CASE STUDY - 0-TO-1 INTERNAL + PARTNER TOOLING | AI ASSISTED DESIGN
dcbel Field Technician App
dcbel technicians run site surveys and installations with no structured way to log their findings, leaving CX and sales to track them manually. I owned the design strategy and delivered a working MVP for a native app.
My Role
Design & strategy
Tools
Figma, Miro, Claude Code
Timeline
Aug 2026 – present
Information architecture
User flows
Feature prioritisation
Heuristic & cognitive evaluation
AI-assisted prototyping
Stakeholder management
Scope negotiation
01 -
Context: an operational problem, not technology
CX and sales teams tracked field assessments and installation progress manually. The volume of work made the process unsustainable.
dcbel technicians conduct field surveys before installing Ara (smart home energy unit) at customer homes. They had no structured way to capture their surveys, which reached the business through emails and messages.
That gave dcbel no reliable visibility into field activity and put a growing coordination burden on teams. The need was acute and predated my involvement. As Ara installations scaled, manual tracking started failing.
how might we
How might we give field technicians a fast, structured way to capture survey and installation while giving dcbel teams reliable visibility into field progress without manual tracking?
Engineering handoff in two weeks
Owned the full path from IA, user flows, feature value assessments, wireframes, evaluation, to remediation.
24 issues resolved
Identified through heuristic, usability, and cognitive evaluation of an AI-generated build, ranked by criticality.
I'd love to share my design process but the details of this project is private. To view the complete project, please use the password I provided or feel free to contact me.
What you will unlock -
Research & scoping
Design strategy
Adapt & compromise: a working build that wasn't usable
Evaluation: 24 issues, ranked by criticality
Handoff & status
Unlock
08 -
Lessons learned
LESSON 01
When AI generation is cheap, objective evaluation is the scarce skill
Today, anyone can quickly produce working software. What that build can and cannot do separates functional software from usable ones. The design contribution is to look for the relevant issues, which will become more valuable as AI generation gets easier.
LESSON 02
Someone else's prototype can be research
My first instinct when I saw a build that ignored UX standards was to treat it as rework. While it didn't meet UX standards, it captured the exact data requirements from someone who works with customers daily.
LESSON 03
New tools, same bar