Redis Real-Time Decisioning Demo | Travel Disruption Recovery

Stage 1: The Architecture

SkyBridge Travel's Real-Time Decisioning Stack

Five tiers form a continuous loop: ingest, context, decide, act, learn. Operational context matters most when the traveler’s plans are breaking in real time. Systems of record stay where they are. Redis becomes the operational context layer that makes the moment actionable.

Data Sources

Reservation System

Operational source

Departure Control System

Operational source

Loyalty Platform

Operational source

Irregular Operations Feed

Operational source

Hotel / Ground Partner APIs

Operational source

Kafka / Trip Events

Operational source

Ingest Layer

Redis Data Integration (RDI)

CDC from repositories and operational databases with sub-second activation

Redis Feature Form + Streams

Feature serving and live event hydration from Kafka, Redis Streams, and industry APIs

Unified Context Layer

Redis RAM

Hot itinerary state, live flight operations, active disruption events, and rebooking inventory

Redis Flex

Warm travel history, loyalty context, prior disruption recovery patterns, and preference embeddings

Feature Store

Rebooking priority, compensation eligibility, churn risk, and upgrade availability features

Redis Context Retriever

Assembles the Traveler 360 — booking state, disruption history, and loyalty context — and exposes it as structured MCP tools for the decision engine

Decision Engine

Eligibility Rules

Policy, compliance, and inventory constraints

NBA Ranker

Contextual ranking weighted by value, risk, and fit

Policy Arbitration

One action selected for the current moment

Output Channels (Act)

Mobile App

Customer-facing activation surface

Airport Kiosk

Customer-facing activation surface

Agent Desktop

Customer-facing activation surface

SMS / Email

Customer-facing activation surface

↺

Learn: Decision logs and outcomes stream back into model training, policy updates, and the Redis context layer.

Decision latency

12.7 ms

Call deflection

-33%

Recovery satisfaction lift

+19 pts

Stage 2: Trigger Event

Sophia Turner creates a decision moment

A flight cancellation triggers a re-accommodation decision for a loyalty traveler mid-itinerary. Rebook, compensate, and message the traveler before they queue up or call the contact center.

Live Trigger

Sophia Turner

Elite traveler | 2-leg itinerary | connection cancelled due to weather

TRAVEL

Eventflight_cancelled

Customer surfaceMobile app

Decision objectiveRecovery + loyalty retention

Decision windowBefore the surface finishes rendering

Context requiredHistory + live signals + policy

Business stakes-33%

Why This Moment Matters

Operational context matters most when the traveler’s plans are breaking in real time.

Without Redis: the application waits on siloed repositories and defaults to a generic next step.

With Redis: the decision surface opens with ranked, contextual action already staged for the moment.

Stage 3: Ingest

Industry repositories and streams flow into Redis

These are common repositories and streaming APIs for travel decisioning. They remain the systems of record. Redis activates the working set needed to decide now.

Redis Data Integration (RDI)Redis Feature Form

Source Systems → Redis

SABR

Sabre / Amadeus

Common repository or streaming source for sabre context

→

OPSE

Ops event bus

Common repository or streaming source for ops context

→

LOYA

Loyalty DB

Common repository or streaming source for loyalty context

→

HOTE

Hotel and rideshare APIs

Common repository or streaming source for hotel context

→

KAFK

Kafka / Redis Streams

Event streaming via Kafka or Redis Streams — no separate broker required with Redis Streams built-in

→

WEAT

Weather / disruption APIs

Common repository or streaming source for weather context

→

Ingest Pipeline Status

CDC modeReal-time

Streaming lag<100 ms

Feature parityTrain = serve

Custom sync code0 lines

Serving roleOperational context layer

Stage 4: Context Assembly

History and live state converge into one working view

Redis assembles profile history, traveler or booking state, live intent, and policy constraints in one low-latency lookup path. What looked like a simple event in Stage 2 becomes a much richer decision moment here.

Redis RAMRedis FlexRedis Context Retriever

Historical Context

Customer value bandelevated

Tenure / relationship depthEstablished

Prior interaction patternKnown baseline available

Eligibility stateResolved in memory

Policy constraintsCurrent and active

Action historyFrequency caps enforced

Live Context

Current intentRecovery + loyalty retention

Streaming signal stateFresh for this session

Capacity / inventoryAvailable

Risk / complianceWithin active rules

Surface readinessMobile app

Decision scopeAuto-rebook + meal + lounge message

Key insight: Redis Context Retriever assembles the Traveler 360 — booking state, disruption history, and loyalty context — so the decision engine has exactly what it needs. The winning action becomes obvious only when historical context and live signals appear in the same response path.

