Services

Nine services, mapped to the airline value chain.

Airlines don't buy "data and AI" — they buy accurate fare quotes, higher yield, faster disruption recovery, refunds that clear themselves, aircraft that stay in service and a revenue-accounting close that finishes on time. Below, each SCIKIQ service line is mapped to the commercial, operational and group domains where it does that work, with the use cases, the KPIs it moves and the accelerators you can open today.

See the value-chain map How we add value, by domain Find your service by role
Chapter 2 · Services on the value chain

Where each service line works on the airline value chain

Read across a row to see where a service line leads and where it supports. Read down a column to see the team an airline gets in that domain. Select any domain to see use cases, the KPIs we help move and the working accelerators.

9
Service lines
8
Value-chain domains
17
Lead roles across the map
34
Supporting roles across the map
Leads the work Supports Hover a dot for detail · select a domain to explore it
01 · Advise
Data & AI StrategyStrategy
Data Governance, Privacy & Regulatory DataGovernance
02 · Build
Data Engineering & Platform ModernisationData platform
AI & Agentic EngineeringAI & agents
03 · Transform
Commercial, Distribution & RevenueCommercial
Customer, Loyalty & ServiceCustomer
Operations & Disruption ManagementOperations
Revenue Accounting & FinanceFinance
04 · Run
Managed Services: DataOps, MLOps & AgentOpsManaged
Service lines engaged 6 6 8 8 7 5 6 5

The mapping shows where each service line typically leads or supports; every engagement is scoped to the airline. The value chain itself is explained on the overview.

How we add value

Domain by domain: from data to a measurable outcome

Each domain follows the same path — source data, a governed data product, AI and agents, an outcome the business measures. KPIs are the measures we help you move and track; we agree targets with you, we don't promise them in advance.

Domain 1 of 8

Network & schedule planning

Fleet, slots and demand shift every season, and every new route, hub bank or partner is a bet made on partial data and spreadsheet schedules.

Data
Schedules, slots, O&D demand & fleet
Data product
Network & route P&L data product
AI & agents
Demand, scenario & connection models
Outcome
Better routes, tighter hub banks

Airline use cases

  • Route profitability and O&D demand forecasting
  • Schedule scenarios within fleet, crew and slot constraints
  • Hub connection-bank and minimum-connection-time analysis
  • Partner and virtual-interline opportunity analysis on the flight-network graph

KPIs we help you move

Route profitabilityConnecting-passenger shareAircraft utilisationSchedule build cycle time

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Virtual Interlining Engine COMPASS
Domain 2 of 8

Pricing & revenue management

Dynamic offers, continuous pricing and ancillaries move faster than fare-class systems, and analysts spend the day on overrides and competitor checks.

Data
Bookings, fares, inventory & competitors
Data product
Demand & pricing data product
AI & agents
Forecast, price & offer models
Outcome
Higher yield and load factor

Airline use cases

  • Demand forecasting and willingness-to-pay by segment
  • Competitor fare monitoring with pricing actions recommended
  • Ancillary and bundle offer optimisation
  • Group-request pricing with the rationale drafted

KPIs we help you move

Revenue per available seat kmLoad factorAncillary revenue per passengerForecast accuracy

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working CATALYST
Domain 3 of 8

Distribution, NDC & retailing

Content flows through GDS, NDC and direct APIs at once, and every carrier writes fare rules differently — so penalties are quoted wrongly and multi-carrier itineraries go unsold.

Data
GDS, NDC, direct APIs & ATPCO fare rules
Data product
Unified offer & fare-rule data product
AI & agents
Fare-rule GenAI & interlining graph
Outcome
Accurate quotes, more itineraries sold

Airline use cases

  • GenAI extraction of penalties and conditions from unstructured fare rules
  • Virtual interlining with MCT, terminal and visa checks
  • Offer normalisation across GDS, NDC and direct content
  • Conversational shopping and agent-assist search

KPIs we help you move

Penalty-quote accuracyFare-rule disputesNew-carrier onboarding timeLook-to-book conversion

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Fare Rules Intelligence Virtual Interlining Engine Distribution Data Hub
Domain 4 of 8

Customer, loyalty & service

Passengers expect to rebook, claim a refund and use their status without calling; contact centres absorb every disruption peak and refund rules differ by market.

