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. DaasLabs is the data and AI services team 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.
Read the case studyWeather, 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.
Read the case studyAn 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 how DaasLabs adds value 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
How DaasLabs adds value
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
How DaasLabs adds value
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
How DaasLabs adds value
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
How DaasLabs adds value
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
How DaasLabs adds value
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
How DaasLabs adds value
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
How DaasLabs adds value
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
How DaasLabs adds value
Opportunities and approaches are described qualitatively. Shaded chips are DaasLabs service lines; the others open accelerators and the case study. See every service line mapped to these eight domains
Nine service lines that advise, build, transform and run — delivered on three pieces of DaasLabs 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 Data Fabric Framework. 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.
Selected engagements for a GDS integration partner and an OTA marketplace, from our published travel-technology case study.
Unstructured ATPCO fare-rule text turned into structured penalty data, processing categories in parallel with cross-category validation and human review for edge cases.
Rule-based parsing that needed per-airline maintenance and manual updates on every format change replaced by a model that adapts automatically.
Graph-driven engine combining LCC and full-service segments into 2-4-5 hop itineraries outside code-share agreements, with MCT, terminal and visa checks built in.
GDS (Travelport uAPI, Amadeus), NDC JSON and airline-direct APIs aggregated, with XML, JSON and EDIFACT normalised to one schema and kept current.
Route, flight-leg, assembly, optimisation, visa and insurance services on async FastAPI, with pre-computed routes and multi-layer caching for real-time OTA workloads.
A travel-technology platform serving agencies and OTAs with airline content across 120+ carriers. Unstructured fare rules and complex multi-airline connections were more than traditional systems could handle. Our GenAI and graph platform turns that data into structured intelligence — accurate penalty quotes, multi-carrier bookings and optimised routes.
Native NDC JSON APIs for IndiGo, SpiceJet, Akasa and more: ancillaries, bundles and branded fares in real time, bypassing GDS fees.
Travelport uAPI, Amadeus and NDC aggregated; XML, JSON and EDIFACT normalised to one schema; schedules, fares and availability kept current.
Natural-language travel queries such as “cheapest 2-stop DEL to NYC under $500”, with LLM intent recognition across chat, voice and WhatsApp.
Fare trends and optimal booking windows, popular corridors and demand forecasts, with recommendations for agents and travellers.
The challenge
120+ airlines, each writing fare rules differently.
What we built
An LLM pipeline from raw rule text to structured, API-ready penalty data.
The challenge
Code-share routes price budget travellers out.
What we built
A graph of the global flight network that finds itineraries outside interline agreements.
Illustrative demo search (one DEL→SFO query), not a measured business outcome. The engine opened a new budget-traveller segment for the client.
Before · raw ATPCO text
CANCELLATIONS BEFORE DEPARTURE CHARGE INR 3500 FOR CANCEL/REFUND. CHARGE INR 4500 FOR CANCEL WITHIN 4 HOURS OF SCHEDULED DEPARTURE. AFTER DEPARTURE TICKET IS NON-REFUNDABLE IN CASE OF NO-SHOW. CHANGES BEFORE DEPARTURE CHARGE INR 2250 FOR REISSUE/REVALIDATION.
After · structured penalty data
{ "carrier": "XX", "penalties": {
"cancellation": [
{ "type": "BEFORE_DEPARTURE", "window": "BHGT-4", "fee": 3500, "currency": "INR" },
{ "type": "BEFORE_DEPARTURE", "window": "BHRF-0-4", "fee": 4500, "currency": "INR" },
{ "type": "NO_SHOW", "fee": "NON_REFUNDABLE" } ],
"change": [
{ "type": "BEFORE_DEPARTURE", "window": "BHGT-4", "fee": 2250, "currency": "INR" } ] } }
Demo run on a sample rule set: 2.3s processing · 6 rules extracted.
| Measure | Rule-based | GenAI |
|---|---|---|
| New airline onboarding | 2-4 weeks of development | Zero-shot |
| Edge-case failures | 8-15% | <1% |
| Maintenance cost | High, per airline | Near zero |
| Format changes | Manual updates | Adapts automatically |
Design comparison of the two approaches from the build, not client-measured results.
| Source | Option | Time | Cost | Route |
|---|---|---|---|---|
| Leading OTA | Cheapest | 35h 30m | $856.70 | DEL→GOA→BLR→DOH→SFO |
| Our engine | Cheapest | 24h 20m | $702.44 | DEL→DOH→SFO |
| Leading OTA | Fastest | 15h 30m | $1,405.72 | DEL→SFO direct |
| Our engine | Fastest | 15h 30m | $1,044.54 | DEL→SFO direct |
22 airlines analysed, 392 virtual combinations.
Infeasible edges are pruned before optimisation, so every fastest, cheapest or best-value result is operationally realistic as well as mathematically optimal.
Fare rules
Virtual interlining
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 DaasLabs 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 DaasLabs teams on our framework and accelerators.