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Faro Index
See what AI gets wrong about your brand. Free 90-sec scan.
I am not an engineer, which explains why this tool exists. My first Faro Index scan returned a dismal 43 percent accuracy; AI confused us with a 3D measurement firm. The problem is clear: AI confidently feeds buyers false claims, and visibility does not guarantee accuracy. Using Claude Code and Cursor, I built a platform to score AI visibility, factual truth, and GEO readiness. Benchmarking 244 companies proved name ambiguity drives errors. I am happy to discuss the methodology or build.
Hi everyone, I am Pooja, the creator of Faro Index. I am not an engineer, and I want to lead with this fact because it is deeply relevant to why this product exists and how it was ultimately built.
When the platform was finally ready to test, the very first scan I ran was on my own company. The results were alarming: ChatGPT described Faro Index as "a financial metric or tool used to evaluate a company's market presence," while Claude called it "a data and analytics company focused on supplier/vendor risk management." Gemini stated it was not a widely recognized term. Only Perplexity came close to understanding our actual product. My initial brand accuracy rate came back at a dismal 43 percent. Most of this confusion traced directly back to FARO Technologies, an older 3D measurement company sharing our first name.
This highlights the core problem we face today. Artificial intelligence models are making confident, highly specific factual claims to buyers who are actively evaluating vendors; however, the companies being described have zero visibility into what is actually being said. Our benchmark data proves that visibility and accuracy are nearly uncorrelated. Being mentioned frequently does not protect your brand from being described incorrectly.
I started building in February using Claude Code for the Python backend and Cursor for the Next.js frontend, despite having never shipped a production API before. Today, the platform measures AI visibility across four major models, scores factual accuracy directly against your website, audits your overall AI readiness with a custom GEO score, and detects content leakage where platforms use your positioning without citing you. Our benchmark covered 244 companies across 11 industries, and the patterns remained remarkably consistent. Name ambiguity serves as the primary driver of low accuracy; missing structured data compounds the issue. Shockingly, the worst-performing companies by accuracy are often among the most frequently cited.
Once we identified these hallucination issues in our own scan, we fixed them using strict Organization schema, targeted disambiguation copy on the homepage, and specific FAQPage schema on the pricing page. Perplexity updated its answers within 48 hours, although ChatGPT took significantly longer. This exact timeline aligns with the broader benchmark data we have collected.
I am very happy to answer any questions about the methodology, the benchmark dataset, or the experience of building a SaaS product as a non-engineer using AI coding tools.
About Faro Index on Product Hunt
“See what AI gets wrong about your brand. Free 90-sec scan.”
Faro Index was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #122 on the daily leaderboard. I am not an engineer, which explains why this tool exists. My first Faro Index scan returned a dismal 43 percent accuracy; AI confused us with a 3D measurement firm. The problem is clear: AI confidently feeds buyers false claims, and visibility does not guarantee accuracy. Using Claude Code and Cursor, I built a platform to score AI visibility, factual truth, and GEO readiness. Benchmarking 244 companies proved name ambiguity drives errors. I am happy to discuss the methodology or build.
On the analytics side, Faro Index competes within SEO, SaaS and Artificial Intelligence — topics that collectively have 551.3k followers on Product Hunt. The dashboard above tracks how Faro Index performed against the three products that launched closest to it on the same day.
Who hunted Faro Index?
Faro Index was hunted by Pooja Muhuri. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Faro Index including community comment highlights and product details, visit the product overview.
Pooja Muhuri
Hi everyone, I am Pooja, the creator of Faro Index. I am not an engineer, and I want to lead with this fact because it is deeply relevant to why this product exists and how it was ultimately built.
When the platform was finally ready to test, the very first scan I ran was on my own company. The results were alarming: ChatGPT described Faro Index as "a financial metric or tool used to evaluate a company's market presence," while Claude called it "a data and analytics company focused on supplier/vendor risk management." Gemini stated it was not a widely recognized term. Only Perplexity came close to understanding our actual product. My initial brand accuracy rate came back at a dismal 43 percent. Most of this confusion traced directly back to FARO Technologies, an older 3D measurement company sharing our first name.
This highlights the core problem we face today. Artificial intelligence models are making confident, highly specific factual claims to buyers who are actively evaluating vendors; however, the companies being described have zero visibility into what is actually being said. Our benchmark data proves that visibility and accuracy are nearly uncorrelated. Being mentioned frequently does not protect your brand from being described incorrectly.
I started building in February using Claude Code for the Python backend and Cursor for the Next.js frontend, despite having never shipped a production API before. Today, the platform measures AI visibility across four major models, scores factual accuracy directly against your website, audits your overall AI readiness with a custom GEO score, and detects content leakage where platforms use your positioning without citing you. Our benchmark covered 244 companies across 11 industries, and the patterns remained remarkably consistent. Name ambiguity serves as the primary driver of low accuracy; missing structured data compounds the issue. Shockingly, the worst-performing companies by accuracy are often among the most frequently cited.
Once we identified these hallucination issues in our own scan, we fixed them using strict Organization schema, targeted disambiguation copy on the homepage, and specific FAQPage schema on the pricing page. Perplexity updated its answers within 48 hours, although ChatGPT took significantly longer. This exact timeline aligns with the broader benchmark data we have collected.
I am very happy to answer any questions about the methodology, the benchmark dataset, or the experience of building a SaaS product as a non-engineer using AI coding tools.