Data & AI services for airlines — advise, build, transform, run.
DaasLabs teams set your data and AI strategy, build the platforms, transform commercial, operations and revenue accounting, and run what we build. Our Data Fabric Framework, pre-built accelerators and a supervised digital workforce of AI agents are how we deliver it faster.
Airline data and AI programmes stall because every one starts from zero.
Siloed reservation, distribution, operations and finance systems, rules held as free text and long implementations mean value arrives late — or not at all.
Industry baseline and error risk from our travel-technology case study; rule-based figures are design baselines from the build.
1 · No governed data foundation
Reservation, departure control, GDS and NDC feeds, operations, MRO and finance systems don't agree — lineage and quality are unknown.
2 · Services firms rebuild from scratch
Each project re-invents pipelines, models and controls, so timelines and fees grow.
3 · Software alone doesn't change the process
Point products solve one use case and leave integration, data and adoption to the airline.
4 · AI pilots never reach production
Proofs of concept built without data, controls and an operating model stay on the shelf.
Airlines are moving from rules engines and copilots to GenAI pipelines and supervised agents.
Models now read fare rules, assemble itineraries and work operational cases end to end; people supervise the exceptions.
So what for an airline: the operating model moves from “a person runs the rules” to “agents do the work, a person supervises many agents” — which makes validation, autonomy limits and audit trails the deciding capabilities, especially where safety is involved.
Outcomes from our delivered fare-rules engagement (99.8%, 60%); 120+ is build scope; 392 is from an illustrative demo search.
A services firm that arrives with its own IP — so airlines pay for outcomes, not reinvention.
Services
Nine service lines across advise, build, transform and run — delivered by teams who know airline distribution, operations and revenue accounting.
Data Fabric Framework
The governed foundation: the 4C method, 167+ pre-built connectors, metadata, lineage, data quality and security.
Accelerators
Fare Rules Intelligence, the Virtual Interlining Engine and the Distribution Data Hub — plus cross-industry accelerators for reconciliation, close and FP&A.
Supervised digital workforce
AI agents that do the routine work end to end, with people approving exceptions and every step logged.
Nine service lines cover the lifecycle, organised the way airlines buy them.
Select a stage on the wheel, or start from your role.
Set direction and the rules the data must meet.
Data & AI StrategyMaturity assessment, AI operating model, business case Data Governance, Privacy & Regulatory DataPassenger-data privacy, refund, emissions & airworthiness lineageEngineer the platforms and the AI that runs on them.
Data Engineering & Platform ModernisationPSS, GDS / NDC, ops & MRO data on one platform AI & Agentic EngineeringGenAI extraction, graph optimisation, agent squadsChange how an airline function works, end to end.
Commercial, Distribution & RevenueFare rules, virtual interlining, NDC, pricing Customer, Loyalty & ServicePassenger 360, refunds, rebooking, service Operations & Disruption ManagementOCC recovery, crew, turnaround, MRO Revenue Accounting & FinanceSettlement recon, revenue integrity, route P&LKeep it healthy and improving after go-live.
Managed ServicesDataOps, MLOps & AgentOps under SLAs, 24×7Every service line runs on the same framework, accelerators and digital workforce. All services in detail →
A repeatable 4C method turns raw airline data into production-ready capabilities in 30–45 days.
- Step 1 · Week 1–2Connect167+ pre-built connectors plus GDS, NDC and airline-direct APIs: reservations, departure control, operations, MRO, revenue accounting and fare rules; batch and streaming.
- Step 2 · Week 2–4CurateStandardise, cleanse and enrich: MDM, de-duplication and data quality rules.
- Step 3 · Week 3–5ContextualizeMetadata catalog, critical data elements, multi-hop lineage, ownership and policy.
- Step 4 · Week 4–6ConsumeData products, APIs, BI, NLQ GenAI studio and the agents behind each accelerator.
Twelve capability layers give every airline one blueprint — and one way to measure progress.
