AeroMRO — Airline Maintenance Intelligence
An AI-driven airline maintenance (MRO) platform: fleet command centre with real-time alerts, per-aircraft 360° health scoring, DGCA compliance tracking, and an AI copilot that turns repeat defects into work orders.
Role
Sole developer — Lexodd Hypernova
Timeline
2025 — Present
Stack
Next.js / TypeScript / Supabase
Year
2026
Fig. 01 — AeroMRO — Airline Maintenance Intelligence
The problem
Airline maintenance control runs on spreadsheets and tribal knowledge. Defects repeat across the same airframe without anyone connecting them, compliance deadlines are tracked by hand, and raising a work order means chasing parts availability and technician rosters across separate systems.
The approach
Modelled the fleet, defects, work orders, parts and compliance items in PostgreSQL with row-level security so each operator sees only its own data. Built a real-time fleet command centre on Next.js 14 with Supabase subscriptions, then layered a 360° per-aircraft health score over defect history, deferrals and utilisation. The AI copilot runs on the OpenAI API over the structured defect record — it detects repeat defects and drafts a work order with parts reserved and a technician assigned, which a controller approves rather than authors.
The outcome
Maintenance controllers get one screen for fleet status, compliance and defect history, and work-order creation drops from a multi-system chase to a single reviewed suggestion. Delivered solo, from data model to production.