated description functions as a first impression for potential customers. Getting that description right isn't an abstract concern. It's becoming a traffic quality issue. Even traditional metrics like email engagement are shifting underneath marketers for similar structural reasons. The signals we relied on are quietly becoming less reliable.

Why waiting for Google to fix this is probably a mistake

Activist Robby Starbuck sued Google in late 2025 over fabricated claims Gemini generated about him. Analysts expect at least two more AI defamation lawsuits to land in 2026. But even if those cases succeed, policy changes will take years to filter down to something that resembles a usable brand correction mechanism.

In the meantime, the combination that matters is: audit what AI says about you now, implement structured data to improve accuracy, and start monitoring the sources AI actually reads with the same rigor you apply to your review profiles.

Nobody designed this situation. Google didn't set out to hallucinate about your brand. But the gap between "AI is confidently describing your business to millions of people" and "you have any mechanism to ensure accuracy" is real, it's wide, and it's not closing on its own.

Your corporate site may be the most accurate source of information about your brand. It's also, increasingly, the last place AI looks.