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Travel & Hospitality

Flight Disruption Intelligence API

Your customer finds out their flight is in trouble from the airline, usually late. Knowing earlier is worth more to them than almost anything else in your product.

OTAs and booking platforms
Travel management companies
Corporate travel software
Travel insurers
Airport and lounge apps

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What your customers would see

example interface

MockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.

The problem this solves

Disruption is where travel products are judged. A booking flow can be excellent and still lose the customer if the first time they hear about a three-hour delay is a gate announcement. Travel managers feel it harder: a cancelled leg with no rebooking plan turns into a day of phone calls and a traveller stranded somewhere inconvenient.

Most of the signal is observable in advance. The inbound aircraft is already running late somewhere else. The airport is under a ground delay programme. Convective weather is building over the departure field in the hour before pushback. A particular carrier's evening rotations on that route slip more often than its morning ones. None of that is secret, but assembling it per flight and keeping it current is real engineering.

There is an honest limit to be set at the start. Predicting that a specific flight will be cancelled four days out is not possible, and a product that implies otherwise will be caught out. What is possible is a calibrated risk, updated as the departure approaches, that is sharply more useful inside the final twenty-four hours than it is a week before.

Who buys this

OTAs and booking platforms, Travel management companies, Corporate travel software, Travel insurers, Airport and lounge apps

Category: Travel & Hospitality

What the API returns

What comes back

Delay probability per flight with an expected delay distribution, not a single number
Cancellation risk banded rather than asserted as a point probability
Inbound aircraft tracking so a late rotation is visible before it propagates
Airport-level congestion and ground delay programme status
Weather exposure at origin, destination and en-route, mapped to operational impact
Historical on-time performance by carrier, route, aircraft type and time of day
Connection risk for itineraries with tight transfers
Disruption events pushed as they develop, with the reason attached

Endpoints

Method	Path	Returns
GET	/v1/flights/{flight_id}/risk	Delay and cancellation risk for one flight
POST	/v1/itineraries/risk	Risk across a multi-leg itinerary including connections
GET	/v1/airports/{iata}/status	Congestion, programmes and weather exposure
GET	/v1/routes/{origin}-{destination}/performance	Historical performance for a route
POST	/v1/watch	Register flights for monitoring and push alerts
WS	/v1/stream	Push disruption events for watched flights

Request and response

illustrative

Request

{
  "legs": [
    { "carrier": "LH", "flight_number": "1434", "departure_date": "2026-11-04",
      "origin": "BCN", "destination": "FRA" },
    { "carrier": "LH", "flight_number": "716", "departure_date": "2026-11-04",
      "origin": "FRA", "destination": "NRT" }
  ],
  "connection_buffer_minutes": 65,
  "alerts": { "push": true, "threshold": "medium" }
}

Response

{
  "itinerary_risk": "elevated",
  "as_of": "2026-11-03T18:20:00Z",
  "legs": [
    {
      "flight": "LH1434",
      "scheduled_departure": "2026-11-04T07:10:00+01:00",
      "delay_probability_15m": 0.41,
      "expected_delay_minutes": { "p50": 9, "p80": 34, "p95": 72 },
      "cancellation_risk": "low",
      "drivers": [
        { "factor": "inbound_aircraft_late", "detail": "Inbound arriving FRA +38m", "weight": 0.44 },
        { "factor": "origin_weather", "detail": "Low ceiling forecast 06:00-08:00", "weight": 0.21 }
      ]
    },
    {
      "flight": "LH716",
      "scheduled_departure": "2026-11-04T10:15:00+01:00",
      "delay_probability_15m": 0.18,
      "cancellation_risk": "low",
      "drivers": [{ "factor": "airport_congestion", "detail": "FRA departure queue above normal", "weight": 0.16 }]
    }
  ],
  "connection": { "buffer_minutes": 65, "misconnect_probability": 0.27, "band": "watch" },
  "confidence": { "horizon_hours": 13, "calibration_sample": 8421 }
}

Where the data comes from

Schedule and status feeds	Published schedules and live status under a commercial data agreement — the backbone of any flight product
Aircraft position data	ADS-B derived tracking, which is how an inbound late rotation becomes visible hours ahead
Aviation weather products	METAR, TAF and convective forecasts from national meteorological services, which are generally open
Air traffic management advisories	Ground delay programmes and flow restrictions, where the relevant authority publishes them
Historical on-time records	Regulatory on-time performance filings, which are public in some jurisdictions and licensed in others
Your booking records	Optional, and only for flights you are entitled to monitor — lets alerting be scoped to actual travellers

On data licensing. Being allowed to read a dataset does not automatically mean you may redistribute it, resell it through an API, or train a model on it. Before any build starts we check the licence, terms and derived-data rules of every source, and we design around the ones that do not permit your intended use. Where a commercial licence is required, that cost is identified during discovery rather than discovered later.

