S.T.

Pluto

Drone management software for medical equipment deliveries.

Live Demo
pluto thumbnail
Deliveries30K+
Success Rate+50%
Efficiency+85%
Executive Summary
  • Led product design for a drone logistics platform transitioning from MVP to beta.
  • Developed a staged validation strategy to safely test autonomous delivery.
  • Identified user trust, rather than drone technology, as the primary adoption barrier.
  • Supported 30K+ deliveries, reducing failed deliveries by 50% while improving operational efficiency by 85%.
  • The challenge wasn't flying drones. It was earning people's trust in autonomous delivery.

Overview

Pluto(NDA) is a Drone Management software, created for a logistic company. I attempt to use food delivery for our MVP to test our point A to B success-fail metric and efficiency to eventually use Drone for medical equipment deliveries.

| 30,000+ deliveries | 85% efficiency gains | 50% reduction in lagging indicators |

Summary

  • User Need: Empower first aid responders with confidence that they can save a person’s life.
  • Business Goal: Provide a digital tracker for emergency missions.
  • Challenge: How might we save more lives with drones delivery?

My role

Lead Product Designer

  • Deliver an MVP at Alpha stage to Beta stage.
  • Expanding the product’s use case from Alpha to Beta stage.
  • Alpha MVP (food delivery) > Beta MVP (First Aid Responder.)
  • Lead the design process from research to bring Alpha to Beta launch of the MVP.
  • Collaborate in a AGILE-SCRUM pod with Product Manager, Product Owner, and Developers in 2-Weeks Sprints.
  • Facilitate product discussion with founders to ensure business requirements are met.
  • Create AI Vibe Code prototypes for iterations, usability testing, and developer hand-off.

Products I worked on Function User
Drone Dashboard Manage all drone Drone Manager
Delivery Dashboard Manage all delivery Delivery Manager
Analytics Dashboard Manage all telemetry Safety Manager

Research

Personas

  • Jonah is a First Aid Responder. When he receives a call for support for an agent on the field over broken equipment, he needs to send another one quickly in a high pressure environment that impacts someone’s life.
  • Ben is an EMT service personnel. There are times when his defibrillator that he has brought on the ambulance fail to operate. Once that happens, he can only call support and hope for another EMT service personnel to be sent with a working one. He can only wait in a high pressure environment where seconds may affect someone’s life.
  • Gerry a Safety manager who ensures that all PPE(Personal Protective Equipment) and equipment maintenance are up to date. He manages the drone and analytics within the context of Pluto.

Userflow

I analyzed the current manual flow from end-to-end of how much steps it takes to deliver a defribilator to a patient and the possible issues that could prevent the patien from getting their medical equipment. I noticed that Pluto is able to automate most steps and the most tedious part of emergency medical treatment. I see opportunities of testing this remote locations where it takes hour for people, just to get emergency treatment. A drone is small and fast enough to go directly to the location with 0 blockers. Our research revealed that the biggest blocker wasn’t technology—it was trust. Users needed to believe the drone would arrive. That insight shaped our entire testing strategy.

Insights

The barrier wasn’t flying a drone. It was convincing people it would arrive.

Product Validation Strategy

  1. Experiment
  • Tested delivery in GPS-obstructed environments.
  • Measured successful autonomous completion.
  • Identified signal-loss failure modes.
  • Introduced operational safeguards before expanding testing.

  1. Staggered real life testing I know that I will never get any approvals to do a real life testing, so I aim to stagger our test cases and impact. Alpha stage has proven that the system is at a stable enough success rate to move onto the next impact stage. I started from delivering food, where the impact score caused by a failure is not life threatening, to delivering birthday cakes, where the impact score is more about preventing a 5-year old from crying at their birthday party. It has taken multiple months just to schedule and plan our gradual progress from one impact score to the next. Every decision I made—from the park test to the birthday cake trial—was guided by one question: ’Does this bring us closer to saving lives?

    I lost a drone on our first park test. A tree branch blocked the signal. I learned to build fail-safes into the system—and to test in more open spaces.

  2. Iterative design I often have to change and adjust on the go. Fortunately, with the availability of AI tools these days, it has helped us to make design and product decision in a matter of hours, rather than days.

Outcomes

When I saw the first drone successfully deliver a handkerchief in the park, I knew that I was onto something. It wasn’t medical equipment—but it was proof of concept.

  • 30000+ Drone delivery within 3 months of launch
  • 50% fewer failed delivery reduction within 6 months of launch
  • Clients reported 85% increase of efficiency from their telemetry

I’ve always been drawn to high-impact problems. Saving lives with technology isn’t just a challenge—it’s a responsibility.