---
title: "Why ChatGPT can't find your business (fix in 3 steps)"
description: "5 source-backed AI claims from Why ChatGPT can't find your business (fix in 3 steps), with key statistics, context, and the original AI Adopters Club source."
url: "https://kbanc.com/claims-library/why-chatgpt-cant-find-your-business-fix-in-3-steps"
source: "https://aiadopters.club/p/use-your-faq-to-get-found-by-ai-search"
date: "2025-06-06"
topics: ["strategy", "implementation", "tools"]
generated: "2026-08-31"
---

# Why ChatGPT can't find your business (fix in 3 steps)

By Kamil Banc | June 6, 2025

## Claims

1. **Winner-Takes-All AI Citations** (source summary): AI search platforms prioritize trust signals over traditional SEO metrics, typically citing only two to three sources.
2. **NotebookLM Processing Capacity** (source summary): NotebookLM can process up to 50 sources and 25 million words for customer feedback analysis.
3. **AI Search Traffic Growth** (source summary): Some websites have seen 67% increases in AI search referrals and doubled conversion rates.
4. **Free Google AI Tools** (source summary): Gemini 2.5 Pro and NotebookLM are both free with a Google account.
5. **Schema Markup Requirements** (source summary): FAQPage JSON-LD schema markup should include dateModified, author information, and keyword arrays for freshness signals.

## Evidence

### Quote
> "AI search platforms prioritize 'Trust Signals', comprehensive information, authority signals, and usefulness over traditional SEO metrics, with only 2-3 sources typically getting cited in a 'winner-takes-all' scenario." - Kamil Banc

### Key Statistics
- **67% increase in AI search referrals**: Some sites have seen this growth in traffic from AI search tools, alongside doubled conversion rates.
- **2-3 sources cited**: AI search platforms typically cite only this small number of sources per query, creating a winner-takes-all citation dynamic.
- **50 sources and 25 million words**: NotebookLM's maximum processing capacity, making it suitable for comprehensive customer feedback analysis from support tickets and reviews.

## Context
The article presents a three-level progressive workflow that converts raw customer pain points from support tickets, reviews, and chat logs into structured FAQs optimized for AI search discovery. The methodology relies entirely on Google's free ecosystem—NotebookLM for ingesting and analyzing customer feedback at scale, and Gemini 2.5 Pro for refining, expanding, and structuring the output. Level 1 focuses on basic FAQ transformation, Level 2 adds search intent research and topic clustering, and Level 3 implements technical elements like FAQPage JSON-LD schema markup and citation optimization. The author's core thesis is that optimizing for Google's AI search using Google's own tools means 'speaking their language from the inside,' since the same ecosystem that generates the content also evaluates and cites it. Practitioners can apply this immediately with a Google account, using the eleven provided prompts as templates for each stage of the workflow.

## Source
- Original: [Why ChatGPT can't find your business (fix in 3 steps)](https://aiadopters.club/p/use-your-faq-to-get-found-by-ai-search)
- Cite: kbanc.com/claims-library/why-chatgpt-cant-find-your-business-fix-in-3-steps

## Primary Evidence
- [NotebookLM](https://notebooklm.google/) (notebooklm.google; supports claims 2, 4)