The same layers we build and score in the maturity assessment. Hover a layer to see what it does.
Runs in your cloud tenancy or on-prem. Code, ontology and agents are handed over — you own what we build. Explore the blueprint →
MIT CISR found enterprises at stages 3–4 perform well above their industry average financially, while those at stages 1–2 perform below it. Source
Airline and cross-industry accelerators mean no service line starts from a blank page.
Pre-built, configurable starting points on the framework — many staffed by an agent squad. Hover or tap a tile.
Commercial, Distribution & Revenue CCO
Revenue Accounting & Finance CFO
Two airline accelerators turned the hardest distribution problems into production systems.
Outcomes (99.8%, −60%, hours → seconds) from our delivered fare-rules engagement; design comparisons and the DEL→SFO demo search are illustrative.
Fare Rules Intelligence. Raw ATPCO text in, structured, API-ready penalty data out — validated across categories, with ambiguous rules sent to a person. See the case study →
24 agent roles across 8 airline domains — your people supervise the exceptions.
Agents work end to end across network, pricing, distribution, customer, operations, airports, engineering and finance. Policy decides what goes straight through; people approve the exceptions — and safety-critical decisions always stay with licensed people.
Connection Bank Monitor · L0
Interline Opportunity Analyst · L1
Pricing Action Recommender · L2
Group Request Pricer · L2
Itinerary Assembler · L3
Content Consistency Checker · L0
Refund & Compensation Drafter · L2
Service Agent Assist · L1
Recovery Options Planner · L1
Crew Legality Checker · L1
Baggage Recovery Agent · L2
Cargo Booking Checker · L1
Predictive Maintenance Watcher · L0
Directive Applicability Checker · L1
Revenue Integrity Agent · L2
Route P&L Commentator · L2
Click a bar to see the agents in it.
Agent designs from our AI & Agentic Engineering practice — not client results. The live control-room demo runs on banking sample data.
An agent does the legwork on every case; a person makes the call when policy says so.
Illustrative replay: a BSP settlement doesn't match the ticket-sales record. The Settlement Reconciler works it.
- 1Source systemsThe BSP billing file shows an agency settlement that the ticket-sales record doesn't match — a refund was counted differently.
- 2Data fabricChange-data capture lands sales, refunds and the BSP file within minutes, quality-checked, as governed data products.
- 3Metadata & ontologyBoth resolve to the same agency, ticket and coupon, with lineage back to each source.
- 4ContextMatching rules, similar past breaks and the revenue-accounting procedure are assembled — only what this analyst may see.
- 5AgentPlans and calls tools through the secure AI gateway:
get_settlement_breakfind_matching_couponsget_refund_history; proposes a match with evidence and a confidence score. - 6PolicyKill switch, autonomy level, confidence ≥ 0.90 and an amount within tolerance decide: straight through, or to a person.
- 7Human gateThe revenue-accounting analyst reviews the evidence on one screen and approves, edits or rejects the proposal.
- 8ActionA governed, typed action clears the break — or drafts an agency debit memo — limited, idempotent and reversible.
- 9AuditEvery step, tool call and decision is recorded; outcomes feed the evaluation that decides whether autonomy can be raised.
Autonomy is set per agent and raised only on evidence — inside hard guardrails.
Confidence threshold
Straight-through only if confidence, amount limit and evidence checks pass.
Amount limits
Refunds, compensation and settlement breaks above the limit always go to a person.
Named human owner
Approves, edits or rejects every exception; maintenance, crew and operational decisions always need a licensed person.
Everything logged
Every plan, tool call and decision; overrides feed evaluation. Personal data masked in prompts.
Kill switch
One control halts every agent at once.
Travel-technology leaders have already realised these gains with DaasLabs teams.
Engagements for a GDS integration partner and an OTA marketplace, from our published case study.
AI fare-rule interpretation engine
99.8% rule accuracy vs an industry average of 82%; penalty costs down 60%; processing from hours to seconds across 120+ airlines.
