Atmora Tech

Industry

Booking flows that survive a fare sale

Booking, offer and disruption systems for operators whose worst day is a fare sale during an outage.

Context

Technology in Travel & Hospitality

We build search and booking flows, offer and ancillary logic, property and guest systems, and the disruption handling that decides whether a bad day becomes a bad quarter. Travel demand is spiky and correlated with the events that break things: a fare sale, a storm, a strike. Systems are designed around that correlation rather than around average load.

Direct booking is frequently oversold as the answer to distribution cost. Once paid acquisition, payment fees, service costs and cancellation behaviour are counted, some segments genuinely cost less through an intermediary, so we model the economics per segment before building a funnel. If the numbers say your channel mix is already close to optimal, the honest recommendation is to spend the budget on disruption handling instead.

1,100:1
look-to-book ratio served within an agreed supplier query budget
71%
of disruption re-accommodations completed without agent involvement
0.9%
false decline rate after retuning fraud thresholds on booking value

Challenges

What actually keeps this sector awake

Not generic disruption — the specific constraints that shape every technology decision here.

  • Distribution complexity across legacy and NDC

    Inventory lives in a passenger service system or property management system with decades-old semantics, while partners expect modern offer and order APIs. Supporting both doubles the surface area and the reconciliation work behind it.

  • Search cost and availability freshness

    Shopping requests outnumber bookings by four orders of magnitude, and every look-to-book ratio increase raises supplier query costs. Caching hard enough to control cost produces price mismatches at checkout that customers experience as bait.

  • Disruption at scale with manual rebooking

    When a hub closes, thousands of itineraries need re-accommodation while the contact centre queue grows faster than agents can work. Policy is applied inconsistently and goodwill costs are discovered only in the following month's accounts.

  • Fraud and payment complexity across currencies

    Multi-currency settlement, high-value bookings, and chargebacks arriving months after travel. Fraud rules tuned for retail decline good customers whose behaviour is normal for travel, and each false decline is a lost high-margin sale.

Approach

How we address them

  • An offer and order layer over the reservation system

    A stable internal offer model that isolates channels from the underlying system's quirks, with caching policies set per route or property and price re-verification before payment so the quoted figure is the charged figure.

  • Search economics under control

    Tiered caching driven by measured price volatility, supplier query budgets enforced in code, and look-to-book monitored per channel so an aggressive partner is throttled before it becomes an unbudgeted supplier invoice.

  • Automated re-accommodation

    Rules-based rebooking that proposes options within policy, ranked by cost and customer value, executed with the passenger's confirmation. Agents handle exceptions rather than everything, and the goodwill spend is visible the same day.

  • Fraud scoring tuned to travel loss curves

    Models trained on your own chargeback history with decision thresholds set against contribution rather than a generic risk score, plus a review queue sized to what your team can actually clear before departure.

Services

Relevant practices

Products

Platforms used in this sector

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