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[Case 01]

Mason AI

Mason AI

[Company]

Xenara AI

[My Role]

UX Design Lead

[Platforms]

Mobile

[Timeline]

6 months

View Prototype

Digitizing labor, logistics and compliance across build sites

[Project Overview]

Mason AI is Xenara’s next-generation operations platform designed to modernize industrial environments such as warehouse construction, modular pod assembly, and live material logistics. It brings AI-assisted scheduling, workflow logistics and safety compliance into a unified ecosystem.

Due to NDA restrictions, I am only able to share the mobile employee platform. This version was designed for field associates, enabling them to clock in securely, access daily schedules, report safety issues, and scan materials all from one intuitive app.

[Company]

Xenara AI

[My Role]

UX Design Lead

[Platforms]

Mobile

[Timeline]

6 months

View Prototype

[Research and Collaboration]

To ground the product in reality, I collaborated directly with an Amazon distribution manager and his team to understand their operational frustrations. Through field observations, interviews, and iterative usability testing, we mapped their workflows and pinpointed friction points.

Together, we reimagined the daily routines of field associates, creating a system that would:

  • Reduce manual errors and duplicated effort.

  • Reinforce safety and compliance protocols.

  • Provide clarity and accountability across roles.

Through continuous feedback loops and on-site validation, we reached the final stages of implementation with Amazon, transforming their legacy processes into a cohesive, modern experience.


[The Core Features of Mason AI 's Mobile Platform]

The Mason AI Hand-Held Staff App connects workers, supervisors, and systems through a single mobile hub.


Clock-In / Clock-Out System

  • GPS and facial recognition verification.

  • Auto-loads required safety forms (JHA) before shift start.

  • Confirms compliance and marks user “On Site / On Shift.”

    Impact: Eliminated false clock-ins and ensured verified compliance.



Daily Work Schedule

  • Interactive task calendar organized by urgency and time.

  • Accept/Decline flow with automated supervisor notifications.

  • Real-time feedback loop for task completion and assistance requests.

    Impact: Increased accountability and reduced communication delays.



SOR (Stop, Observe, Report)

  • Camera-first workflow for reporting hazards.

  • XENA AI generates incident reports automatically with editable fields.

  • Tracks issue progress with real-time supervisor chat.

    Impact: Reduced incident response time by 60% and tripled report submissions.


Material Scanning System

  • QR-based material tracking by truck and condition.

  • Photo logging and automated admin alerts for damaged goods.

    Impact: Improved traceability and reduced inventory errors.

[Persona]

Ravi Smith

Assembly Line Worker

"I just want to focus on doing my job, not chasing paperwork or trying to find out what’s next"

Age: 35

Goal: Complete daily tasks efficiently

Tech Proficiency: Medium

Gender: Male

[Needs]

A simple way to manage tasks and safety efficiently

Clear, real-time communication with supervisors

Tools that save time and make reporting easier

[Frustrations]

Paper forms slow down reporting and task tracking

Unclear task priorities lead to confusion

Difficult to verify materials and log issues in real time

Ravi's User Journey

Morning: Clock in

Action: Ravi arrives at the site and opens Mason AI.

System: GPS confirms his presence, Face ID verifies identity, and the JHA safety form auto-loads for signing.

Outcome: Ravi clocks in seamlessly and starts his day on time.

Mid-Morning: Check Schedule

Action: He opens the Daily Work Schedule.
System: Tasks appear in priority order
Outcome: He accepts tasks and begins work immediately.

Noon: Spotting a Safety Hazard

Action: Ravi notices a loose cable on the floor.
System: He taps the SOR widget, snaps a photo, and XENA AI auto-generates a report.
Outcome: Supervisor receives an alert and dispatches help.

Afternoon: Material Verification

Action: A delivery truck arrives. Ravi opens the Material Scanning widget.
System: Scans each QR code, adds photos of damaged boxes, and submits the report.
Outcome: Admin gets an instant alert; materials logged cor.rectly.

Evening: Clock out

Action: Ravi finishes his tasks and taps Clock-Out.
System: GPS reconfirms location, syncs data, and updates dashboard.
Outcome: Supervisor sees full summary of Ravi’s shift — attendance, completed tasks, and safety reports.

Ravi's User Journey

Morning: Clock in

Action: Ravi arrives at the site and opens Mason AI.

System: GPS confirms his presence, Face ID verifies identity, and the JHA safety form auto-loads for signing.

Outcome: Ravi clocks in seamlessly and starts his day on time.

Mid-Morning: Check Schedule

Action: He opens the Daily Work Schedule.
System: Tasks appear in priority order
Outcome: He accepts tasks and begins work immediately.

Noon: Spotting a Safety Hazard

Action: Ravi notices a loose cable on the floor.
System: He taps the SOR widget, snaps a photo, and XENA AI auto-generates a report.
Outcome: Supervisor receives an alert and dispatches help.

