“A fare rule gets misread and we quietly under-collect the penalty.”
SCIKIQEvery fare rule is read and validated by AI; an agent audits tickets and flags exposure before it leaks.
Industry · Airlines
One live view of every fare, flight and passenger
Unify reservations, fares and rules, ops and crew, loyalty and ancillary into one governed view — then explain why revenue is leaking, on-time slipped or a bank of flights went irregular, trace it to the PNR and the fare rule, and act before it costs the schedule.
Your systems
PSS / reservationsFare & pricingOps & crewLoyaltyBaggageFare rules
SCIKIQ
one governed data fabric360GraphCopilotAgents
Outcomes
Less revenue leakageFaster recoveryHigher ancillaryLive in weeks
01A day in the role
What the network and revenue team sees
before the first wave
One governed brief instead of nine dashboards — every number traced to its source system, every recommendation showing its reasoning.
Good morning, ImranMonday · 5:50 AM
Network health85▲ 4AI confidence93%Agents working8liveDecisions need you3
Today’s brief
Load factor held at 84% while yield improved for a fourth week.
On-time performance recovered after the turnaround change, and ancillary attach rose on the reworked fare rules. Weather is building on two European stations for the evening wave.
If only one decision is made today, pre-emptively re-accommodating the exposed connections is expected to protect the most revenue.
Confidence93%
Network health
- Load factor+2.1ptHolding at 84%
- On-time performance+5.4ptTurnaround change
- Yield (RASK)+3.8%Fourth week up
- Disruption cost− 22%Fewer misconnects
- Ancillary attach+9.2%Fare rules reworked
- Fuel per ASK− 3.1%Routing optimised
What changed
- Weather risk 2 stationsBuilding on the European evening wave
- Misconnects − 22%Pre-emptive re-accommodation is now automated
- Ancillary revenue +9.2%Fare rules interpreted from governed data
- Fare-rule exceptions − 44%GenAI engine resolves rules consistently
- Crew utilisation +3.6ptRosters aligned to the live network view
- Turnaround variance − 18%One definition across ops and finance
Decisions
Re-accommodate the exposed connections
Protects$1.8M revenue
Confidence95%
WindowBefore the evening wave
Adjust fare rules on two markets
Impact+$540K ancillary
Confidence88%
WindowThis week
AI reasoning — why re-accommodate now?
- Two European stations show building weather for the evening wave
- 340 connections are exposed across the affected banks
- Re-accommodating now costs a fraction of a misconnect recovery
- 61 comparable pre-emptive moves avoided 74% of disruption cost
Recommendation Re-accommodate now.
AI has been working
- Monitored 1,200 rotations across the network
- Interpreted fare rules across 40,000 combinations
- Scored connections for misconnect probability
- Simulated three re-accommodation plans
- Reconciled ops and finance to one definition
- Generated the network operations briefing
Why340 connections are exposed to building weather
Why nowPre-emptive moves cost a fraction of misconnect recovery
Evidence61 comparable moves avoided 74% of disruption cost
Confidence95% · based on 4 years of disruption data
AlternativeWait and recover — models $1.8M additional cost
Ask me anything — or tell me the outcome you’re trying to achieve
Illustrative airline view · sample data
02Where it hurts
The questions commercial and ops teams can't answer fast
Each is a question that today means days of manual work across reservations, fares, ops and loyalty. SCIKIQ answers it from governed data — with lineage a revenue auditor will accept — then acts on it.
“When a bank goes irregular, rebooking is manual and passengers wait.”
SCIKIQDisrupted PNRs surface instantly; an agent finds feasible rebooking options and recovers the flow.
“We leave bags, seats and upgrades on the table at every touchpoint.”
SCIKIQNext-best-offer is scored per passenger; an upsell agent makes the offer at the right moment.
“We see the delay after it cascades, not the cause before it does.”
SCIKIQOps signals fuse into one live view; root cause of the delay traces to the crew, gate or turn.
“We can't see which frequent flyers are quietly drifting to a rival.”
SCIKIQOne view of tier, spend and behaviour; the next-best action reaches the passenger in time.
“GDS, NDC and direct LCC feeds never reconcile into one picture.”
SCIKIQEvery channel fuses into one governed graph, as a measurable, reusable data product.
Three kinds of data, one governed graph — SCIKIQ fuses live flight signals, systems of record, and the fare-rule text in between.
Real-time signals
live signals & telemetry
Flight statusSensors & ACARSBooking eventsBaggage scansGate & turn events
Structured
systems of record
PSS / reservationsTicketingFares & rulesLoyaltyOps & crew
Unstructured
text, images, audio
Fare-rule textContractsAdjuster & agent notesChat transcriptsCall transcripts
One governed 360 & knowledge graphfused, contextualised and AI-ready
03The four layers
Four layers. Each answers a harder question.
Enterprise 360 tells you what happened. The knowledge graph tells you why. The copilot explains it in plain language. The agent factory does something about it — on your airline data.
