The platform

A finite, typed, permissioned set of airline capabilities AI can use safely.

SkyAide is not a chatbot with airline access. It is a governed intelligence layer: connectors that normalize context, a typed action catalogue that AI can call, and a governor that decides whether any material action is allowed to execute.

SRC · PSS

Passenger service

SRC · LOYALTY

Miles & benefits

SRC · OPS

Flight & crew

SRC · PARTNER

Airport & retail

Layer 1

Normalized context

Layer 2

Typed actions

Gate

Trust Governor

EXECUTE

Verify & learn

Platform shape

Decision loop

11

steps · shared by every module

Operating modes

6

diagnostic → bounded automation

Governor gate

100%

of material actions

Decision record

Full

auditable · reproducible

Intelligence

How SkyAide decides

One decision loop, reused across every use case. Discover Options spans airline, alliance, interline, partner, loyalty, airport, catering, and destination.

The decision loop

Observe → Understand → Predict → Discover Options → Simulate → Value → Recommend → Approve → Execute → Verify → Learn.

Value principle. SkyAide must determine what is best for this passenger, this airline, and this journey, not merely what is most likely to be purchased. The system may recommend a service, an operational action, a commercial offer, a partner option, the use of an existing benefit, or no action.

Foundation

One shared foundation

Every capability reuses the same connectors, identity model, consent logic, approval workflow, execution path and audit trail. Nothing is rebuilt per feature, so what your security team reviews once holds everywhere.

  • 01 · Data

    Connector ingest & normalized context

    Airline, loyalty, partner, traveler-authorized, operational, commercial, and external sources normalized into a canonical domain model that carries field-level provenance, freshness stamps, and conflict detection.

    systems · PSS · loyalty · ops · partner · traveler

    read-only shadow

  • 02 · Data

    Connector synthesis & certification

    AI-drafted connectors pass contract, semantic, sandbox, adversarial, security, reliability, and human-review gates before promotion. Dual-run comparison protects live traffic.

    gates · 7 · promotion · human-signed

    diagnostic shadow

  • 03 · Actions

    Typed action catalogue

    Every action is a first-class product object: typed inputs and outputs, risk class, permission scopes, preconditions, approval requirements, idempotency behavior, retry, dry-run, postconditions, compensating actions, and versioning.

    risk classes · low / material / severe

    recommendation approval bounded auto

  • 04 · Identity

    Identity, entitlement & resolution

    Resolve one traveler across PSS, loyalty, partner, and consent stores, applying authority precedence, entitlement roll-ups, and cross-system deduplication.

    precedence · PSS > loyalty > partner

    read-only

  • 05 · Context

    Decision-readiness assessment

    Before any recommendation is offered, SkyAide grades the context: confidence, freshness, coverage, provenance. Insufficient context routes to a human, not to a guess.

    gate · configurable confidence / freshness / coverage floors

    diagnostic

  • 06 · Governor

    Trust Governor & deterministic policy engine

    AI interprets and predicts. A deterministic policy engine, not the model, decides whether a material action executes. Every approval, denial, and override is auditable and reproducible.

    gate · preconditions + scope + risk + approver + dry-run · audit · immutable

    approval bounded auto

  • 07 · Measure

    Incrementality & uplift measurement

    Every intervention is measured against a counterfactual. SkyAide learns which recommendations actually moved traveler value and airline cost, not which ones were merely clicked.

    method · holdout + doubly-robust

    shadow

  • 08 · Learn

    Privacy-preserving cross-customer learning

    The economic learning moat compounds without leaking data between airlines. Federated updates, differential privacy budgets, per-tenant isolation.

    technique · federated + DP-SGD · ε budget · per-tenant

    read-only
Journey & value

Traveler journey & value

The capabilities that ride on that foundation. Each one is an assembly of typed actions, gated by the Trust Governor, and measured against a counterfactual.

