Strategic Platform Integration

The first big decision came fast: do we build GEO as a standalone platform, or integrate it into Writesonic’s existing ecosystem? 

Given the millions of active users already on the platform, the answer was clear. Integration would give us immediate reach and save users from adopting yet another tool. 

We further simplified the navigation, adding GEO to the sidebar with a “New” tag, so users could easily spot it. When clicked, the sidebar expanded to reveal the full GEO menu without disrupting the rest of the flow. 

Competitive Intelligence Foundation

Here’s where things got a little more interesting. We were starting from scratch, having no references or competitors to benchmark/guide us. 

We started by identifying the AI platforms businesses care about: ChatGPT, Claude, Gemini, and Perplexity. These became our foundation for tracking mentions and rankings. 

The key insight? If a business isn’t recommended by ChatGPT for “best UX design agencies,” they need to know why. 

We created the tracking infrastructure to capture this crucial data across all major platforms. 

Visibility Score Development

We knew businesses needed one key metric to measure their AI presence, so we created the visibility score. We factored in more than just mentions, including metrics like rank, position and performance trends among others. 

The dashboard was created to help track your visibility against competitors, so you could quickly see if you’re making progress. 

We also included industry benchmarks to help businesses understand their standing within their specific niche/category. 

Competitive Benchmarking Engine

Knowing your own performance is just half the equation. Businesses need to see how they compare to competitors. We built real-time competitor analysis that tracks market share across various queries. 

The share of voice feature was also created to help you understand how often your brand was mentioned compared to competitors. 

For example, if you were in the project management space, you’d know if Asana, Notion or even Monday.com dominated AI recommendations for your niche. 

Brand Sentiment Analysis

AI models don’t just mention brands, they evaluate them. We added sentiment tracking across platforms, categorizing mentions into sentiments like: 

• Positive 

• Neutral 

• Negative

What we found interesting was the sentiment differences between platforms. ChatGPT might speak positively of your brand, while Claude might be neutral. 

The idea was to help businesses understand this, and adjust their strategies accordingly. 

Brand Perception Mapping

We took sentiment analysis a step further by mapping out how each AI platform perceives your brand. We then created a tool that shows exactly how ChatGPT, Claude, Gemini, and Perplexity describe your business. 

The results were interesting, with one LLM model seeing you as a “productivity tool”, while the other might consider you a “project management software.” 

The idea was to effectively communicate how these differences impact how & when you get recommended. 

Advanced Filtering System 

Data without context is noise. To fix that, we built a robust filtering system for date ranges, AI models, and geographic regions. Using this component, users could compare how their brand performed in the US versus Europe, or check how you fare on ChatGPT compared to Claude. 

The region filter turned out to be crucial, since AI recommendations vary by geography. A brand that leads in North America might struggle in Asia. 

These insights could help businesses know exactly where to focus their optimization efforts.

Adding AI Crawler Analytics

This is where we got really technical, diving deeper into analyzing AI behavior. The AI crawler analytics feature could help you figure things like: 

• When ChatGPT mentions your website, does it actually visit? 

• Do those clicks turn into conversions? 

Our dual-traffic analysis feature further helped separate AI bot visits from human clicks, helping businesses see the full funnel from brand mentions to conversions.  

User Education Framework

GEO is complex. Most businesses had never heard of it before we built this platform. We couldn't just dump data on users and expect them to understand it, a focus on user education was required simultaneously. 

We created custom illustrations that explain technical concepts visually. What's the difference between AI visits and human visits? Our diagrams made it clear. How does click-through rate work in the AI context? 

We show it step by step: 

• The progressive disclosure approach means users get the information they need when they need it. 

• Beginners see simplified views. 

• Power users can dive into advanced analytics.

  
  

Market-First Launch Strategy

Writesonic had millions of users. We had a first-to-market product. The combination was powerful. Instead of starting from zero, we could introduce GEO tracking to an established audience that already trusted the platform.

The launch strategy focused on education and early adoption, with Writesonic positioned as the pioneer in GEO analytics. 

Conclusion 

When competitors eventually entered the market, Writesonic already had the data, the users, and the reputation of being a category leader.

The result? Writesonic became the go-to platform for businesses wanting to understand and optimize their AI presence. 

With our solution, they captured new markets while strengthening their relationship with existing users.