Stage 5: Feature Serving

Online features hydrate the ranker in milliseconds

Redis Feature Form serves model-ready signals from Redis with train-serve parity. No fan-out calls to downstream systems during decisioning. No stale fallbacks.

Redis Feature FormRedis Feature Store

elite_tier

Real-time feature served from Redis with train-serve parity.

platinum0.3 ms

misconnect_risk

Risk and policy factor used to gate the action.

high0.4 ms

partner_inventory

Real-time feature served from Redis with train-serve parity.

available0.5 ms

airport_congestion

Real-time feature served from Redis with train-serve parity.

elevated0.6 ms

compensation_budget

Real-time feature served from Redis with train-serve parity.

within policy0.7 ms

trip_value_score

Model or ranking signal pulled online at decision time.

0.880.8 ms

Feature Serving Performance

Features Hydrated

186

P99 Lookup Latency

<15ms

Train / Serve Parity

100%

Stage 6: Ranking

Candidate actions are ranked for this moment

Rules, policies, vector similarity, and business weighting combine to rank the best action now. The winner is selected because it fits the moment, not because it is the easiest generic fallback.

Redis SearchNBA RankerRules Engine

#1 Winner

PRIMARY ACTION

Auto-rebook + meal + lounge message

Best arrival outcome under current capacity constraints

NBA score0.94

#2 Candidate

ALTERNATIVE

Travel credit only

Cheaper now, but more likely to trigger dissatisfaction

NBA score0.79

Suppressed

POLICY

Standby on partner carrier

Suppressed because misconnect risk remains too high

Suppressed0.24

Stage 7: Business Impact

The value of choosing the right action now

Fast decisions matter, but the real value is choosing the right action while the moment is still open. Redis improves both latency and outcome quality.

Decision Economics

Decision latency12.7 ms

Call deflection-33%

Recovery satisfaction lift+19 pts

The key insight: value compounds when the decision surface uses live operational context instead of generic fallback logic.

Moment Outcome

Generic

Siloed systems
lower relevance

→

Redis

Context-aware
higher impact

Stage 8: Outcome

Same surface. Different decision layer.

On the left is a generic or delayed path. On the right is the Redis-powered experience with ranked action already staged. Same customer moment. Different outcome.

Generic Experience

Sophia Turner

Travel workflow

What the system knows

Partial profile, delayed retrieval, and limited live context.

Action shown

Travel credit only

Outcome risk

The action fits broadly, but not the full moment.

Lower

relevance

Higher

fallback use

Siloed

context

Redis-Powered Experience

Sophia Turner

Decision-ready profile

#1 Next Best Action

Auto-rebook + meal + lounge message

Best arrival outcome under current capacity constraints

Why it wins

Combines history, live signals, business value, and active policy constraints in the same low-latency response.

Visible impact

Call deflection: -33%.

High

relevance

Low

fallback use

Ready

before render

Stage 9: The Architecture, Proven

Architecture first. Architecture last. Outcome in the middle.

The same five tiers you saw at the start now tie directly to a measurable decision outcome. Common repositories and streaming APIs stay in place. Redis remains the operational decisioning layer that makes the customer moment work.

Data Sources

Reservation System

Operational source

Departure Control System

Operational source

Loyalty Platform

Operational source

Irregular Operations Feed

Operational source

Hotel / Ground Partner APIs

Operational source

Kafka / Trip Events

Operational source

Ingest Layer

RDI

CDC and activation from systems of record

Streams + Features

Live event hydration and model parity

Unified Context Layer

Redis RAM

Hot itinerary state, live flight operations, active disruption events, and rebooking inventory

Redis Flex

Warm travel history, loyalty context, prior disruption recovery patterns, and preference embeddings

Feature Store

Rebooking priority, compensation eligibility, churn risk, and upgrade availability features

Redis Context Retriever

Assembles the Traveler 360 — booking state, disruption history, and loyalty context — and exposes it as structured MCP tools for the decision engine

Decision Engine

Rules

Eligibility, compliance, and policy

Ranker

Propensity, value, and fit

Arbitration

One action selected now

Output Channels

Mobile App

Customer-facing activation surface

Airport Kiosk

Customer-facing activation surface

Agent Desktop

Customer-facing activation surface

SMS / Email

Customer-facing activation surface

↺

Learn: Outcomes feed retraining, policy updates, and future decisions without changing the systems of record.

Decision latency

12.7 ms

Call deflection

-33%

Recovery satisfaction lift

+19 pts