ThirdEye, AI Visibility Product Design
Designing a product that helps brands understand and improve their visibility across AI search platforms like ChatGPT, Perplexity, and Gemini.
Role
Product Thinking, & Design
Type
Product Systems
Year
2026
Context
Search behavior is changing. For years, brands relied almost entirely on search engines to drive organic traffic, SEO teams optimized websites to rank higher on Google and other platforms.
But a shift has started happening. Instead of browsing multiple links, users are increasingly asking questions directly to AI assistants like ChatGPT and Perplexity. This changes how discovery works. Even brands that rank well in traditional search results may not appear inside AI generated answers.
ThirdEye was built to address this new gap. It is a web application that tracks brand visibility across AI search platforms, audits how AI models interpret brand content, and generates recommendations that make websites more AI-crawler friendly.
The product is designed for startup founders, brand owners, and agency teams who care about their long-term visibility and reputation online.
The Challenge
When work on the product started, the idea was clear but the product itself had not taken shape yet. The founder had identified a real opportunity — brands were still focusing on SERP rankings while users were shifting toward AI tools.
But the product direction was still undefined. There was no clear UX structure, no information architecture, and it was difficult to explain the value of the product to potential users. The product was essentially at the idea stage.
Core problems to solve
- No defined product positioning or value proposition
- No UX structure or information architecture
- Early market, user expectations not fully defined
- Difficult to explain what the product actually does
- Needed to define what the product should measure
My Role
I worked on ThirdEye from ideation to production. My role covered product thinking, product architecture, UX design, and overall product direction.
Before designing the interface, I studied products in adjacent categories — SEMrush, Ahrefs, Jasper, Writesonic, Profound, and other SEO and AI content tools — to understand how these tools measure visibility and how users interpret that data.
I worked closely with the tech team and designer Gautam to design the product in Figma and build the first MVP. After launch, we collected feedback from sales calls and early product conversations to refine direction.
Market Research
Studied adjacent tools and user expectations
UX flow
Defined product structure, flows, and modules
Design
Full product design in Figma with the team
MVP & Iteration
Launched, gathered feedback, refined
Approach
If brands want to improve their visibility across AI platforms, they need to understand three things: which prompts and topics influence AI responses, how their brand currently appears in those responses, and what content changes could improve that visibility.
The three fundamental questions
- Which prompts and topics influence AI responses in my category?
- How does my brand currently appear inside those AI responses?
- What content changes would improve my visibility in AI answers?
From this thinking, the product began to take shape around two main pillars — a research system and a content system. This structure made the product significantly more actionable. Instead of only reporting data, it could help users actively improve their visibility.
Solution
The product evolved into two main modules
- A research system
- Content system
Monitoring & Research System
Helps users discover prompts, topics, and keywords that influence AI responses. Instead of tracking prompts individually, the system groups them into clusters so users can understand patterns in how AI models respond to certain topics.
Content System
Focuses on content creation and optimization for AI-crawler-friendly output. Includes four interconnected tools that help users generate content aligned with discovered prompt clusters.
Key Decisions
Several strategic decisions shaped the final product direction and made it meaningfully different from what was originally conceived.
Repositioning the Product
Initially positioned as an AI prompt tracking tool. I shifted the positioning toward broader AI visibility — making the value proposition easier to explain and more meaningful for users.
Prompt & Topic Clusters
Tracking individual prompts creates fragmented insights. Clusters allow users to see patterns in how AI conversations are structured — making the research module significantly more useful.
Visibility Audit
Introduced an audit blending signals from traditional SEO, SERP performance, and AI readiness — helping brands understand where they stand and what actions to take next.
Outcome
Idea → MVP
From raw idea to a structured, shippable product with clear architecture
Core Value Proposition
Core product modules, Research and Content, with a closed feedback loop
Repositioned
The biggest outcome was clarity. Before the product work began, the idea existed but the product lacked structure and direction. Through research, product thinking, and design, we defined a clear product architecture and a stronger positioning.
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