01
Layer 1 · Enterprise 360
What is happening?
Unify flight, fare, booking, ops and loyalty data across reservations, pricing and crew into one real-time view — so commercial, revenue and operations share the same numbers.
OutcomeRevenue & on-time
02
Layer 2 · Knowledge Graph
Why is it happening?
Trace the relationships between flights, fares, PNRs, crew and passengers to find why revenue leaked or on-time slipped — and prove exactly where every fact came from.
OutcomeRoot cause & exposure
03
Layer 3 · AI Copilot
Tell me, in plain language
Let a revenue analyst, ops controller or loyalty lead ask in plain language — answered from governed data with the lineage an auditor will accept.
OutcomeCompetitive advantage
04
Layer 4 · Agent Factory
Don’t just tell me — fix it
Turn answers into action. Agents rebook disrupted passengers, interpret fare rules, make the next-best ancillary offer and audit tickets for leakage — every step logged and auditable.
OutcomeRevenue & recovery
04The outcome
What airlines get
One governed view of every fare, flight and passenger.
LessRevenue leakage from misread fare rules
FasterRecovery from disruption and IROPS
HigherAncillary revenue per passenger
WeeksTo a live airline 360
04In the field · Travel technology
Reading fare rules and stitching routes with AI
A leading travel-technology & airline-distribution company runs two AI programmes on SCIKIQ — the world’s first AI-powered fare-rule interpretation engine, and a graph-driven virtual-interlining engine that stitches multi-airline itineraries without traditional interline or code-share agreements. Client and carrier names withheld.
The world’s first AI-powered fare-rule interpretation engine
NLP text classification and entity extraction read complex fare-rule text and pull out penalties and conditions accurately — with airline-specific rule handling across 30+ carriers, parallel category processing, cross-category validation and consistency checks, and a human-in-the-loop path for ambiguous cases. The result eliminates revenue leakage from misread penalties, processes fares 3× faster, keeps calculations consistent across all fare types, cuts manual review and cost, leaves a complete audit trail, and scales as inventory grows.
30+Carriers’ fare rules read by AI
3×Faster fare processing
Cross-categoryValidation & consistency checks
Human-in-loopFor ambiguous rule cases
Stack — fine-tuned open LLMs (Qwen) served via vLLM, NLP and entity extraction, Python FastAPI, MongoDB, async processing and fuzzy matching.
Graph-driven virtual interlining
AI multi-airline route optimisation stitches seamless connections without traditional interline or code-share agreements. The global flight network is modelled as an intelligent graph — airports are nodes (IATA, terminals, MCT, visa rules) and flights are edges (fare, times, LCC/FSC type, baggage, connection constraints). A multi-criteria Dijkstra search minimises a composite edge weight — time + price + layover penalty + connection risk + visa complexity — so it prefers operationally-realistic routes over the naive cheapest. Industrial feasibility is baked in: minimum connection time per airport, terminal-change compatibility, transit-visa / TWOV / 96h eligibility via a Visa API, baggage self-connect (US DOT) rules and sensible layover min/max. Multi-source ingestion — GDS/uAPI, NDC JSON, direct LCC APIs — plugs into one unified graph; new carriers are just new edges, no redesign. In one example, a DEL→SFO multi-hop itinerary was found at ~24h / $702 against a leading OTA’s 35h / $857.
2–5 hopRoutes optimised on one graph
FSC + LCCFull-service and low-cost carriers
MCT · visa · bagFeasibility built in
24h / $702DEL→SFO vs OTA’s 35h / $857
Stack — Python 3.11 / FastAPI / async, multi-layer Redis caching, React / Vite / Tailwind, Kubernetes / Jenkins on AWS.
30+Carriers’ fare rules read by AI
3×Faster fare processing
2–5 hopRoutes optimised
MCT · visa · bagFeasibility built in
Illustrative of a real SCIKIQ travel-technology engagement; client and carrier names withheld.
04Skill your team
The engineers who deploy this
SCIKIQ Certified Data and AI Engineer runs a specialisation for this sector — the same value chain, data landscape and constraints as this page, taught as hands-on labs with timed assessments and two end-to-end capstones.
Where is revenue leaking across your fares this quarter?
We would love to think through it with you — no pitch, no form maze.
Frequently asked questions
How does SCIKIQ help airlines with disruption?
It unifies flight, fare, crew and operational data into one governed view, so the impact of a disruption on cost, load factor and revenue is visible from a single place. Agents then act on recovery within defined limits.
Can SCIKIQ handle fare and revenue complexity?
Fare-rule automation is one of SCIKIQ's documented deployments: a world-first GenAI fare-rule engine built for an international airline, grounded on governed data rather than free-text interpretation.
Does it connect to existing airline systems?
Yes. SCIKIQ extracts from reservation, operations and finance systems in place, including legacy and SAP sources, without invasive change to those systems.