50 Founders. 43 Had AI Errors. Average Cost: €28,000 Each.
Last week, we ran an experiment.
We asked 50 SaaS founders to do one thing: check if AI answers about their brand were accurate.
Not visibility. Not rankings. Just: "Is what ChatGPT, Perplexity, and Gemini say about you actually true?"
The results scared me.
📊 The Dataset
Metric | Value |
|---|---|
Founders surveyed | 50 |
Companies represented | 50 |
Stages | Pre-revenue to €20M ARR |
Industries | SaaS (B2B, B2C, enterprise, SMB) |
Platforms checked | ChatGPT, Perplexity, Gemini, Claude |
🚨 The Results
Finding | Value |
|---|---|
Founders who found at least one error | 43 out of 50 (86%) |
Total errors discovered | 147 |
Average errors per founder | 3.4 |
Founders who found zero errors | 7 (14%) |
Founders who found pricing errors | 18 (36%) |
Founders who found feature errors | 14 (28%) |
Founders who found narrative errors | 11 (22%) |
86% of founders had wrong information about their brand circulating in AI.
And they had no idea until they looked.
💰 The Money
We asked each founder to estimate the business impact of each error.
Error Type | Average Estimated Cost |
|---|---|
Pricing error (showing higher than actual) | €47,000 |
Pricing error (showing lower than actual) | €23,000 |
Missing critical feature | €38,000 |
Wrong market positioning | €52,000 |
Outdated company narrative | €31,000 |
Competitor credited for your work | €44,000 |
Total estimated losses across 50 founders: €1,204,000
Average per founder: €28,000
Per error average: €18,500
These are not hypotheticals. These are founders calculating actual lost deals, longer sales cycles, and confused prospects.
📖 The Stories (Anonymized)
Founder #12 (B2B SaaS, €4M ARR)
"ChatGPT said our SOC2 compliance was 'in progress.' We've had it for 14 months. We lost at least two enterprise deals to this. One prospect literally told us 'we checked and you're not SOC2 yet.' I couldn't argue — they trusted AI over us."
Estimated loss: €180,000
Founder #27 (Dev tool, €1.2M ARR)
"Perplexity claimed our free tier had a 1,000-row limit. It's 10,000. Has been for 8 months. We found 27 support tickets from users who hit 1,000 rows and bounced — they never even reached our actual limit."
Estimated loss: €45,000
Founder #31 (AI productivity, pre-revenue)
"Gemini described us as 'another generic AI wrapper.' We have 3 patents pending and a completely novel architecture. An investor told me they 'checked around' and got that impression. We didn't get the meeting."
Estimated loss: Unknown (potential seed round)
Founder #44 (Enterprise SaaS, €12M ARR)
"ChatGPT positioned us as 'best for mid-market.' Our entire go-to-market is enterprise. We've spent 18 months moving upmarket. AI still thinks we're a small business tool. Our enterprise leads are confused before we even talk to them."
Estimated loss: €300,000+
Founder #8 (E-commerce platform, €2.5M ARR)
"Our pricing changed 9 months ago. ChatGPT still shows the old prices. We've had at least 50 support tickets asking about 'why prices went up' — they didn't. AI just never updated."
Estimated loss: €65,000
📉 The Decay Problem
We tracked how long these errors had been active.
Time Active | % of Errors |
|---|---|
< 3 months | 18% |
3–6 months | 34% |
6–12 months | 29% |
> 12 months | 19% |
The oldest error we found: 27 months.
A feature that hadn't existed for over two years was still being cited as a current differentiator.
🔍 Where Errors Came From
Source | % of Errors |
|---|---|
Founder's own outdated content | 41% |
Old Reddit threads | 19% |
Competitor comparison pages | 16% |
Tech press articles (2022–2024) | 12% |
G2/Capterra reviews | 8% |
Industry forums | 4% |
41% were fixable by updating the founder's own site.
59% came from sources founders couldn't touch directly.
✅ What Founders Did Next
We followed up after 30 days.
Action Taken | % of Founders |
|---|---|
Updated their own site content | 78% |
Added FAQ schema | 64% |
Created comparison pages | 57% |
Published fresh content | 52% |
Set up ongoing monitoring | 41% |
Did nothing | 12% |
The ones who acted saw results:
Metric | Improvement |
|---|---|
Error rate reduction (30 days) | 34% |
Citation increase (30 days) | 28% |
Support tickets related to errors | -41% |
🛠️ How We're Solving This at Rankfender
This experiment is exactly why we built what we built.
RAIVE tracks your brand across 7+ AI systems daily. You don't have to manually search anymore. You just open a dashboard.
RCGE v2.2 (launching next week on Product Hunt) proofreads your content against your Brand Book before it goes live — catching inconsistencies before they become AI errors.
ROSE v1.0 (late April) scans your entire site, finds every page where key topics appear, and ensures they all tell the same truth.
The goal: Turn 86% error rates into 0%.
🎁 The Offer
I want to run this experiment again — but with your brand.
Next 20 founders who comment or DM me:
Your domain
Your top 3 keywords
I'll personally run a full AI accuracy audit and send you:
Every mention across ChatGPT, Perplexity, Gemini
Every error (pricing, features, narrative)
Estimated impact based on your business
A prioritized fix list
No card. No commitment. Just data.
👇 Your Turn
Three questions:
When did you last check what AI says about your brand?
If you found an error, what would it cost you?
What's stopping you from looking?
Drop a comment. I read every single one.
Imed Radhouani
Founder & CTO – Rankfender
Helping founders stop losing money to AI errors
Replies