AI Feedback Intelligence Tool
Simplifying Complex Customer Feedback with Gen AI
OVERVIEW ✍️
As a UX designer focused on scalable enterprise solutions, I led the design of an internal AI-powered tool that automated how Vanguard teams analyze customer feedback.
Previously, teams had to manually review over 1,000 feedback entries per week—an inefficient and error-prone process.
FINAL PRODUCTS 🖥️
The tool scans thousands of feedback entries and automatically detects recurring themes, sentiment trends, and user pain points. What once took entire research teams now happens in seconds, helping the team spot patterns they didn’t even know to look for.
FINAL PRODUCTS 🖥️
Manually sifting through user feedback used to take days—now, it’s done in seconds. The AI rapidly organizes, summarizes, and highlights key insights so the team can focus on what matters.
THE PROBLEM 🚩
Vanguard experienced a surge in customer feedback following a wave of layoffs and retirement rollovers, especially within the onboarding journey. The existing system required team members to manually sift through thousands of comments every week—most of which went unread or undocumented.
This overwhelmed the team’s capacity, delayed the discovery of critical issues like broken features, and limited the company’s ability to take timely, meaningful action based on user input. Despite the volume of feedback, there was no scalable way to extract themes, measure sentiment, or prioritize urgent problems without additional manual labor.
AI DESIGN ♾️ DESIGN AI
Designing AI products goes beyond interface work—it requires understanding how data flows, how models behave, and how users trust machine-generated insights. I collaborated closely with engineers to align design with data pipelines and model capabilities, ensuring outputs were both reliable and explainable.
To build trust, I designed visualizations and summaries that balanced clarity with transparency, helping users interpret AI results with confidence. This project reinforced my belief that great AI design lies in simplifying complexity—turning raw data into decisions people can act on.
KEY FEATURES 🖥️
To address the overwhelming volume of feedback, I led the design of an AI-powered tool that automates the end-to-end analysis process. Working cross-functionally with engineers and strategists, I mapped out the data pipeline, prioritized user pain points, and created a streamlined interface that enables teams to extract insights in real time.
The tool was built to adapt to different team workflows, helping them surface trends, monitor sentiment, and act quickly on what matters most.
THE SOLUTION 🎲
To address the overwhelming volume of feedback, I led the design of an AI-powered tool that automates the end-to-end analysis process. Working cross-functionally with engineers and strategists, I mapped out the data pipeline, prioritized user pain points, and created a streamlined interface that enables teams to extract insights in real time.
The tool was built to adapt to different team workflows, helping them surface trends, monitor sentiment, and act quickly on what matters most.
FINAL PRODUCTS 🖥️
The final product of the tool inspired my passion in solving complex real-world problems with the combination of Data and AI. Data is the future, and by automating this manual process, I learn to deliver products that not only improved operational efficiency, but also help business understand their product metrics, users' needs and expectations.
✅ Improving efficiency and streamlining the feedback analysis process
✅ Empowering stakeholders with timely and actionable insights
✅ Enabling Vanguard to make informed decisions that aligned with user needs