Data
PNRs, loyalty, transcripts & refunds
Data product
Passenger 360 with consent
AI & agents
Rebooking, refund & service agents
Outcome
Faster resolution, loyal passengers

Airline use cases

  • Proactive re-accommodation and notifications when the schedule changes
  • Refund and compensation eligibility with the decision drafted
  • Customer 360 and next-best-offer for loyalty members
  • AI-assisted service across chat, voice and WhatsApp

KPIs we help you move

Refund cycle timeContact-centre cost per contactSelf-service resolution rateNet promoter score

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working CATALYST Run an agent live
Domain 5 of 8

Flight operations, crew & OCC

The operations control centre recovers from disruption by phone and spreadsheet, with crew legality, rotations and passenger connections solved in separate systems.

Data
Movements, rosters, rotations & weather
Data product
Real-time operations data product
AI & agents
Disruption, crew & fuel models
Outcome
Fewer delays, faster recovery

Airline use cases

  • Disruption prediction with recovery options ranked by cost and passenger impact
  • Crew pairing and reserve optimisation within legality rules
  • Fuel-efficiency and tankering analytics
  • Delay root-cause analysis from delay codes and milestones

KPIs we help you move

On-time performanceDisruption cost per departureCrew reserve utilisationFuel burn per block hour

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Control Tower
Domain 6 of 8

Airports, ground handling & cargo

A turnaround involves a dozen handlers working to the minute, while bags, cargo and passengers are tracked in different systems.

Data
DCS, baggage messages, milestones & AWBs
Data product
Turnaround & baggage data product
AI & agents
Turnaround risk & baggage models
Outcome
On-time turns, fewer mishandled bags

Airline use cases

  • Turnaround tracking with on-time-departure risk flagged early
  • Mishandled-baggage prediction and recovery
  • Cargo capacity forecasting and booking-quality checks
  • Gate and stand allocation support

KPIs we help you move

Turnaround punctualityMishandled bags per 1,000 passengersCargo load factorHandler SLA compliance

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Control Tower RPA Automation
Domain 7 of 8

Engineering & maintenance (MRO)

Unscheduled removals ground aircraft and maintenance records are partly free text, while airworthiness rules demand that every step is traceable.

Data
Sensor data, tech logs, task cards & parts
Data product
Aircraft health data product
AI & agents
Predictive & GenAI text models
Outcome
Fewer AOG events, audit-ready records

Airline use cases

  • Predictive maintenance from sensor and defect trends
  • Tech-log and defect text classified with GenAI
  • Parts demand forecasting and inventory optimisation
  • Directive and bulletin applicability checks with evidence attached

KPIs we help you move

Technical dispatch reliabilityUnscheduled removalsAircraft-on-ground hoursParts inventory value

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working Data Governance Hub Agent governance
Domain 8 of 8

Revenue accounting, finance & compliance

Tickets, coupons, EMDs, settlements and interline billing are reconciled by hand; agency debit memos, refunds and payment fraud leak margin, and emissions reporting adds numbers to defend.

Data
Tickets, coupons, BSP / ARC & the GL
Data product
Governed revenue data product
AI & agents
Matching, integrity & commentary agents
Outcome
A faster close, less leakage

Airline use cases

  • Coupon-level revenue recognition and interline proration
  • Settlement and interline reconciliation with breaks explained
  • Agency debit memo, refund and payment-fraud detection
  • Route P&L and emissions reporting with AI commentary

KPIs we help you move

Days to closeAuto-match rateRevenue leakage recoveredUnflown revenue accuracy

Named measures, not promised results. Baselines and targets are set with you in the assessment.

See it working CLARION Revenue Assurance COMPASS NARRATOR
Find your service

Start from your role

Each service line has a clear owner on the client side. Pick yours to jump to the services most teams like yours start with.

CCO
Chief Commercial Officer
COO
Chief Operating Officer
CFO
Chief Financial Officer
CDO
Chief Data / Digital Officer
CIO
Chief Information Officer
Travel tech
OTAs, TMCs, GDS providers & distributors
01

Advise

Set direction, make the business case and put the rules in place that data and AI must meet.

Advise · Service line

Data & AI Strategy

ForCDOCIOCCO
On the value chainNetwork & schedulePricing & revenueDistribution & NDCCustomer & loyaltyFlight ops & crewEngineering & MROFinance & compliance

The client problem

AI pilots multiply across commercial, operations and the contact centre, but few reach production. There is no shared, evidence-based view of where the airline stands on data and AI, which use cases pay back, or who owns them.

