AI Search is Creating a New Problem

Over the last few months, I started noticing that more people are searching directly on AI platforms instead of traditional search engines.

People are asking ChatGPT, Claude, Gemini and Perplexity things like:

• Best AI automation tool
• Best CRM for agencies
• Best email outreach software
• Best SEO tool for startups

The problem is that most businesses have absolutely no visibility into how often AI systems mention their brand.

Traditional SEO tools still focus heavily on:

• Rankings
• Backlinks
• Organic traffic
• Keyword positions

But AI-generated answers work differently.

These systems decide which brands get recommended, which websites get cited and which businesses become part of the conversation.

And currently, most businesses are completely blind to that layer of visibility.

That was the main reason I built this AI Visibility Tracker.

What This AI Visibility Tracker Does

The workflow automatically tracks brand mentions across:

• ChatGPT
• Claude
• Gemini
• Perplexity
• Google AI Overviews

Instead of manually checking every AI platform one by one, the system automatically runs weekly and collects all visibility data into a centralized dashboard.

The tracker monitors:

1. Brand mentions
2. Competitor visibility
3. Share of voice
4. Citation frequency
5. Top cited URLs
6. Weak performing queries
7. Weekly visibility trends

I built the entire workflow using n8n, APIFY and Google Sheets.

Why I Built This Instead of Using Enterprise Tools

When I started researching AI visibility platforms, most enterprise solutions were charging somewhere around $500/month.

For agencies or large SaaS companies that pricing may be fine.

But for creators, marketers, indie hackers and small businesses, that pricing becomes very difficult to justify.

So I wanted to build a much more affordable alternative that still provides practical insights.

Right now the entire workflow costs me around $6/month when configured for weekly runs.

How the Workflow Works

Step 1: Weekly Automation Trigger

The workflow starts with an n8n Schedule Trigger.

I intentionally configured it to run weekly instead of daily because AI visibility trends usually do not change aggressively every 24 hours.

Weekly tracking gives much cleaner trend analysis while also saving API credits.

Step 2: Brand & Competitor Setup

Inside the Set Node, I configure:

• Brand name
• Domain
• Search queries
• Competitors
• Competitor domains

This step is actually very important because poor competitor configuration leads to inaccurate visibility tracking.

Step 3: AI Visibility Data Collection

For collecting AI visibility data, I used this APIFY actor.

The actor helps fetch ranking and mention data across multiple AI platforms automatically.

Then I connected it with n8n using HTTP Request nodes and bearer token authentication.

Step 4: Data Transformation

Once the API response is received, the workflow processes all the raw JSON data using JavaScript Code Nodes inside n8n.

This transformation step converts raw API data into structured visibility metrics that can easily be analysed inside Google Sheets.

Step 5: Dashboard Automation

Finally, the processed data is automatically appended into Google Sheets where the dashboard visualizes:

• Mention rates
• Citation performance
• Competitor comparison
• Share of voice
• Weekly visibility growth

I also shared the Google Sheets dashboard publicly for anyone who wants to replicate the setup.

How This Solves a Real Problem

One thing I realized while building this is that manual AI visibility tracking is basically impossible at scale.

You cannot realistically:

• Open 5 AI platforms manually
• Test dozens of prompts weekly
• Compare competitors
• Track visibility changes
• Document citations
• Analyze trends over time

Doing that manually wastes a massive amount of time.

This automation converts the entire process into a mostly hands-free workflow.

Once configured properly, the system automatically generates visibility insights every single week without requiring manual monitoring.

Why I Used n8n

I chose n8n mainly because:

• It gives full workflow flexibility
• Easy API integrations
• Supports JavaScript transformations
• Self-hosting is affordable
• Highly customizable automation logic

For anyone interested in rebuilding or modifying the workflow, I shared the full n8n template.

Full Video Tutorial

I also created a complete step-by-step video tutorial showing:

• Workflow setup
• APIFY integration
• Dashboard creation
• Authentication setup
• Google Sheets automation
• Common mistakes
• Optimization tips

Things That Broke During Testing

A few things caused problems initially.

JSON formatting errors were the biggest issue. Even a single misplaced comma inside arrays for competitors or queries can break parts of the workflow.

Authentication setup also caused problems initially until the bearer token configuration was properly configured.

Another important lesson was avoiding daily runs. Weekly automation gives better long-term visibility analysis and prevents unnecessary credit usage.

Why I Think AI Visibility Tracking Will Become Important

Most businesses are still focused entirely on Google rankings.

But AI systems are increasingly becoming recommendation engines.

People are now directly asking AI:

• what tool should I use
• which software is best
• which platform is trusted
• which service should I choose

If your brand never appears inside those answers, you are potentially invisible to a growing category of future traffic and customer discovery.

That is why I think AI visibility tracking will eventually become as important as traditional SEO reporting.

Interested to know how others here are approaching this problem. Are you already tracking AI visibility for your brand or clients?

And which AI platform do you think currently has the strongest influence on buying decisions?