Intelligent Apps
Transforming predictive models into intelligent MVPs
Project overview
Realogy, a leading U.S. residential real estate services provider, sought to leverage the power of data science by embedding predictive models into intelligent applications. The goal was to support users—agents, brokers, and business teams—in identifying new market opportunities and assessing risks with data-backed insights. However, there were multiple challenges:
How to translate complex predictive data models into intuitive, actionable product experiences?
How to validate the business potential of these models through scalable, cross-platform proof of concepts and MVPs?
Approach & Design Process
Discovery & Alignment
I partnered closely with data science, product, and engineering teams to:
Understand the core capabilities and business value of the predictive models.
Define product goals, technical constraints, and user expectations for real estate professionals.
Initiate collaborative whiteboard sessions to uncover the functional potential of the models.
Key Activities:
Stakeholder alignment, design thinking workshops, domain research on real estate user needs and predictive insights.
Ideation & Concept Development
Working in lean, iterative cycles, I facilitated ideation sessions to translate abstract data capabilities into tangible product concepts. These included:
Predictive market insights
Risk analysis dashboards
Agent performance forecasting tools
Opportunity scoring interfaces
Tools & Methods: Lean UX, agile methodology, story mapping, and information architecture.
Prototyping & Interaction Design
I designed a range of wireframes, interactive prototypes, and interfaces that explored data interaction, mobile-first behaviors, and cross-platform viability.
Applied Google Material Design guidelines to accelerate development readiness.
Defined interaction patterns, behavior guidelines, and accessibility standards.
Integrated data visualization techniques for clear and meaningful storytelling.
Deliverables: Lo-fi and mid-fi prototypes, design specifications, annotated wireframes, UI assets.
Collaboration & Leadership
Led UX efforts across a small distributed team in India and Colombia, ensuring cross-time-zone collaboration and a shared design vision. I supported:
Offshore UI development teams with detailed design guidelines and assets.
Internal team alignment through iterative design reviews and feedback loops.
Stakeholder engagement by introducing Lean UX and Design Sprint methodologies.
Focus Areas: Communication, design operations, process improvement.
Core Skills Applied
Research & Strategy: UX discovery, stakeholder interviews, domain analysis
Design Execution: Wireframing, prototyping, design systems, mobile-first UI
Methodologies: Lean UX, Agile, Design Thinking, Design Sprints
Collaboration: Cross-functional leadership, distributed team coordination
Tools: Figma, Jira, whiteboarding, interactive prototyping platforms
Disclaimer: In my role as a UX consultant under NDA agreements, I'm limited in how much I can visually disclose. While I can't show full-resolution designs or detailed project visuals, I’ve highlighted key insights and outcomes to showcase my thinking and contributions.
White boarding and ideation session
Lo-fi wireframes for proof of concept
White boarding and ideation session
White boarding and ideation session
White boarding and ideation session
Lo-fi wireframes for proof of concept
Lo-fi wireframes for proof of concept
“By combining data science with human-centered design, we helped the client move from abstract model potential to real, scalable product opportunities.”
Learnings
The power of curiosity and collaboration when navigating emerging technologies like predictive modeling.
Importance of rapid, iterative prototyping to drive stakeholder understanding and validate feasibility.
How UX can bridge disciplines—design, development, and data science—to align vision, value, and delivery.
Working on the Intelligent Apps initiative at Realogy was a pivotal experience that deepened my understanding of how UX can unlock the value of emerging technologies. It challenged me to translate complex data science into accessible, impactful user experiences—balancing business goals, user needs, and technical feasibility.
Most importantly, it reminded me that design is not just about creating interfaces, but about shaping thoughtful tools that turn data into decisions, empower users, and transform prototypes into real impact.