Outcomes

  • A maturity baseline across 12 capability layers and five stages, scored on evidence rather than opinion
  • A prioritised roadmap of use cases across the value chain, with a business case for each
  • An AI operating model: ownership, funding, delivery and controls

What we do

  • Data & AI maturity assessment
  • AI strategy & use-case prioritisation
  • AI operating model & centre of excellence design
  • Business case & value tracking

Typical engagements

  • AssessmentMaturity assessment and roadmap
  • Pilot · 30-45 daysProve the top-ranked use case on your data
  • BuildRoadmap delivered through our Build and Transform services
  • Managed runValue tracking and roadmap refresh

Delivered with

Maturity assessment Value calculator SCIKIQ Data Fabric
Advise · Service line

Data Governance, Privacy & Regulatory Data

ForCDOComplianceSafety & quality
On the value chainDistribution & NDCCustomer & loyaltyFlight ops & crewAirports & cargoEngineering & MROFinance & compliance

The client problem

Passenger data sits in many systems under privacy, payment-card and passenger-data-to-authorities rules. Refund, emissions and airworthiness reporting want proof of where a number came from, and AI adds a new layer to govern.

Outcomes

  • A working CDO function and data operating model
  • Critical data elements with owners, lineage and data quality controls for regulatory reports
  • AI and agent governance: policy, approvals, evaluation and a kill switch

What we do

  • CDO set-up & operating model
  • Passenger-data privacy & consent controls
  • Lineage for refund, emissions and airworthiness reporting
  • Metadata catalogue & data quality rules
  • AI & agent governance

Typical engagements

  • AssessmentGovernance and lineage gap review against regulatory reports
  • Pilot · 30-45 daysCatalogue, lineage and DQ for one regulatory report
  • BuildCDO function, metadata repository and controls, airline-wide
  • Managed runOngoing DQ monitoring and catalogue stewardship

Delivered with

Data Governance Hub SCIKIQ Data Fabric Agent governance
02

Build

Engineer the data platforms and the AI that runs on them, with governance built in.

Build · Service line

Data Engineering & Platform Modernisation

ForCIOCDO
On the value chainNetwork & schedulePricing & revenueDistribution & NDCCustomer & loyaltyFlight ops & crewAirports & cargoEngineering & MROFinance & compliance

The client problem

Data sits across the reservation system, departure control, GDS and NDC channels, operations, MRO and finance platforms in XML, JSON, EDIFACT and free text. Every new report or model starts with another bespoke extract.

Outcomes

  • One governed, cloud-agnostic data platform on Azure, AWS or hybrid
  • Batch and streaming pipelines with lineage from source, including GDS, NDC and airline-direct content
  • Repeatable environments deployed with Infrastructure as Code

What we do

  • Pipelines, batch & streaming
  • GDS, NDC & airline-direct API integration
  • Airline data models: PNR, ticket, coupon, flight, crew, aircraft
  • Lakehouse / warehouse on Azure or AWS
  • Infrastructure as Code & legacy modernisation

Typical engagements

  • AssessmentData estate and target-architecture review
  • Pilot · 30-45 daysConnect and curate priority sources end to end
  • BuildPlatform build in sprints, tested with real data
  • Managed runDataOps under agreed SLAs

Delivered with

SCIKIQ Data Fabric 268 data sources Distribution Data Hub
Build · Service line

AI & Agentic Engineering

ForCIOCOOCCO
On the value chainNetwork & schedulePricing & revenueDistribution & NDCCustomer & loyaltyFlight ops & crewAirports & cargoEngineering & MRO

The client problem

GenAI and agent pilots stall at the controls review: no autonomy limits, no audit trail, no named owner for the exceptions — and in an airline, no tolerance for an unexplained decision.

Outcomes

  • Supervised agent squads in production, with autonomy and guardrails set per agent
  • GenAI that turns free text — fare rules, tech logs, transcripts — into structured data
  • People review only the exceptions, with the agent's draft and evidence in front of them

What we do

  • Agent design: roles, squads & autonomy levels
  • GenAI document & text extraction (LLM serving, NLP)
  • Graph & optimisation models for networks and routing
  • Conversational interfaces: chat, voice & WhatsApp
  • Evaluation & guardrails: policy, limits, kill switch

Typical engagements

  • AssessmentAgent opportunity and controls review
  • Pilot · 30-45 daysOne agent squad working real cases under your controls
  • BuildSquads integrated with your systems and scaled
  • Managed runAgentOps: monitoring, overrides, drift

Delivered with

Fare Rules Intelligence Virtual Interlining Engine Run an agent live Governance & controls
03

Transform

Domain practices that change how an airline function works, end to end, with our accelerators as the starting point.

