RailYatri accelerates performance with cloud modernization

RailYatri’s legacy infrastructure struggled to support rapid user growth, 24x7 booking operations and customer demand for instant travel information. Limited scalability, slow provisioning and fragmented data systems affected performance and delayed the production of critical insights. Manual support workflows further reduced efficiency, making it difficult to deliver the seamless, unified booking experience passengers expected.

NTT DATA addressed these challenges by implementing a robust cloud-native platform with dynamic scaling, faster provisioning and centralized real-time analytics. Google Cloud’s Compute Engine enables consistent performance during traffic surges, BigQuery unlocked instant operational insights, and Cloud Speech APIs automated customer interactions. This modernized platform enhanced reliability, streamlined operations and elevated the overall travel experience for customers.

RailYatri partnered with NTT DATA to modernize its train and intercity-bus booking operations and real-time travel analytics. A phased, cloud-native approach automated routine workflows, streamlined data processing and delivered actionable insights across platforms.

Our expertise and collaborative approach addressed RailYatri’s operational challenges, leading to the design of AI- and  cloud-powered solutions that aligned with the company’s  technology vision.

The solution encompasses:

• Real-time data analytics: BigQuery manages operational and booking data to provide up-to-date insights.
• Interactive dashboards:Looker dashboards track booking trends, revenue patterns and service performance to support data-driven decision-making.
• Scalable infrastructure:Google Compute Engine enables faster provisioning and dynamic scaling to handle traffic spikes.
• AI-powered customer experience:Google Cloud Speech-to-Text transcribes support calls for analysis, while Cloud Text-to-Speech automates travel updates.
• Advanced booking alerts:This feature sends real-time notifications during high-demand travel periods.
• Reliable cloud-native operations:Migrating databases and core services to Google Cloud provides a stable backend and 24x7 availability.

This integrated approach enabled RailYatri to reliably scale travel operations, deliver real-time insights, and provide passengers with a seamless, data-driven booking and  travel experience.

Faster booking operations and reduced lead times

RailYatri achieved nearly 60% faster infrastructure provisioning using Compute Engine, enabling quicker scaling and smoother booking experiences during peak travel periods.

Improved customer service efficiency

By using Google Cloud Speech-to-Text and Text-to-Speech, the platform automated call transcription and travel announcements, reducing manual effort and improving the speed and quality of customer interactions.

Scalable, resilient infrastructure

Migrating core services and databases to Compute Engine virtual machines (VMs) created a stable, cloud-native backend that reliably supports 24x7 booking operations, even during high-demand travel seasons.

Enhanced data-driven insights

BigQuery delivers real-time analyses of bookings, user behavior and operational trends, while Looker Studio dashboards help teams monitor revenue, performance and business metrics to guide strategic decisions.

Reliable, always-on travel services

The cloud-native architecture enables consistent uptime and performance, enabling passengers to access information about schedules, availability and bookings without disruption.

Improved passenger experience during peak demand

The introduction of the Advanced Resource Period feature provides real-time alerts for high-demand slots, helping passengers secure tickets more efficiently during busy  travel periods.