Ren Energy
The planet can't wait. But the companies trying to help were stuck fighting their own tools.
Role
UX Designer Intern
Timeline
Sep – Dec 2022
Company
Ren, Google Cloud Partner
Outcome
$250K new revenue in three months
The planet is running out of time. And the companies trying to fix it are stuck fighting their own tools.
Ren Energy helps factories track and reduce their environmental impact. But their clients were spending more time wrestling with data uploads than actually making progress on sustainability. One broken import could stall an entire factory's onboarding for weeks. 2,000 factories waiting. The mission stalling at a spreadsheet.
Flow diagram showing the broken pipeline: Upload → Error → Engineer fix → Retry → More errors. A cycle that never resolves. Show it as a frustrating loop. The recruiter should feel the absurdity: the planet needs help but we're debugging CSV files.
First instinct: give clients full control over their data.
Let them see everything. Edit everything. Fix their own errors. If we put the power in their hands, they wouldn't need to wait for engineers. More control should mean more autonomy.
Screenshot of the full-control interface. Many options, fields, editing capabilities. It looks powerful but overwhelming. Annotate: 'Gave clients every tool. They didn't know which one to use.'
Full control created confusion. So I automated everything. That broke trust.
Too many options paralyzed users. So I swung to the opposite extreme. Automate the matching, the validation, the error correction. Take the burden off the client entirely. But now they couldn't see what the system was doing. When it made mistakes, they had no way to catch them. They trusted spreadsheets more because at least they could see the data.
Two-panel failure: PANEL 1: Full control (red X) - 'Too many options. Clients paralyzed.' PANEL 2: Full automation (red X) - 'No visibility. Clients couldn't verify.' Show both approaches and why each failed. The recruiter should feel: 'ok so what DO you do?'
Neither control nor automation works alone. The answer is collaboration.
The breakthrough was designing a system where the machine handles precision and the human validates meaning. The system suggests. The client confirms. Trust comes from verification, not blind faith and not overwhelming options.
Three-panel comparison: PANEL 1: Full control (red X). PANEL 2: Full automation (red X). PANEL 3: Collaboration (green check) - system suggests, human confirms. The third panel should feel like a resolution. Show the actual UI with confidence scores and one-click overrides.
Trust came from verification, not perfection.
The system didn't need to be perfect. It needed to let clients see its work and confirm it was right. A confidence score next to each match. A one-click override when it was wrong. Transparency turned a black box into a partnership.
Annotated screenshot of the collaboration interface. Show confidence scores, one-click overrides, validation checkpoints. Annotate: 'Confidence score lets clients trust without blindly accepting' and 'One-click override keeps the human in the loop without slowing them down.'
Precision met partnership. The data started flowing.
$250K
In new revenue generated
60%
Faster imports
35%
Fewer errors
90%
Matched automatically with client verification
Final product: The polished collaboration dashboard. Data flows in, system processes and suggests with confidence scores, client reviews and confirms. Show the whole system working. This is the resolution.
The best answer was the one nobody was proposing.
The debate was always control vs automation. Give users power or take it away. The real answer was designing a system where human judgment and machine precision work together. Not one or the other. Both.