Virtual interlining, New Delhi → San Francisco
18% cheaper and 10 hours faster on the cheapest option; $361 saved on the fastest — from 22 airlines and 392 virtual combinations.
Multi-source distribution hub
Travelport uAPI, Amadeus, NDC JSON and airline-direct APIs aggregated; XML, JSON and EDIFACT normalised to one schema.
Production-grade routing services
Route, flight-leg, assembly, optimisation, visa and insurance services on async FastAPI with multi-layer caching.
Child, infant & service-fee rules
CAT 19 discounts, accompanied-travel conditions and unaccompanied-minor fees extracted into structured data.
What could a supervised agent squad free up in your airline?
Move the sliders to your own volumes. Agents work the cases; people review 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. Supervisor capacity = 150 h × 60 ÷ ((1 − STP share) × review minutes). Excludes platform and run costs.
Faster than services alone, a better fit than software alone.
How the DaasLabs model compares with the usual ways airlines deliver data, AI and agentic automation.
| Criterion | Big-4 / SIservices only | Point productssoftware only | In-house build | DaasLabsservices + framework + accelerators |
|---|---|---|---|---|
| Time to production value | 6–12 months | 3–6 months + integration | 12–18 months | 30–45 days |
| Airline data models & domain depth | Generic methods | One use case | Build | Pre-built |
| Reusable data foundation | Rebuilt per project | Vendor-specific | Build | Data Fabric Framework |
| Ready-made accelerators | Varies | Single product | None | Airline + cross-industry |
| Supervised AI agents in operations | Pilots / PoCs | Copilot features | Build & govern | Agent squads with guardrails & AgentOps |
| Process change & adoption | Yes | Left to the airline | Partial | Yes |
| Run & continuous improvement | Separate contract | Product support | Internal team | Managed services |
| Total cost of ownership | High | Medium–High | Very High | Low |
Big-4 / SI (6–12 months), point products (3–6 months + integration) and in-house builds (12–18 months) are compared on larger screens.
Four phases, each ending with something you keep — and three ways to buy it.
Discovery & Planning
You receive a maturity assessment and target blueprint, with a prioritised roadmap.
Gate: pilot scope signed offAnalysis & Design
Target-state design and ontology, mapped to your controls; agent autonomy agreed with operations, safety and finance.
Gate: design authorityBuild & Deploy
Landing zone as code, pipelines, accelerators and agents configured and tested on real data.
Gate: go-live readinessSupport & Embed
Runbooks, evaluation suites and knowledge transfer to a trained team.
Gate: handover sign-offStaff Augmentation
Architects, data and AI engineers embedded in your teams, under your delivery lead.
Project Delivery
Outcome-based delivery of an accelerator or platform build by a DaasLabs pod, with phase gates.
Managed Services
We run and improve the platform, models and agents — DataOps, MLOps and AgentOps under SLAs.
A 30–45 day accelerator pilot proves value on one use case before you commit to scale.
Fixed scope, fixed timeline, agreed success criteria — and a scale-up business case at the end.
One accelerator & its agent squad
Typically Fare Rules Intelligence for your top carriers, or settlement reconciliation, with agents starting at “act with approval”.
2–3 source systems
e.g. ATPCO fare rules, a GDS or NDC feed and the PSS — or BSP files, ticket sales and the GL.
One business unit
e.g. distribution or revenue accounting, with a named owner and SMEs for rules and UAT.
DaasLabs pod
Engagement lead, data engineer, domain SME, AI / agent engineer.
Success-criteria targets are agreed in week 1.
Three steps take us from this conversation to value in production.
Start small with one accelerator, prove it, then scale on the same framework.
Scoping workshop
Walk through your data landscape and pain points, pick the pilot use case and agree success criteria.
1–2 weeksAccelerator pilot
Deploy one accelerator and its agent squad on the framework against live data, and measure the result.
30–45 daysScale & embed
Roll out across business units and further accelerators through project delivery or managed services.
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