  • 01 · Cater

    AI Meal, Dietary & Catering Intelligence

    Predict meal acceptance from route, cabin, tenure, prior consumption, dietary constraints, and cultural context. Optimize galley loads for waste, cost, and traveler value.

    signals · route, cabin, tenure, culture, dietary

    recommendation bounded auto

  • 02 · Offers

    Unified airline + partner offer graph

    Every eligible offer across airline, alliance, interline, partner, loyalty, and airport, expressed as nodes and edges in one graph the value optimizer can traverse.

    scope · alliance + interline + partner + airport

    read-only

  • 03 · Value

    AI Journey Value Optimizer

    Multi-objective optimization across passenger utility, airline margin, partner obligation, and operational cost. Outputs a ranked slate, not a single guess.

    objectives · 4 · pareto-ranked slate returned

    recommendation approval

  • 04 · Recover

    Disruption Recovery Optimizer

    When a flight breaks, evaluate the option space one passenger at a time across rebooking, rerouting, refunds, hotels, ground transport, rail, and meals, ranked by joint traveler and airline value.

    granularity · per passenger, not per flight

    recommendation approval bounded auto

  • 05 · Loyalty

    Loyalty currency & benefit optimizer

    Recommend the best use of miles, credits, upgrades, and status benefits for each traveler, including the recommendation to hold rather than spend.

    horizon · journey + forward window · includes "do not spend"

    recommendation

  • 06 · Passport

    Traveler preference memory & consent layer

    A portable preference passport the traveler owns. Opt-in, cross-airline, revocable per scope, per party, per purpose.

    consent · per scope · per party · per purpose · revocable

    read-only traveler confirmation

  • 07 · Causal

    Causal offer & intervention optimization

    Learns which interventions caused traveler value or airline lift rather than which ones merely correlated with it. Uplift first, not clicks.

    method · doubly-robust causal forests · per-tenant holdouts

    shadow recommendation

See it run on your airline.

Connect a shadow tenant to your PSS, loyalty, and operational feeds. Governed recommendations in three weeks; measured uplift in six.

Book a demo

The Trust Governor

AI interprets. A deterministic engine decides.

The objective is not to give AI broad system access. It is to give it a finite, typed, permissioned and auditable set of airline and partner capabilities it can use safely. Every material action clears the preconditions, permission scopes, risk class, dry-run cost, human approver and audit trail before it runs.

A second, independent governor then checks the decision itself. It enforces consent and purpose, transparency, contact frequency, suitability, value floors, non-discrimination, accessibility and price fairness, and it screens for proxy discrimination, unjustified differential treatment and dark patterns. Nothing that reaches a passenger executes without a reproducible reason.

Sample decision trace worked example

08:41:22 recovery · rebook_pax(A.Nguyen) approved · Δcost −$142

08:41:20 value · rank_slate(SIN-LHR) recommend · conf 0.94

08:41:17 cater · predict_meal(J.Osei) predict · halal · veg

08:41:14 loyalty · loyalty_hold(K.Ito) hold · do not spend

08:41:11 context · readiness_check route to human · insufficient

08:41:08 group · solve_group(family) recommend · seats +3

08:41:04 recovery · reroute(M.Silva) approved · via LIS · Δ +$38

08:41:01 offers · offer_graph.build(HND) read · 1,204 edges

How one action is evaluated worked example
action
rebook_pax
module
disruption recovery
risk_class
material
preconditions
3 / 3 · pass
confidence
0.94
fairness check
12 / 12 · pass
approver
ops.duty_manager
dry_run
Δcost −$142 · Δvalue +$38
decision
approved · executed
verified_at
08:41:22.417Z

Security & data protection

Airline-grade by architecture, not by assertion.

SkyAide is designed for security review. Every control below is a property of how the platform is built, so it is verifiable during evaluation, before any passenger record is in scope.

  • Tenant isolation by construction

    Every airline is a separate tenant boundary. Cross-customer learning moves model updates under a differential privacy budget. Records never move.

  • Least-privilege typed actions

    AI is never granted broad system access. It may call only catalogued actions, each carrying explicit permission scopes and a risk class.

  • Immutable decision ledger

    Every recommendation, approval, denial, override and execution is written to an append-only audit record that can be replayed.

  • Deterministic policy gate

    A model never decides whether a material action runs. A deterministic engine evaluates preconditions, scopes, approver eligibility and dry-run cost.

  • Data minimisation & consent

    Traveler preference data is opt-in and revocable per scope, per party, and per purpose. Consent state is enforced at the action gate.

  • Deploy inside your perimeter

    SkyAide starts read-only and in shadow. Nothing touches a passenger record until your ops and security teams sign off the Governor.