Transform · Service line

Commercial, Distribution & Revenue

ForCCOHead of distributionRevenue management
On the value chainNetwork & schedulePricing & revenueDistribution & NDCCustomer & loyalty

The client problem

Content flows through GDS, NDC and direct APIs at once, and each of 120+ airlines writes fare rules differently. Penalties are quoted wrongly, disputes follow, multi-carrier itineraries go unsold and pricing moves faster than the tools.

Outcomes

  • Accurate penalty quotes at the point of booking, across every carrier
  • Multi-carrier itineraries sold outside traditional interline agreements, with every connection checked
  • Pricing and network decisions made on one view of demand, fares and competitors

What we do

  • Fare-rule & penalty extraction (ATPCO CAT 16, 19, 31, 33)
  • Virtual interlining & route optimisation
  • NDC offer & order retailing
  • Demand forecasting & price intelligence
  • Network & schedule analytics

Typical engagements

  • AssessmentDistribution, pricing and fare-rule diagnostic
  • Pilot · 30-45 daysFare rules for your top carriers, or one O&D market for interlining
  • BuildDistribution hub, retailing and RM analytics rollout
  • Managed runModel and pipeline operations under SLA

Delivered with

Fare Rules Intelligence Virtual Interlining Engine Distribution Data Hub CATALYST
Transform · Service line

Customer, Loyalty & Service

ForChief Customer OfficerHead of loyaltyContact centre
On the value chainPricing & revenueDistribution & NDCCustomer & loyaltyFlight ops & crewAirports & cargo

The client problem

Passenger data is split across reservations, loyalty, service and disruption systems, so offers are generic, service volumes spike with every disruption and refunds wait in queues.

Outcomes

  • A single passenger view shared by service, loyalty and marketing
  • Rebooking, refund and compensation cases prepared by agents and approved within policy
  • Self-service that resolves rather than deflects, across chat, voice and WhatsApp

What we do

  • Passenger 360 & loyalty analytics
  • Next-best-offer & ancillary personalisation
  • Refund, EMD & compensation automation
  • Conversational service & agent assist

Typical engagements

  • AssessmentPassenger data and service-use-case review
  • Pilot · 30-45 daysPassenger 360 and one refund or rebooking flow
  • BuildIntegrated loyalty, service and marketing platform
  • Managed runModel monitoring and MLOps

Delivered with

CATALYST Dispute & Chargeback Run an agent live
Transform · Service line

Operations & Disruption Management

ForCOOOCCEngineering & ground ops
On the value chainNetwork & scheduleCustomer & loyaltyFlight ops & crewAirports & cargoEngineering & MRO

The client problem

Disruption is recovered by phone and spreadsheet. Crew, aircraft, ground handling and maintenance data live in separate systems, so problems surface after they have rippled through the network.

Outcomes

  • Disruption recovery options ranked by cost and passenger impact for the duty manager
  • Turnaround, baggage and maintenance risks flagged while there is still time to act
  • Operational automation run as one portfolio from a control tower

What we do

  • Disruption prediction & recovery
  • Crew & aircraft-rotation analytics
  • Turnaround, baggage & cargo monitoring
  • Predictive maintenance & tech-log GenAI
  • Automation control tower & RPA

Typical engagements

  • AssessmentOperations data and disruption-cost review
  • Pilot · 30-45 daysOne hub, fleet or process end to end
  • BuildControl tower and operations data products
  • Managed runBot, model and agent operations under SLA

Delivered with

Control Tower RPA Automation Value calculator
Transform · Service line

Revenue Accounting & Finance

ForCFORevenue accountingControllers
On the value chainNetwork & schedulePricing & revenueDistribution & NDCFinance & compliance

The client problem

Tickets, coupons, EMDs, BSP and ARC settlements and interline billing are reconciled by hand across many systems. Agency debit memos, refunds and payment fraud leak margin, and the close waits on spreadsheets.

Outcomes

  • A faster, cleaner close with automated matching across sales, settlement and the ledger
  • Revenue leakage and payment fraud detected and recovered
  • Route P&L and management commentary drafted by AI and signed off by people

What we do

  • Sales, settlement & interline reconciliation
  • Coupon-level revenue recognition & proration
  • Revenue integrity: ADMs, refunds & fraud
  • FP&A, route P&L & board pack
  • Emissions & regulatory reporting

Typical engagements

  • AssessmentRevenue accounting and close diagnostic
  • Pilot · 30-45 daysOne accelerator on a live settlement or reporting stream
  • BuildReconciliation, revenue integrity or FP&A rollout
  • Managed runRecon and close squads run under SLA

Delivered with

CLARION COMPASS NARRATOR Revenue Assurance Fraud & Authorization SOX Compliance
04

Run

Keep platforms, models and agents healthy and improving after go-live.