Afternoon: Material Verification

Action: A delivery truck arrives. Ravi opens the Material Scanning widget.
System: Scans each QR code, adds photos of damaged boxes, and submits the report.
Outcome: Admin gets an instant alert; materials logged cor.rectly.

Evening: Clock out

Action: Ravi finishes his tasks and taps Clock-Out.
System: GPS reconfirms location, syncs data, and updates dashboard.
Outcome: Supervisor sees full summary of Ravi’s shift — attendance, completed tasks, and safety reports.

[Design System]

Foundation: MUI-based component library tailored for field use

  • Status Language: Consistent colors/states for On Shift, Pending, Resolved, Alert.

  • Microcopy: Plain language, action-first labels (“Report hazard”, “Scan next”).

  • Latency Patterns: Optimistic updates + queued sync for low connectivity.


[Design Process]

The design process centered on usability in high-pressure environments:

  • Conducted shadowing sessions with Amazon staff to observe real workflows.

  • Created low-fidelity wireframes to test flow clarity and tap ergonomics.

  • Developed high-fidelity prototypes in Figma using Mason’s MUI-based design system.

  • Iterated through usability testing rounds with field teams.

Each iteration reduced the time to complete core tasks (clock-in, reporting, scanning etc.)

[Outcomes & Impact]

  • 40 percent reduction in administrative overhead.

  • 60 percent faster safety response times.

  • 300 percent increase in reported safety issues (due to ease of use).

  • Validated by Amazon and entering final implementation phase.

A Personal Challenge

When I first took on this project, everything shifted overnight. The lead designer left unexpectedly, and one of my co-designers went on leave at the same time, leaving me as the only designer on the team. Suddenly, I was promoted to lead designer and found myself responsible for the entire design process from start to finish. It was definitely a learning curve, but it also became one of the most defining moments of my career so far. I had to adapt fast, stepping into leadership while still managing the hands-on design work, user research, testing, and stakeholder communication. It taught me how to stay calm under pressure, make decisions confidently, and balance creative vision with execution. Looking back, that challenge helped me grow faster than I expected. It strengthened my ability to communicate across teams, and deliver an end-to-end product that not only worked but also reflected a clear, human-centered vision.

A Personal Challenge

When I first took on this project, everything shifted overnight. The lead designer left unexpectedly, and one of my co-designers went on leave at the same time, leaving me as the only designer on the team. Suddenly, I was promoted to lead designer and found myself responsible for the entire design process from start to finish. It was definitely a learning curve, but it also became one of the most defining moments of my career so far. I had to adapt fast, stepping into leadership while still managing the hands-on design work, user research, testing, and stakeholder communication. It taught me how to stay calm under pressure, make decisions confidently, and balance creative vision with execution. Looking back, that challenge helped me grow faster than I expected. It strengthened my ability to communicate across teams, and deliver an end-to-end product that not only worked but also reflected a clear, human-centered vision.

What I Learned

This project taught me the difference between leading design and simply designing well. When I suddenly became the sole designer on Mason AI, I had to make decisions without the safety net of a larger design team, which forced me to develop real creative independence and sharpen my judgment. I learned that good leadership isn’t about having every answer; it’s about keeping momentum, even when you’re still figuring things out. One of the biggest lessons was how to design for environments that don’t behave like tech products. Warehouses, construction zones, and logistics hubs aren’t “user-friendly” spaces. Watching how workers adapted (or struggled) in those settings taught me to value friction differently: not every extra step is bad if it protects safety or builds trust. I learned to balance human psychology, safety protocols, and speed in ways that felt authentic to how people actually work. Collaborating with Amazon’s operations manager also taught me to listen beyond words, to understand the tension between what people say they need and what actually slows them down. It showed me how to design systems that earn adoption rather than assume it. Most importantly, this project made me more patient and intentional as a designer. I learned that clarity doesn’t come from good UI it comes from understanding people enough to make their day just a little easier.

What I Learned

This project taught me the difference between leading design and simply designing well. When I suddenly became the sole designer on Mason AI, I had to make decisions without the safety net of a larger design team, which forced me to develop real creative independence and sharpen my judgment. I learned that good leadership isn’t about having every answer; it’s about keeping momentum, even when you’re still figuring things out. One of the biggest lessons was how to design for environments that don’t behave like tech products. Warehouses, construction zones, and logistics hubs aren’t “user-friendly” spaces. Watching how workers adapted (or struggled) in those settings taught me to value friction differently: not every extra step is bad if it protects safety or builds trust. I learned to balance human psychology, safety protocols, and speed in ways that felt authentic to how people actually work. Collaborating with Amazon’s operations manager also taught me to listen beyond words, to understand the tension between what people say they need and what actually slows them down. It showed me how to design systems that earn adoption rather than assume it. Most importantly, this project made me more patient and intentional as a designer. I learned that clarity doesn’t come from good UI it comes from understanding people enough to make their day just a little easier.