REPAIRSOULTIONS2 MOBILE APP · CORE WORKFLOW

Turning research into a core workflow people can trust

RepairSolutions2 helps DIYers diagnose dash lights and repair their cars by reading diagnostic information.

ROLE

Lead UX Designer

TIMELINE

6 weeks · 2026

TEAM

Product · Eng · Business

SCOPE

Research → Release

Executive snapshot

THE PROBLEM

Users were leaving the app to complete their repair journey - we weren't providing a complete solution.

THE MANDATE

Improve engagement by extending the workflow and addressing the user's needs.

MY ROLE

Research strategy · Workflow design · Alignment · Validation

VALIDATED OUTCOME

Meaningful engagement that helped users repair their vehicles.

Transformation Process

The app helped users diagnose the vehicle, but didn't help them repair it.

01 · Research

Define user needs with interviews & surveys.

02 · Understanding Data

Work with engineers to determine data and technology gaps.

03 · Design

Leverage AI to fill in the gaps and help users actually fix their car.

Research Goal

Define the user's repair journey to determine how to extend the workflow within Repair Solutions2.

Methods

  • User Interviews (14 users, 30 min video sessions)

  • User surveys (n=1,100)

Key insights

After scanning their car for codes, 63% of users left the app to do additional research.

Design Implication:

Help users confirm the cause of the code so they can confidently identify the appropriate repair - not just take our word for it.

Knowledge, resources and cost savings are the key drivers of DIY repairs.

Design Implication:

We can encourage and enable users to DIY by providing them with guidance.

Part of their research is deciding whether to DIY or take it to a repair shop.

Design Implication:

Identify the most important variables and help them make a decision with a clear comparison.

Artifacts

Mapping the user journey informed key touchpoints in the workflow.

01 · Research

Define user needs with interviews & surveys.

02 · Understanding Data

Work with engineers to determine data and technology gaps.

03 · Design

Leverage AI to fill in the gaps and help users actually fix their car.

Research Goal

Define the user's repair journey to determine how to extend the workflow within Repair Solutions2.

Methods

  • User Interviews (14 users, 30 min video sessions)

  • User surveys (n=1,100)

Key insights

After scanning their car for codes, 63% of users left the app to do additional research.

Design Implication:

Help users confirm the cause of the code so they can confidently identify the appropriate repair - not just take our word for it.

Knowledge, resources and cost savings are the key drivers of DIY repairs.

Design Implication:

We can encourage and enable users to DIY by providing them with guidance.

Part of their research is deciding whether to DIY or take it to a repair shop.

Design Implication:

Identify the most important variables and help them make a decision with a clear comparison.

Artifacts

Mapping the user journey informed key touchpoints in the workflow.

From dead end to full-walkthrough:

BEFORE · CODE - FOCUSED

Users were presented with a diagnosis and a brief description of the 'most likely fix', but no action items or path forward.

AFTER · REPAIR-FOCUSED

A clear breakdown of the issues detected, easy next steps, and full repair guides for the recommended fixes.

Post-launch Outcomes

Validated AI Feedback Loop

The AI learning loop successfully tapered down negative responses by making corrections based on feedback and analytics.

Increased Engagement

Analytics showed an increase in engagement time and views per report.

Execution
Trade-offs

Due to resource constraints, I performed internal usability testing to validate execution prior to launch.

Reflection

What I would repeat:

Tackling complex workflows by finding the intersection of complex logic and data with the user's needs.

What I would change:
Dedicated usability tests prior to launch to help validate the data presentation.

What's next?:
Usability testing and validating generated data reliability at scale.

Thanks for your time

“It is not that we have a short time to live, but that we waste a lot of it.”
Seneca

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