The intelligence layer

The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.

Separate models per horizon, because the useful features four days out and four hours out barely overlap
Aircraft rotation graph construction so delay propagation through a tail number is modelled explicitly
Weather features derived into operational terms — ceiling, visibility, crosswind, convective coverage — rather than raw observations
Calibration measured and reported, so a stated 40 per cent means roughly four in ten over the validation period
Distribution output instead of point estimates, since the tail of the delay distribution is what breaks connections
Cancellation treated as a banded risk with a floor, because base rates are low and a point probability invites false precision
Driver attribution on every prediction, so an alert can say why rather than just how likely
Continuous monitoring of calibration drift by carrier and airport, with retraining when it degrades

What it changes for the business

Proactive service

Your customer hears about the problem from you, before the airline tells them.

Rebooking lead time

Travel managers get hours rather than minutes to find an alternative routing.

Fewer support spikes

Disruption traffic flattens when the affected travellers have already been notified and offered options.

Insurance and ancillary fit

Calibrated risk at the point of sale supports honest upsell of flexible fares, lounge access or cover.

How it fits into your product

How it embeds

Watch registration from your booking flow, so every ticketed itinerary is monitored automatically
WebSocket stream and webhooks for push into your app, SMS or email channels
Itinerary-level scoring that accounts for connections rather than scoring legs in isolation
Alert thresholds configurable per customer, so a corporate account can be noisier than a leisure one
Components for a disruption banner and a risk detail panel, or raw JSON if you have your own front end
Rate limits and batch endpoints sized for overnight re-scoring of a large booking book

Included in every build

Authentication, scoped API keys and per-customer rate limiting
Input validation with predictable, documented error responses
Versioned endpoints so your integration never breaks without notice
Request and response logging for audit and dispute resolution
Latency, error-rate and usage monitoring with alerting
OpenAPI specification, sandbox environment and client examples
Backups, failover design and a documented recovery procedure
An agreed support arrangement once the service is live

How we would build it

about 20 weeks

Discovery and feasibility

Weeks 1–3

This one depends heavily on data access, so we start by proving the sources exist, can be licensed for your use, and actually carry the signal the model needs. If they do not, you find out here.

Architecture and baseline model

Weeks 4–6

A written architecture and a baseline model measured against a holdout period, so you know what accuracy you are buying before the full build starts.

Production build

Weeks 7–16

Data pipelines, model training and serving, historical archive, validation harness, API, security, monitoring and documentation.

Validation and integration

Weeks 17–20

Backtesting against held-out history, accuracy reporting your team can audit, integration support and an agreed retraining cadence.

Commercial models

There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.

Fixed-scope prototype

A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.

Fixed-price build

An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.

Build then retainer

We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.

White-label product

The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.

Questions buyers ask

Can you tell us four days out that a flight will be cancelled?

No. At that horizon the honest output is a route and carrier base rate adjusted for forecast weather, which is weak. Cancellation signals sharpen inside about twenty-four hours and sharpen again inside six. We would rather return a wide band and say the horizon is long than give you a confident number that does not hold up.

Where does the flight data come from?

Schedule and status data is licensed from an aviation data provider; there is no lawful way around that for a commercial product. Aircraft position, weather and air traffic advisories are largely open. The licence cost is identified in discovery, and it is usually the largest recurring cost in this build.

How do you measure whether the predictions are any good?

Reliability curves on held-out history, by horizon, carrier and airport, plus Brier scores for the binary outcomes. We report those openly, including the segments where the model performs worse — typically small carriers and airports with thin historical records.

Can it suggest alternative flights?

It can surface alternatives from the schedule data with their own risk attached. It cannot price or book them, and it has no view of fare rules, seat availability or your contracted carrier agreements. Rebooking logic sits in your system.

Does weather make this unpredictable anyway?

Weather makes a portion of it unpredictable, and that portion is properly reflected in wider distributions rather than hidden. Mechanical, crew and rotation delays — which are a large share of all delays — are considerably more predictable than weather cancellations.

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Tell us what your product does today and what you want another system to be able to ask it. We will come back with an approach, a scope and a price.

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