CASE STUDIES
Designing for Impact & Growth
The case studies below showcase my hands-on leadership across design strategy, systems thinking, and execution — from concept to launch. Select case studies are shared in context based on the relevance of the opportunity.
Growth Referrals Journey
Redefined how users discover, share, and earn through referrals by designing a system grounded in motivation and timing — not marketing gimmicks. A data-led, human-centered redesign that turned a stagnant referral program into a self-sustaining growth engine.
Digital Marketplace Platform
Designed an enterprise-scale marketplace that brought order, speed, and trust to complex B2B transactions. Translated operational chaos into a unified buying experience for providers and payers, accelerating revenue and strengthening cross-functional alignment.
AI + Live Agent
Designed a hybrid conversational system that balanced automation with empathy, reshaping how healthcare providers access critical payment data. Proved that AI can amplify the human experience when designed with understanding, not just intelligence.
From Insight to Impact
The gap between understanding users and shipping value isn’t about research volume; it’s about translating insight into decisions that matter. Most teams collect data but struggle to act on it with confidence. The difference comes down to knowing what’s actionable, what’s ambiguous, and what deserves deeper validation before committing resources.
Great products emerge when insight drives prioritization, not just documentation. This means balancing speed with rigor: running lean discovery when the cost of being wrong is low, investing in depth when the stakes are high, and always connecting what we learn back to measurable business outcomes. The goal is building products people trust and adopt through repeated use, not just features that test well in isolation.
My philosophy
How I Design for AI-Driven Product Success
Whether it’s a startup product racing toward product–market fit or an enterprise platform built for scale, great products don’t succeed just because they function. They succeed when they’re shipped, adopted, trusted, and continuously improved.
This visual reflects how I approach building AI-driven systems responsibly: combining human insight, model behavior, engineering rigor, and feedback loops that keep improving performance long after launch.
For me, intelligent products should not only work, they should learn, adapt, and deliver measurable value for both people and the business.
How I Work
Design Strategy
Ground every decision in purpose and evidence.
• User-centered approach
• Data-informed insights
• Competitive analysis
• Cross-functional alignment
• Prioritization tied to outcomes
AI/ML Product Design
Design systems that learn, adapt, and perform.
• System design + data design
• Model evaluation and refinement
• Human-in-the-loop validation
• Responsible AI principles
• Scalable patterns for AI-driven workflows
Art of the Possible
Bring ideas to life fast and refine through proof.
• Rapid prototyping
• User testing
• Feedback loops that de-risk decisions
• Iterative design
• Continuous experimentation
Experience that spans industries, teams, and technologies
Across in-house roles and consulting engagements, I’ve had the privilege of partnering with product, research, engineering, data science, and leadership teams at scale to design systems, accelerate growth, and ship products that matter.