Run · Service line

Managed Services: DataOps, MLOps & AgentOps

ForCIOCOO
On the value chainDistribution & NDCCustomer & loyaltyFlight ops & crewEngineering & MROFinance & compliance

The client problem

After go-live, airline APIs change, fare formats shift, models drift and agents need someone watching overrides, limits and evaluation results — around the clock, because airlines never close.

Outcomes

  • Platforms, pipelines, models and agents run under agreed SLAs
  • Continuous improvement driven by override and evaluation data
  • Your teams freed from L2/L3 support

What we do

  • Run & L2/L3 support
  • DataOps, including GDS / NDC connector upkeep
  • MLOps
  • AgentOps: logs, overrides, drift, kill switch
  • Continuous improvement

Typical engagements

  • AssessmentRun-readiness and support model review
  • Pilot · 30-45 daysHypercare for a newly live capability
  • BuildMonitoring, runbooks and SLAs
  • Managed runOngoing service under agreed SLAs

Delivered with

Governance & controls SCIKIQ Data Fabric
Our assets

What makes our services faster

Every engagement starts from SCIKIQ IP rather than a blank page. These assets are how we deliver — they come with the service.

1
SCIKIQ Data Fabric

The governed foundation every engagement runs on: the 4C method (Connect, Curate, Contextualize, Consume), 268 data sources extended with GDS, NDC and airline-direct APIs, and governance, lineage, data quality, an AI/agent layer and security built in — cloud-agnostic on Azure, AWS or hybrid.

Explore the framework
2
Accelerators

Three airline accelerators — Fare Rules Intelligence, the Virtual Interlining Engine and the Distribution Data Hub — plus cross-industry accelerators such as CLARION, COMPASS and NARRATOR, tailored to your rules, data and controls.

Accelerators by service line
3
Supervised digital workforce

AI agents that plan, call tools and gather evidence on the governed data. Policy decides what goes straight through; people approve everything else; every step is logged and a kill switch halts all agents.

How it works Watch one work
How we engage

From a business outcome to measured value

Every engagement starts from the business outcome, not the technology. We frame it through the same business lens each time, then deliver in five phase-gated stages. Most airlines start with a discovery and value case for one value-chain domain, then scale to the next on the same foundation.

How we frame an engagement

  1. 1Business outcome & KPI
  2. 2Value-chain domain
  3. 3Decisions
  4. 4Data
  5. 5AI & agents
  6. 6Governance & adoption
  7. 7Measured value

Delivery phases

Phase 1
Discover & value case

Outcome, domain and KPIs agreed; data and process assessment; baseline and business case.

Phase 2
Design

Decisions, data products, models, agents and controls designed for the chosen domain.

Phase 3
Build & integrate

Sprint delivery on the SCIKIQ Data Fabric, integrated with reservation, departure-control, GDS and NDC, operations, MRO and finance systems; tested on real data.

Phase 4
Deploy & adopt

Go-live, people and process change, agent autonomy limits agreed with operations, safety and finance; value tracked against the baseline.

Phase 5
Run & scale

Managed service — DataOps, MLOps and AgentOps under SLA — and the next domain on the same foundation.

Each phase ends with a gate signed off by your steering group; a pilot in one domain typically reaches a production-ready capability in 30-45 days. The SCIKIQ Data Fabric we build on

Engagement models

Staff Augmentation

Data engineers, architects, analysts and AI specialists embedded in your teams, under your delivery lead.

Project Delivery

Outcome-based delivery of an accelerator or platform build, owned end-to-end by SCIKIQ with phase gates.

Managed Services

We run and improve your data platforms, models and agents — DataOps, MLOps, AgentOps and support under agreed SLAs.

Weekly status Bi-weekly steering Phase-gated sign-off 30-45 day pilot → scale

Start with the outcome you need

Pick a domain and a KPI. We'll propose a discovery and value case or a 30-45 day pilot and show you what the first weeks look like.

Talk to us Take the self-assessment
Next chapter · 3 of 6
The foundation

Every domain above runs on the same governed data fabric — reservations, distribution, operations, maintenance and the ledger connected, curated, contextualised and consumed, with lineage from source to regulatory report.

Next chapter: The foundation