MORE PROJECTS
ROLE
Product Designer
COLLABORATORS
Devin Toth
Jacob Runyon
Marc Subido
Fred Mustiere
SKILLS
Interaction Design
Prototyping
Accessibility
Stakeholder Management
THE OUTCOME
A 50% reduction in internal workflows and a 2x increase in accessibility
The lack of accessibility in the PDF's user experience and was a significant drawback that negatively impacted the product's appeal. The goal was clear: streamline documentation, improve accessibility, and enhance usability, resulting in a 50% reduction in internal processes and a twofold increase in accessibility, making drone identification and classification more efficient for users.
THE SUMMARY
What is Titan C-UAS?™
Titan is an autonomous drone detection and defense system that uses artificial intelligence to detect and safely neutralize aerial threats without causing harm. A key feature is its ability to classify drones using backend classifiers, which push identification details to the UI in real time.
When detections include precision or non-precision GPS data, an informational component becomes available, drawing from an internal library to help users better understand the identified threat.
This is called Drone Education.
As part of Drone Education, there is a PowerPoint presentation that details the drone models that Titan can identify with its classifiers and it's specific characteristics.This is embedded in the UI as a PDF. Users are not able to search or navigate it easily to locate specific drone types.
THE CHALLENGE
A fragmented internal experience
The process of updating the Drone Education involved collaboration from 5 different teams that relied on multiple platforms—Trello, Excel, and PowerPoint—to contribute to a single resource. This convoluted workflow not only created inefficiencies but also resulted in a lack of accountability.
A major sub-issue was that the categorization of drones was flawed, relying on frequency types instead of accurate classifications. The specifications associated with particular drones were also incorrect; we often presented drone characteristics with a false sense of certainty, despite the reality of their variability.
I learned that the previous designer had attempted to categorize drones by their hardware components but faced challenges due to the interchangeable nature of drone parts. Recognizing the need for a holistic understanding of the problem, I dedicated time to research and gather insights from various stakeholders.
THE PRELIMINARY IDEA
Marie Kondo-ing the UI
Following initial conversations with business development, my first instinct was to remove the PDF altogether. I collaborated with a software developer to analyze click data, which confirmed my hypothesis—users were seeking drone-specific information but rarely engaged with the PDF.
Internally, maintaining the source PowerPoint required coordination across five teams and was often outdated, making it a high-effort, low-impact asset.
The initial response was a firm no. Business development defaulted to the classic “if it works, why change it” stance, emphasizing their preference for having a secondary resource where users could find additional drone-specific information.
I had to pivot.
outline or previous visualizer
USER RESEARCH
Grounding decisions in cross-functional user research with internal SMEs
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Head of AI/ML: Discussions revealed backend inconsistencies in drone classification. The AI classifiers used for drone detection were often inaccurate due to drones switching frequency bands or operational modes—a factor that wasn’t being openly acknowledged.
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Drone Pilots: Pilots emphasized the need for precise, real-time data to correlate on-ground observations with their tablet displays. However, hardware variability often led to misidentification issues, creating distrust in the system.
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Business Development (BD) Team: Conversations highlighted dashboard and naming inconsistencies, reinforcing the need for a new, more structured approach to drone education.
COMPETITIVE BENCHMARKING
Drones on Drones on Drones
Few products match Titan’s capabilities, and those that come close often lack drone-specific data due to lack of ability or complexity. With limited public information available for direct competitors, I turned to comparative benchmarks from adjacent industries.
Adjacent industry
Flight management for drones
Key Relevant Feature: Catalogue of drone flights per type of drone
Similar industry
RF/drone detection & jamming
Key Relevant Feature: Drone identification
SOLUTION #1
The Drone Library
Equipped with user insights and competitive analysis, I conceptualized the Drone Library—a centralized repository designed to provide users with easy access to drone information. My design focuses included:
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Search Functionality: Users needed the ability to search for specific drone types and related information effortlessly.
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Intuitive Interactions: The interface needed to be user-friendly, allowing for seamless navigation and interaction.
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Customization Options: Filters would enable users to tailor their searches based on their specific interests.
SOLUTION #2
Solving the problem from both sides
In redesigning the Drone Education cards, I focused on displaying key information clearly and concisely. By shifting focus from specific drone models to the protocols used for classification, we could provide users with accurate information while managing expectations about detection accuracy. For example, instead of asserting “this drone is present,” we would indicate the likelihood based on established protocols.
This would help preserve user trust by avoiding overconfidence in the system’s detections.
THE OUTCOME
What we say, and how we say it, matters
The redesigned Drone Library significantly would have improved operational effectiveness and efficiency. By limiting the number of touch points and establishing clear responsibilities, it would have enhanced accountability throughout the process.
This feature would have allowed for:
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Increased Customer Satisfaction: Users can now search for and identify drones more quickly, leading to a more satisfying experience.
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Clearer Access to Information: The new design allows users to easily access drone and protocol-specific information.
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Scalability: The library is designed to accommodate the addition of new drones and protocols seamlessly.
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Reduced Touch points – Streamlining the update process led to a 50% decrease in internal inefficiencies.
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Better Photo Representation & Naming Conventions – This reduced mismatches in UI displays, reinforcing user trust.
Despite initial resistance, I successfully championed the Drone Library initiative for six months, demonstrating its long-term value. I was able to aligning AI/ML teams, BD teams, and pilots around a shared vision ensured smoother implementation and was able to get buy-in from engineers.
However, it was decided that this was a larger lift that feasible at the moment and with the upcoming product release, it needed to be punted.
LESSONS LEARNED
Not now, but not never: navigating timing and tradeoffs
Sometimes teams lack the time or budget to invest in innovations that could genuinely help them but it's up to you to communicate the potential value. I advocated for this feature, but the benefits never quite justified the lift required to launch it.
What I was able to do, however, was align the teams around a shared direction and secure the feature’s place on our long-term roadmap, ensuring it will be addressed when the timing is right.
