Opendoor

Opendoor

Helping people in crisis navigate shelter, food, and legal aid
Summary
Opendoor is a mobile app that helps people in crisis find verified shelter, meals, and emergency support in minutes, replacing fragmented paperwork with clear, plain-language guidance.
My Contribution
As co-founder and design lead, I led 50+ usability sessions with shelter residents and prototyped the core service discovery flows from 0 to 1 using Figma.
Timeline
From May 2025 to Aug 2025
Team
1 Product Manager
2 Designers
2 Engineers
Skills
User Research
Interaction Design
Rapid Prototyping
Usability Testing
Tools
Miro
Figma
UserTesting
At a Glance

From a side project to award-winning, reimagining how people access essential resources nationwide.

Key Highlights

$10k won

as the Grand Prize Winners of the Kevin Xu Innovation Challenge

50 Users

tested the prototypes, 92% said they'd use OpenDoor if deployed

Selected

Builder cohort, 2025 Harvard Applied AI Grid Incubator and Amazon Web Services

5+ shelters

expanding early pilots across the Bay Area and Boston

Overview

Opendoor grew from a side project into an award-winning platform with strong usability validation.

Key Highlights

$10K grand prize

Grand Prize Winners of the Kevin Xu Rhodes Innovation Challenge

50+ usability tests

92% said they would use if deployed

5+ shelters

expanding early pilots in SF and Boston

Search and filter

Categorized listings for housing, legal aid, recovery, and job search, with filters for open-now status, walk-in availability, and other needs.

Comprehensive Listing Information

Each listing shows eligibility requirements, what to bring, and services offered, along with hours, availability, and directions.

Conversational AI

Users can describe their situation in plain language and the AI interprets that query to surface the most relevant resource.

The Problem

Outdated, fragmented systems prevent people in crisis from finding immediate help.

Even after finding the right service page, users had to scroll through 4 screens before reaching eligibility requirements, one of the first things people need to know before calling or visiting. That audit became the foundation for Opendoor, rebuilt as a mobile-first experience designed around clear information hierarchy from the ground up.

User Insights

Fragmented information turned simple searches into exhausting dead ends.

Through 13 shelter staff interviews and surveys across 50+ community members, three critical pain points emerged. Without real-time updates, people spent limited time and transit money traveling to locations that couldn't take them.

Key Insights

48%

took wasted trips due to unclear eligibility

20+ minutes

spent hunting per single resource

4+ sites

checked just to find a single service
Search and filter, designed around real constraints
Categorized listings for housing, legal aid, recovery, and job search, with filters for open-now status, walk-in availability, and accessibility needs, helping people in crisis narrow results in seconds, not another site to dig through.
Everything a user needs to know, in one view
Each listing shows eligibility requirements, what to bring, and services offered, along with hours, availability, and directions, giving users everything they might need to confirm they qualify for that specific resource and get there, without wasting a trip.
Ask OpenDoor: a second way to search using AI
Alongside the standard browse-and-filter experience, users can describe their situation in plain language (what's going on, what they need) and the AI interprets that to surface the most relevant resource, no prior information required.

Competitive Analysis

Current tools force people to choose between complex emergency directories and generic consumer maps.

The patterns from our Good Samaritan research pointed to a gap larger than one shelter's website. Existing directories like 211 and FindHelp aggregate resources but are hard to navigate, while general tools like Google Maps and Yelp are easy to use but aren't built for the specific, often urgent needs of vulnerable populations.

Ideation

Rapid AI prototyping helped us evaluate different layouts in hours instead of days.

We tested four early layouts, from a simple search bar to a map view, to see what was easiest for people to envision themselves using before fully flushing out the final designs.

Information Architecture

We defined the core MVP user flow by identifying what to prioritize and intentionally cut.

Before designing any screens, we mapped every possible user action and narrowed the core experience down to three core MVP flows: the homepage, service details, and conversational AI search, with supporting features like offline access, bookmarks, and accessibility settings built around them.

Designing the Solution

We evaluated design tradeoffs to find the most intuitive path for users in high-stress situations.

Filter-First Layout

Has a map/list view toggle (visual search vs. list search)
High cognitive load (e.g wall of controls/filters)

Organized by Categories

Establishes a clear, predictable navigation path
No primary actions like "Call" or "Directions"

Guided-Discovery Layout

Users can discover services through categories or browsing
Users can easily scan for name, status and primary actions

Expanded Card Pop-Up

Groups key information into logical, collapsible sections
Caseworkers noted that important details are buried

Single-Column Details Page

Has a full page view for expanded service details
Still is hard to scan for our users

Easy-to-Scan Details Page

Builds users' trust with an entrance photo, confirming the location is safe to approach.
Uses progressive disclosure by hiding secondary info

Pre-Filtered Smart Search

Relevant filters like sort by "Distance" and "Type of Help"
Users think "Type of Help" UI is complex and unclear
Requires selecting filters before seeing any results

Integrated Search

No complicated pre-search filter barrier
Accidental conflicting touch targets for many users
The service card competes with the search dropdown

Conversational AI Search

Separates search into its own view
Uses progressive disclosure by hiding secondary info
Positions AI as a conversational path to an answer

Lastly, we also built an design system with WCAG AA color contrast and expanded touch targets to minimize errors during navigation.

Impact & Outcome

Validated with 50+ users and 5+ shelters including a responsive design system in 8 weeks.

Key Outcomes

$10K Winner

Kevin Xu Innovation Challenge

Harvard & AWS

incubator & sponsor backed

92% user intent

would use if deployed locally

Reflection

Testing early and often kept us moving when the answers weren't as obvious.

We didn't know what would work until we put rough prototypes in front of shelter residents. Testing early showed us what people actually needed and stopped us from spending weeks guessing.
We didn't know what would work until we put rough prototypes in front of shelter residents. Testing early showed us what people actually needed and stopped us from spending weeks guessing.
Berkeleytime
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AI clinical prior authorization tool

Back

Overview

The Problem

User Insights

Designing the Solution

Impact & Outcome

Reflection