Margins are thin and fuel is volatile, distribution is moving to NDC and offers, disruption tests every promise made to passengers, and regulators ask how every refund, emission and maintenance decision was reached. The answer runs across the whole airline — from how a fare is filed to how an aircraft is turned and the ledger closes. SCIKIQ is the governed data and AI platform that helps airlines and travel-technology companies make that shift, one value-chain domain at a time.
Airlines organised around systems — a reservation system, a departure-control system, an operations platform, a maintenance system — now compete on decisions made across all of them. The airlines pulling ahead treat data as a product and AI as an operating capability, not a set of pilots. These are the pressures they are responding to.
Fuel, labour and aircraft costs swing faster than fares can follow, and profit per passenger is small. Every point of load factor, yield and on-time performance has to be earned.
Data & AI: demand forecasting, price intelligence and route P&L, with agents that take cost out of operations and finance.
Content now flows through GDS, NDC and direct APIs at once. Agencies and OTAs must compare offers that look different on every channel — and read fare rules no two carriers write alike.
Data & AI: one normalised content model, GenAI fare-rule extraction and virtual interlining.
See the acceleratorsWeather, ATC flow and a tight network turn one late aircraft into missed connections, crew out of hours and a contact centre under siege. Recovery is still pieced together by hand.
Data & AI: disruption prediction, ranked recovery options and agents that re-accommodate passengers within policy.
Passengers expect to rebook, claim a refund and use their status without queuing. Service volumes grow faster than agent teams, and loyalty is earned or lost on the bad days.
Data & AI: passenger 360, AI-assisted service and refund agents that draft the decision.
Passenger-rights and refund rules, emissions schemes and fuel mandates, data-privacy law and airworthiness requirements all ask for traceable data, delivered faster.
Data & AI: lineage behind every refund, emission and maintenance record, and AI-assisted regulatory reporting.
Reservation, departure control, operations and maintenance run on separate platforms with different identifiers, and critical rules live in free text. Every new report, model or agent starts with another extract.
Data & AI: one governed data fabric, with GenAI turning free text into structured, usable data.
See the acceleratorsAn airline cannot let an opaque model decide a maintenance finding, a crew assignment or a passenger's refund. AI has to be inventoried, evaluated, supervised and explained.
Data & AI: agent governance — autonomy levels, approvals, evaluations and a kill switch.
See agent governanceThe response isn't one platform or one model — it is data and AI applied domain by domain, from network planning to revenue accounting, on a shared, governed foundation.
See where, domain by domain ↓Three commercial domains, the passenger, three operational domains and the group functions that run underneath them. Select a domain to see the data it runs on, the AI opportunities, and what SCIKIQ does there.
Fleet, slots and demand shift every season. Planners still build schedules in spreadsheets and point tools, and every new route, hub bank or partner is a bet made on partial data.
Data it runs on
Data & AI opportunities
What SCIKIQ does here
Dynamic offers, continuous pricing and ancillaries move faster than revenue-management systems built on fare classes. Analysts spend their day on overrides and competitor checks instead of strategy.
Data it runs on
Data & AI opportunities
What SCIKIQ does here
Content now flows through GDS, NDC and airline-direct APIs at once. Every carrier writes its fare rules differently, so agencies and OTAs quote penalties wrongly, disputes follow and multi-carrier itineraries go unsold.
Data it runs on
Data & AI opportunities
What SCIKIQ does here
Passengers expect to be rebooked, refunded and recognised without calling anyone. Contact centres absorb every disruption peak, refund rules differ by market, and loyalty data sits apart from operations.
Data it runs on
Data & AI opportunities
What SCIKIQ does here
The operations control centre recovers from disruption by phone and spreadsheet. Crew legality, aircraft rotations and passenger connections are solved in separate systems, and every delay ripples through the day.
Data it runs on
Data & AI opportunities
What SCIKIQ does here
A turnaround involves a dozen handlers working to the minute. Bags, cargo and passengers are tracked in different systems, so mishandled bags and missed connections surface only after they happen.
Data it runs on
Data & AI opportunities
What SCIKIQ does here
Unscheduled removals ground aircraft, maintenance records are partly free text, and parts inventory is sized for the worst case — while airworthiness rules demand that every step is traceable.
Data it runs on
Data & AI opportunities
What SCIKIQ does here
Revenue accounting reconciles tickets, coupons, EMDs, BSP and ARC settlements and interline billing across many systems. Agency debit memos, refunds and payment fraud leak margin, and emissions reporting adds numbers to defend.
Data it runs on
Data & AI opportunities
What SCIKIQ does here
Opportunities and approaches are described qualitatively. Shaded chips are SCIKIQ service lines; the others open accelerators. See every service line mapped to these eight domains
Nine service lines that advise, build, transform and run — delivered on three pieces of SCIKIQ IP, so airlines and travel-technology companies start from working components rather than a blank page.
In our AI & Agentic Engineering and Managed Services work, agents do the routine work end to end — in every domain above. Policy decides what goes straight through; people approve everything else. Nothing happens off the record.
Agents find the new itinerary, apply the fare rule and draft the refund or voucher. Anything above the compensation limit, or with a failed check, waits for a named duty manager.
Watch an agent work a caseAgents plan, call tools and gather evidence on the governed Data Fabric — the same PSS, operations and finance data your teams use.
Each agent has an autonomy level. A case goes straight through only if confidence, compensation limits and checks such as MCT and visa pass.
Everything else lands in a named owner's queue with the agent's draft, rationale and evidence — approve, edit or reject.
Every plan, tool call, guardrail check and decision is logged; overrides feed evaluation, and a kill switch halts all agents.
Pre-built, configurable solutions on the SCIKIQ Data Fabric. Two were built for travel distribution; the rest are cross-industry accelerators our teams configure to airline data, rules and controls.
Two airline accelerators from our travel-technology work, for OTAs, TMCs, GDS providers and airline distributors.
LLM pipeline that turns unstructured ATPCO fare rules (CAT 16, 19, 31, 33) into structured, API-ready penalty and discount data — cross-category validation, airline-specific logic and human review for edge cases.
The global flight network as a graph — airports as nodes, flights as edges — with multi-criteria Dijkstra optimisation for fastest, cheapest and best-value itineraries outside traditional interline agreements.
Travelport uAPI, Amadeus, NDC JSON and airline-direct LCC APIs aggregated into one schema, with XML, JSON and EDIFACT normalised and schedules, fares and availability kept in sync.
Commercial engagements combine these with pricing, network and loyalty analytics on the same distribution data.
About Commercial, Distribution & RevenueCross-industry accelerators below are configured to airline data in an engagement; their demo pages run on banking sample data.
Enter your own volumes. The estimate compares today's manual handling with agents working the cases and people reviewing only the exceptions.
Estimate only, not a quote or a SCIKIQ result. Manual hours = cases × minutes ÷ 60. Supervised hours = cases × (1 − STP share) × review minutes ÷ 60. Hours saved = manual − supervised. FTE = hours saved ÷ 150. Cost saved = hours saved × cost per hour (× 12 for a year). Supervisor capacity = 150 h × 60 ÷ ((1 − STP share) × review minutes). Excludes platform and run costs.
Tell us the problem — fare-rule disputes, a refund backlog, disruption recovery, a reconciliation that never closes, a platform to modernise. We'll propose an assessment or a 30-45 day pilot, delivered by SCIKIQ teams on our framework and accelerators.