Omio deploys OpenAI models to reshape travel product development

Omio, a multimodal travel booking platform, has deployed OpenAI models across its entire engineering pipeline — from product development to the launch of updated booking interfaces.

Scale and Stakes

Omio coordinates work with more than 3,000 transport providers across 47 countries: airlines, railways, bus routes, ferries. With such infrastructure complexity, accelerating development directly impacts how quickly users receive new features — and how accurate schedule, pricing, and availability data remain. The integration spans engineering operations as a whole: teams use models to accelerate code writing, automate testing, and rapidly deploy features. The first public result — updated booking interfaces that the company has already launched.

The CTO's Requirement: Restructuring, Not Overlay

The company's CTO, Tomash Vocetka, laid down a strict condition: AI must not simply overlay outdated processes. Each internal division is required to completely rethink its workflows taking into account the capabilities of new tools — from scratch, not cosmetic patches. This is a position many companies declare but rarely maintain. The standard scenario — take the old approval scheme, add a chatbot, and announce «digital transformation». Omio chooses a different path. Vocetka's approach implies several specific principles:

• OpenAI models are integrated into engineering operations, not isolated in a separate «AI department»
• Each internal function is reconsidered for the capabilities of the models
• The success criterion is the speed of product market launch, not demonstration effect
• The first measurable result — new booking interfaces for end users
• The goal — to scale the approach across all 47 markets of presence

Why Travel is Complex for AI

Travel is an industry with a particularly high cost of error. Incorrect pricing, outdated schedules, or booking failures mean direct financial losses and dissatisfied customers. Moreover, data changes constantly: schedules, fares, and availability are updated in real-time by thousands of providers. This is why travel platforms traditionally approached automation of critical paths cautiously. Integration of language models directly into engineering operations — not just into customer chatbots — is a more significant signal: the company trusts models enough to build production infrastructure on them. For competitors in online booking, this creates pressure. If Omio can deploy features to market faster thanks to AI acceleration of engineering teams, the structural advantage will only grow.

What This Means

Omio demonstrates what real AI integration looks like in a large operational company: not a chat assistant in the corner of the interface, but a fundamental rethinking of how code is written and deployed. The scale — 3,000 providers, 47 countries — is representative enough for other complex B2C platforms to take notice of this pattern.