Omio, the multimodal travel platform coordinating operations with over 3,000 transportation providers across 47 countries, has embedded OpenAI models across its entire engineering operation. The result: technical effort required to build new products has dropped to roughly 20 percent of previous levels, and projects that once demanded multiple developers over a full fiscal quarter now ship with a single engineer working for about one month.
The company's CTO, Tomas Vocetka, mandated that internal functions redesign their operational frameworks to operate as a native AI enterprise. Omio explicitly rejects bolting new technology onto old processes. Instead, the engineering division rebuilt its software development lifecycle around OpenAI Codex, applying it to preliminary research, architectural planning, active coding, automated testing, code reviews, and system maintenance.
Vocetka first provided base ChatGPT access across the workforce to build familiarity with generative models. He describes that initial phase as a preliminary introduction. "Codex handles the actual production workload," he said. To bridge proprietary data environments with these tools, engineers built custom internal connectors. Developers skip basic information retrieval and move directly to task execution inside their integrated development environments. The practice is now expanding into non-technical corporate functions across the wider organisation. For product teams exploring similar integrations, OpenAI Courses can help bridge the skills gap when adopting these tools at scale.
What faster cycle times unlock
Management now allocates capital and engineering hours with far greater precision. Teams test experimental concepts and validate consumer demand using minimal resources. Prototyping eliminates unviable features before full-scale production commitments. Iteration on existing products has accelerated sharply, with updates and new interface elements reaching the live environment at a pace the previous workflow could not support.
The compression in delivery timelines reshapes how product decisions get made. Lower time and cost barriers for software creation mean internal decision-making moves faster. Experimental features that would have been debated for weeks can now be built, tested, and validated - or killed - in days. This shift has direct implications for AI for Product Development roadmaps, where speed of experimentation often determines competitive positioning.
Conversational commerce grounded in live data
Omio launched a conversational travel booking interface in 2023 by connecting OpenAI models to its proprietary transportation inventory spanning trains, buses, ferries, and flights. The system processes natural language queries about complex multimodal routes - a user can ask for the fastest route from Rome to Florence or compare flights and trains between Paris and Barcelona.
Legacy travel booking forced users to navigate multiple websites and manually aggregate itineraries across providers. Omio's generative interface replaces that fractured process. The models analyse text inputs and ping live booking systems to construct viable travel paths. Responses are grounded in real-time pricing and availability data, preventing the generation of travel options based on stale training data. Consumers receive directly bookable itineraries, not generic advice.
The company defines this architecture as conversational commerce - the AI operates as the primary interface layer between the consumer and the global transportation network. Omio expanded the integration into a dedicated ChatGPT experience that accesses the company's transportation network directly. The technical team views this as a departure from legacy search-based interfaces toward native generative customer experiences.
Governance keeps humans in control
Omio's corporate policy mandates that human personnel retain full accountability for all deployed code and final business outcomes. Generative tools function strictly as acceleration engines.
"The responsibility and accountability stay with people. AI helps us develop faster, analyse faster, and make decisions faster, but people stay in charge," Vocetka said.
Automated systems cannot independently execute irreversible changes to the booking infrastructure or core multimodal routing algorithms. Broad employee access to OpenAI tools operates alongside rigorous oversight, creating an environment designed for both speed and systemic stability.
Why this matters for product development professionals
The Omio case provides a measurable benchmark, not a theoretical promise. An 80 percent reduction in development effort and a multi-month-to-one-month timeline compression are specific outcomes, not aspirational targets. For product leaders weighing AI integration, the lesson is not that Codex magically accelerates work. It's that embedding these tools across the full development lifecycle - from research through maintenance - and connecting them to proprietary data environments produces quantifiable shifts in velocity. The governance model also matters: speed without human accountability for irreversible system changes creates fragility. Omio's structure keeps both elements in balance.
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