AI Brand Visibility
Track Your Brand Across AI Responses
The goal was to create a system that transforms scattered AI visibility into a measurable, actionable, and strategic advantage.
Problem Statement
- Brands lose control over how they are represented
- Visibility becomes unpredictable across AI platforms
- Traditional SEO tools fail to capture AI-driven discovery
Many businesses cannot track how AI systems describe or recommend them, leading to missed opportunities and reputational risks.
Project Goals
- Monitor how AI platforms represent them
- Identify gaps, risks, and inaccuracies
- Predict the impact of improvements
- Automate corrective actions
The product needed to feel intelligent, data-driven, and trustworthy—not overwhelming.
My Role
UX Research
Product Thinking & Strategy
Wireframing & Prototype
Design System
UI & Interaction Design
Design Process
User Research & Competitive Analysis
To understand the problem space, I started by exploring how businesses currently track their digital presence and where those methods fall short in an AI-driven environment.
I focused on three primary user groups:
- Marketing teams responsible for brand visibility
- Product managers tracking growth and engagement
- Founders making strategic decisions
Through discussions and behavior analysis, a few consistent patterns emerged:
- Users rely heavily on traditional SEO tools, which don’t reflect AI-generated visibility
- There is little clarity on how brands are represented in AI responses
- Decision-making is often based on incomplete or delayed insights
Many users expressed uncertainty—not just about performance, but about what to measure in the first place.
UI/UX Design Implementation
The design approach focused on translating a complex and abstract concept into a clear, usable experience.
Design Strategy
Instead of overwhelming users with raw data, I structured the interface around a simple flow:
Understand → Evaluate → Act
Each screen answers three questions:
- What is happening?
- Why does it matter?
- What should I do next?
Usability Testing & Iteration
Once the initial designs were ready, I tested them with users to validate assumptions and uncover usability gaps.
Testing Approach
I conducted task-based testing, asking users to:
- Identify their brand’s visibility status
- Interpret AI-generated mentions
- Take action based on insights
This helped evaluate both clarity and decision-making flow.
Iterations Made
Based on feedback, I refined the design:
- Simplified data presentation to highlight only critical insights
- Improved visual hierarchy to guide attention
- Made actions more prominent and easier to access
- Added contextual hints to reduce confusion
These changes significantly improved usability and reduced the time needed to complete tasks.
Solution Approach
Unified Visibility Dashboard
- Brand visibility trends
- AI-generated mentions
- Competitive positioning
- Risk indicators
The interface avoids clutter and focuses on clarity, helping decision-makers act quickly.
Multi-AI Monitoring System
The platform tracks how brands appear across multiple AI ecosystems, instead of relying on a single source.
This ensures:
- Broader visibility insights
- Consistent brand representation
- Early detection of issues
Predictive Insights
- What to fix
- Expected impact of changes
- Priority actions
This transforms the product from a tool into a decision-making assistant.
Automated Workflows
- Trigger-based actions
- Integration with tools like Slack or CRM systems
- Approval-based execution
This helps teams move from insight → action without friction.
Trust & Compliance Layer
- Alert systems for misinformation
- Brand consistency checks
- Risk monitoring
This ensures businesses maintain credibility across AI channels.
Challenges
Abstract Problem Space
Solution: Introduced simplified scoring and visual indicators.
Information Overload
Solution: Focused on prioritization and progressive disclosure.
Trust in AI Insights
Users may question AI-driven recommendations.
Solution: Added transparency and explainable insights.
Key Features
From “Search Rankings” to “AI Presence”
Instead of focusing on clicks or rankings, the product introduces a new way of thinking:
How often and how accurately does AI mention your brand?
This led to the idea of a visibility score system, helping users understand their position in AI-generated answers.
Results & Impact
The final design delivers the following:
- Clear understanding of AI visibility
- Actionable insights instead of raw data
- Reduced manual monitoring effort
- Improved decision-making speed
It repositions AI from a black box to a manageable system.
Conclusion
This project explores how the shift from traditional search to AI-driven discovery is changing the way brands are seen and evaluated online. By focusing on AI visibility instead of rankings, the solution introduces a more relevant and future-ready approach to digital presence.
The final product simplifies a complex ecosystem into a clear, actionable experience—helping users understand how their brand appears in AI-generated responses and what steps they can take to improve it. Through thoughtful design, structured insights, and guided actions, the platform moves beyond passive analytics and becomes an active decision-making tool.
From research to iteration, the process reinforced the importance of clarity, prioritization, and user trust when designing for emerging technologies. Rather than overwhelming users with data, the solution focuses on delivering meaningful insights that lead to confident decisions.