Ascentia Design System
Built on our expertise with OEM systems and data intelligence, uniting advanced software and aircraft technology into one intelligent platform.
Problem Statement
Aircraft health information is scattered across disconnected systems, historical repair knowledge is hard to retrieve, and predictive intelligence to prevent recurring failures is lacking. As a result, maintenance becomes:
•Reactive instead of preventive•Aircraft downtime increases•Operational costs rise•Avoidable delays and cancellations
Solution
A unified predictive maintenance platform that consolidates aircraft health, maintenance history, and operational data into a single intelligent workspace. It uses advanced analytics and AI-driven insights to proactively identify failures, enable faster decisions, and reduce downtime while improving operational efficiency.
Key Results
Reduction in maintenance
Annual Operational CostsImprovement in maintenance
Predictive AccuracyFaster fault diagnosis
Investigation TimeDiscovery & Research
Conducted stakeholder interviews with maintenance engineers, reliability managers, and MRO planners across multiple operators to map the fragmented aircraft-health landscape and uncover why failures keep recurring.
Pain Points — Voice of the Engineer
- Aircraft health data is scattered across 6+ disconnected OEM systems.
- We find out about faults after they have already caused delays.
- Historical repair knowledge lives in tribal memory and paper logs.
- Recurring failures repeat because no one can spot the pattern.
- Diagnosing a fault means cross-referencing manuals, logs, and sensors across systems.
- There is no predictive intelligence to warn us before a failure happens.
Key Insights
Teams respond to failures instead of preventing them, driving up aircraft-on-ground events and unplanned downtime.
Repair history and expertise are locked in siloed OEM systems and senior engineers’ heads, inaccessible when needed most.
Nothing connects historical failure patterns to future risk — the same faults recur because the pattern is never seen.
Empathy
Three distinct user groups — each racing against downtime, each blocked by a different gap in the current workflow.
David
Senior Maintenance Engineer"I spend half my diagnosis time hunting for data across systems instead of fixing the aircraft."
Priya
Fleet Reliability Manager"I can see what broke, but never what will break next. Everything is a lagging indicator."
Marcus
MRO Planner"Unplanned work destroys my schedule. If I knew a part would fail, I could plan around it."
Journey Mapping
Mapping the current-state fault investigation flow revealed fragmentation at every step — from detection through resolution, knowledge is never captured for the next time.
Current-State Journey — Fault Investigation
Define
How might we unify fragmented aircraft-health data, repair history, and operational telemetry into a single intelligent workspace that predicts failures before they ground an aircraft?
Success Metrics
Business KPIs
Ideation
Instead of another reactive dashboard, we designed a predictive maintenance platform with AI-driven intelligence at its core.
Solution Directions
Ideation Concepts
Aircraft Overview
A unified fleet status view showing every aircraft’s health score, active alerts, and next service at a glance.
Prognostic Health Monitoring
Predictive failure insights with probability, time-to-failure, and confidence per affected system.
Issues Investigation
Guided fault diagnosis linking fault codes to history, sensor data, manuals, and a recommended fix.
Maintenance Record Details
Digital repair history with parts, technician, recurrence flags, and full audit trail per work order.
AOG Board
A live critical-events board tracking every aircraft-on-ground situation to resolution with ownership and ETA.
Design System
A unified token-based design system — color styles, typography, and a component library purpose-built for the dense, data-rich surface of aircraft maintenance.
Color Tokens
Component Library
Annotated hotspots reveal the interaction logic, design decisions, and user research findings embedded in each element of this interface.
