AirAsia

My career has revolved around designing and implementing highload, customer-centric solutions across various industries. Before AirAsia, I served as Head of Engineering and Solution Architect at GreenPacket, spearheading IoT, AI, Fintech and Blockchain initiatives for large-scale PaaS/SaaS applications. Reporting directly to the Group CEO taught me how to balance innovative development with strategic business goals—from budgeting and resource allocation to tender management and KPI oversight.

Earlier, I worked at Materialise NV, contributing to 3D printing applications for wearables and FDA Class-II medical devices, which instilled in me a rigorous approach to quality, compliance, and agile delivery. Hackathons have also been a recurring source of hands-on innovation in my journey.

These experiences converged at AirAsia, where I now oversee three parallel engineering teams—Chatbot, Contact Center/CRM and the MOVE SuperApp’s frontend and backend—along with a smaller R&D group. Directly reporting to AirAsia’s C-level, I manage vendor selection and legal compliance to budget negotiations and product roadmaps. It’s a unique opportunity to unify cutting-edge tech and a dynamic user experience in one of the most operationally intensive sectors---travel.

Transforming AirAsia’s Customer Experience with Google Cloud and Salesforce

We operate in an industry where flight schedules, passenger demands and operational constraints can change rapidly. By integrating Google Cloud and Salesforce CRM as our core infrastructure, we’ve created a flexible, cloud-first system that consolidates inbound flight data from multiple sources—such as Navitaire for flights and booking information and other systems for Ad hoc changes to the booking, as well as manual emails or CSVs from Ground Operations—into BigQuery as a central data warehouse. We then transform and normalize this data so it can power various customer-facing notifications and services.

From the passenger’s perspective, that means consistent and proactive communication. If their flight is retimed or canceled, they receive an immediate notification—often accompanied by email or SMS follow-ups—eliminating guesswork and anxiety. Post-flight, the same unified data architecture allows us to handle balance reminders or refund processes seamlessly. The result is an end-to-end travel journey that feels intuitive, personalized and reliably informed.

We run containerized microservices—spanning chatbots, social messaging services, inbound notification processors and outbound messaging—in a scalable environment. This is especially critical when abrupt schedule changes or promotions create sudden spikes in user activity.

At AirAsia, we leverage AlloyDB primarily for our MOVE Chat/AirAsia Chat, storing messages from our community channels (Flight, Travel & Interest groups). Its high-performance architecture lets us handle real-time, chat-based communication reliably—even under heavy user loads.

We rely on Firebase for real-time capabilities—such as the AirAsia chat real-time message status, push notifications and user session management. It complements BigQuery pipelines by offering event-driven triggers to keep customers informed in near real time.

Acting as a central hub for case management and multi-channel communications, Salesforce orchestrates email, SMS and contact center engagements. Whenever a retime or merge event is flagged in BigQuery, Salesforce automatically creates cases and issues notifications, ensuring consistent messaging across all touchpoints.

Together, these technologies ensure that BigQuery remains our central data warehouse for time-sensitive flight information from various (often legacy) systems, while AlloyDB and Firebase handle specific real-time engagement features in the MOVE app. Ultimately, passengers benefit from faster, more accurate updates and seamless communication channels throughout their journey.

Enhancing Passenger Engagement Through AI and Community-Driven Chat

The AirAsia MOVE app’s community chat (also known as AirAsia Chat) fosters real-time communication among MOVE’s customers—whether they’re sharing travel tips, discussing flight experiences, or simply networking before a journey. This sense of community builds trust and engagement well before takeoff.

Behind the scenes, AI/ML is key to delivering a more personalized experience. We analyze chat interactions and inbound flight data (from BigQuery) to determine when users might need additional support—say, a gate change notification, or a personalized promotion based on recurring travel behavior. Sentiment analysis can flag frustrated users or high-impact operational disruptions, prompting faster escalation to live agents via our Contact Center or direct communication channels. Over time, this data-driven personalization turns a simple chatroom into an intelligent platform that consistently adapts to each traveler’s needs.