This is a portfolio-safe redesign of a real, deployed product built for government and federal security training environments.
Helix was a diagnostic platform for airport CT scanner maintenance, designed to replace legacy command-line workflows with a visual system built around how technicians actually reason through physical equipment. The work addressed a high-stakes operational problem: fault identification depended too heavily on institutional knowledge, raw technical output, and the experience level of whoever was on shift. I designed Helix around spatial reasoning, progressive disclosure, and reusable diagnostic modules so scanner health, component status, and failure context could be understood faster and with less cognitive overhead.
Maintenance engineers operating CT scanner infrastructure in busy airport terminals were working against tools that were never designed for field diagnosis. Legacy command-line interfaces exposed system information, but they did not organize that information around how technicians located faults, prioritized action, or understood the physical machine in front of them.
That created an organizational risk. Troubleshooting depended on deep institutional knowledge, made fault identification slower and more error-prone, and left teams vulnerable when experienced personnel were unavailable or turned over. The design challenge was to move expertise into the interface itself, so any qualified technician could reason through the system with more confidence.
The strategic decision behind Helix was to treat spatial reasoning as the primary diagnostic model. Technicians think in physical systems: where the scanner is, which component is failing, how the failure affects throughput, and what action should happen next. The interface needed to reflect that mental model instead of forcing users to translate logs and terminal output into physical understanding.
The platform was built around a 2.5D isometric visualization system that let technicians reason through scanner infrastructure the same way they would on the floor. That decision shaped the information architecture, component hierarchy, status language, and data visualization model.
Progressive disclosure governed the experience. The system revealed the right level of detail at the right moment, moving from site-level awareness to component-level diagnosis without overwhelming technicians with every available data point at once.
The site view functioned as the operational command layer. A 2.5D isometric layout mapped every CT scanner across a multi-terminal environment in real time, with each unit color-coded by operational status: operational, degraded, or outage.
Ten scanners across Terminal A, B, and C were visible simultaneously, giving technicians immediate spatial orientation across the full site. Scanner cards surfaced health percentages for each unit, while the right panel summarized throughput trends, system capacity, and downtime by failure type.
A time-range control scoped the operational data to a relevant window, allowing technicians to assess the health of the full site and identify where to act next without leaving the command view.
Drilling into a specific unit brought the technician into a detailed isometric model of the scanner itself. Major components were labeled directly on the form: Detector Array, X-Ray Emitter, Gantry Ring, Power Supply Unit, and Conveyor Belt.
The physical model and diagnostic data were designed in direct correspondence. What technicians saw in the visualization mapped to what they needed to diagnose, reducing the translation cost between abstract system output and physical inspection.
Each component was paired with a diagnostic module shaped around how technicians understand and inspect that part of the machine. This turned complex scanner internals into a structured, component-level workflow.
The component diagnostic language was designed as a reusable system, consistent across scanner types and extensible to future hardware without redesigning the entire experience.
Helix shifted diagnostic work from an expert-dependent process to a system-supported workflow. The redesign was preferred by 88% of evaluated users and supported 42% faster task completion, showing that the interface was not only easier to use but better aligned to the operational tempo of airport maintenance.
The platform was compliant with government security standards and compatible with MATLAB, D3.js, and multiple operating systems. Design decisions were grounded in data visualization principles, human factors research, and eye-tracking studies so the interface could perform under high-pressure, time-sensitive maintenance conditions.