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Applied research 03 · Aviation

Recover the network without losing the reason.

Aviation is a moving constraint system: aircraft, crew, maintenance, slots, weather, baggage, cargo, passenger obligations, and safety. AI is useful when it can compose those facts into options that an accountable operator can inspect.

Decision domainOperations control, engineering, service recovery, and cargo
Evidence surfaceFlight state, crew, maintenance, airport, weather, booking, baggage
Control boundaryRank and coordinate; licensed and operational roles authorize

Our point of view

The best plan is the one operations can still execute.

A mathematically attractive recovery can fail because it ignores a duty-time edge, maintenance limitation, stand constraint, transfer minimum, or passenger obligation. We represent those dependencies explicitly and keep hard safety and regulatory rules outside probabilistic generation.

The model explains the disruption; the graph understands the network; the constraint solver proves feasibility; the agent prepares and coordinates the selected response. Every recommendation shows who and what it affects.

Design principleGenerate options freely. Test feasibility deterministically. Let the accountable controller choose.

These are applied research patterns, not descriptions of completed customer engagements. Named organizations below are public sector signals, not Zustis clients.

Applied research outputs

Three moments where context is the product.

Each pattern coordinates across existing systems while keeping safety-critical authority with qualified people.

01

Network disruption recovery

Rank recoveries by feasibility, consequence, and reversibility.

This research pattern structures disruption state as a dependency graph and defines recovery packages that could show aircraft, crew, slots, connections, passenger impact, cost, and downstream risk together.

The decision

Delay, swap, cancel, ferry, reaccommodate, or protect—and in what sequence?

The evidence

Tail assignment, crew legality, maintenance state, NOTAM/weather, gates, slots, rotation, passenger connections, bags, cargo, and curfews.

System behavior

Detects the disruption, builds feasible options through a constraint engine, simulates network propagation, and returns ranked trade-offs with evidence.

Human boundary

Operations control approves the recovery. The system cannot release a flight, assign illegal crew, or waive a safety constraint.

02

Maintenance evidence

Put the governing technical evidence beside the defect.

The reference design connects free-text tech logs and inspection evidence to aircraft configuration, maintenance programme, MEL/CDL, task cards, engineering orders, and comparable history.

The decision

What evidence and approved task path should the licensed engineer review for this defect?

The evidence

Tech logs, fault messages, BITE data, photos/drone imagery, maintenance history, parts, manuals, task cards, MEL/CDL, and engineering orders.

System behavior

Normalizes terminology, identifies applicability and revision, retrieves cited technical passages, flags contradiction, and drafts a review pack.

Human boundary

Licensed personnel diagnose, defer, rectify, certify, and decide airworthiness. The agent never returns-to-service an aircraft.

03

Baggage, cargo & passenger exceptions

Resolve the broken journey as one case, not three queues.

This research pattern joins scan events, connection risk, load and capacity, special handling, cargo commitments, and service policy so teams could act before an exception becomes a claim.

The decision

Recover, reroute, hold, offload, notify, compensate, or escalate—which action preserves the journey and obligations?

The evidence

PNR, ticket rules, bag scans, ULD/cargo state, transfer time, load control, station capability, SLA, disruption cause, and contact preference.

System behavior

Predicts connection failure, applies eligibility rules, proposes available recovery, generates multilingual communication, and tracks completion.

Human boundary

High-value cargo, safety/security exceptions, policy waivers, and contested compensation route to authorized teams.

Reference architecture

A live operational twin with hard edges.

The network picture stays probabilistic where prediction helps and deterministic where feasibility and safety require it.

01 · Observe

Event fabric

Operational messages, schedules, technical records, customer events, and external conditions share one time axis.

02 · Relate

Aviation graph

Flight, tail, component, crew, airport, passenger, bag, cargo, rule, and obligation become connected state.

03 · Prove

Constraint layer

Safety, legality, maintenance, capacity, transfer, and policy rules eliminate infeasible actions.

04 · Coordinate

Operations agents

Role-specific agents prepare decisions and synchronize approved steps across OCC, engineering, airport, and service teams.

The line we do not cross.

  • AirworthinessNo model determines or certifies airworthiness; applicable technical evidence is prepared for licensed review.
  • Operational releaseNo autonomous dispatch, flight release, crew legality waiver, load-control release, or safety-rule override.
  • Current stateOptions expire as aircraft, crew, weather, airport, or network conditions change.
  • One audit trailInputs, constraints, options rejected, human edits, approvals, notifications, and completion events remain replayable.

Validation before autonomy

Replay the irregular days.

A sunny-day schedule proves little. Historical disruptions reveal whether the system respects the edge cases and changing state.

  1. 01

    Replay

    Reconstruct historical disruptions and defects from timestamped data without using the final resolution as an input.

  2. 02

    Stress

    Test cascading delays, incomplete messages, simultaneous defects, language ambiguity, stale weather, and system outage.

  3. 03

    Shadow

    Compare feasibility, time-to-option, missed constraints, and human preference alongside current teams.

  4. 04

    Delegate narrowly

    Automate only approved low-risk preparation and notification steps; monitor overrides and drift continuously.

What earns the right to scale.

Feasibility
No recommended option violates safety, legality, maintenance, capacity, or airport constraints.
Evidence fidelity
Technical and policy claims resolve to correct, applicable, current revisions.
Recovery utility
Controllers receive usable options earlier without losing downstream consequence visibility.
Abstention
The system escalates novel, contradictory, security-sensitive, or low-confidence cases correctly.

Public sector signals

A region already operationalising AI.

  1. Emirates Group collaboration on enterprise AI

    Public commitment to practical use cases across operations, commercial functions, and customer experience.

  2. Emirates and Boeing digital maintenance partnership

    Regional signal for drone inspection, immersive tooling, and predictive maintenance.

  3. UAE GCAA quality and AI management certifications

    Governance context for deploying AI inside a safety-led aviation system.

  4. ICAO global safety and security framework

    International context for keeping safety accountabilities explicit as digital systems evolve.

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