AI Takes Off: Real-World Airline Innovations in CX and Ops Automation

According to reports, 4.9 billion travelers are expected to travel by air in 2025, along with 70% of customers demanding real-time, digital-first support. Rising expectations for speed, personalization, and intelligent operations are redefining how airlines engage with passengers. As legacy systems struggle to keep up, the industry is moving toward more connected and responsive travel experiences.

This is where intelligent automation and generative AI are poised to make a significant impact. By delivering instant service, automating repetitive tasks, and supporting complex processes, these technologies create a seamless experience for both passengers and airline staff. They enable smoother, more efficient air travel by transforming how operations are managed across the journey. For example, virtual assistants can handle up to 80% of recurring passenger queries, while AI tools provide ground staff with critical information far more quickly than traditional systems.

In line with this transformation, Niveus Solutions - Part of NTT DATA partnered with a leading airline to design and implement AI-powered solutions that enhance both customer-facing services and internal operational processes.

Client Background

The client is a leading low-cost airline headquartered in Malaysia, recognized as one of Asia’s largest and most influential aviation brands. With operations spanning over 165 destinations across 25 countries, the airline serves more than 20 million users monthly through its digital channels. Known for pioneering a digital-first approach, the airline continuously invests in technologies that enhance the passenger journey, from booking and check-in to customer support and post-flight services.

Over the years, the airline has positioned itself as a technology-forward organization, actively pursuing AI, automation, and cloud-native capabilities to streamline operations and improve customer experiences. Its focus on scalability, cost-efficiency, and innovation has helped it stay competitive in a highly dynamic aviation industry. With a vast user base that spans multiple geographies, the airline faces the complex challenge of providing personalized, responsive, and multilingual support to its diverse customer segments while maintaining operational efficiency.

In pursuit of its digital transformation goals, the airline partnered with Niveus Solutions to enhance both frontline customer engagement through a next-generation virtual travel concierge and internal customer operations through AI-powered automation. This strategic collaboration aimed to replace outdated, rule-based systems with intelligent, scalable solutions that meet the evolving expectations of modern travelers.

Problem

The airline faced two related challenges impacting both passenger experience and operational efficiency. On the customer side, they needed a conversational solution capable of delivering real-time, personalized travel assistance across multiple touchpoints. The existing chatbot system was limited in scope, rigid in design, and unable to understand context or support multiple languages effectively.

Simultaneously, their internal operations teams struggled with high volumes of repetitive support tasks. Much of their time was spent on manual data lookups and responding to recurring queries, which slowed down response times and limited their ability to focus on complex service issues.

Solution

Niveus collaborated with the airline to address both needs through two parallel AI-driven solutions. For passengers, we developed a Generative AI-powered Virtual Travel Concierge designed to provide seamless, multilingual support and handle dynamic travel workflows. For internal operations, we built an AI Assistant that automates routine support tasks, accelerates information retrieval, and enhances overall team productivity.

Both solutions were built using the same foundational principles: advanced natural language processing, secure cloud-native infrastructure, and tight integration with the airline’s backend systems. This approach ensured consistency, scalability, and ease of maintenance across use cases. 

Case Study 1 - Virtual Travel Concierge – Architecture & Technical Deep Dive

Objective

To assist passengers with personalized, real-time information about their bookings, travel policies, and destinations through a conversational interface, enhancing both user experience and operational efficiency.

1. Overcoming Challenges in Building a Multilingual, Multimodal AI System

The development of the Virtual Travel Concierge (VTC) presented multiple challenges, particularly in handling language diversity, multimodal input, and system scalability. One of the primary hurdles was enabling the system to understand and respond accurately in more than seven languages with high precision. Niveus addressed this by training custom natural language understanding (NLU) models and fine-tuning them using region-specific data sets to adapt to local dialects, cultural nuances, and user behavior.

Document variability was another challenge, especially with processing travel-related documents such as passports and IDs. The team implemented OCR technology with context-aware validation, allowing accurate extraction and verification of critical information. This helped streamline tasks such as name corrections and refund form processing.

To ensure scalability and reliability, Niveus used a modular, stateless architecture deployed on Google Kubernetes Engine (GKE). This architecture supports auto-scaling during peak loads and maintains consistent performance across sessions. The system is orchestrated through LangGraph, which manages intent detection, workflow routing, multilingual processing, and built-in clarification handling, delivering a seamless and human-like interaction.

2. Workflow Prioritization and Phased Rollout Strategy

Over 50 workflows were designed and phased in to deliver incremental value. In the initial phase, high-impact operational workflows were prioritized to address the most common passenger queries. These included booking changes, flight cancellations, baggage add-ons, refund eligibility checks, itinerary generation, and flight status updates. Their deployment immediately reduced the live agent workload and improved response times.

The second phase focused on experience-enhancing capabilities. Sentiment analysis was introduced to detect frustration and prioritize agent hand-offs via Salesforce CRM. Proactive workflows were added for logged-in users, offering timely reminders and action prompts based on booking statuses.

Subsequent enhancements included itinerary planning from destination prompts, OCR-based data capture for automating document handling, and workflows for loyalty point checks, promotions, seat upgrades, and travel insurance. All workflows are built using a Workflow Designer tool, enabling business teams to create and update logic without developer intervention, ensuring rapid iteration and deployment.

3. Integrated AI Capabilities and Their Impact

The VTC combines several advanced AI capabilities:

• Multilingual NLU: Supports 7+ languages with over 80% accuracy, enabling inclusive support across diverse geographies.
• Sentiment Detection: Identifies user frustration with over 70% accuracy, enabling timely escalation and live-agent routing.
• OCR Integration: Achieves over 70% accuracy in extracting key information from IDs, passports, and forms, reducing manual input.
• Proactive Triggers: Monitors booking and account statuses to push contextual prompts, creating a more intelligent and helpful assistant.

These capabilities work together through LangGraph orchestration, providing smooth intent recognition, context retention, and intelligent response generation.

4. Performance Monitoring and Continuous Improvement

Post-deployment, AI performance is continuously monitored using a custom admin portal. This portal offers dashboards to track usage metrics, workflow completion rates, language accuracy, and user satisfaction. Real-time insights help in identifying bottlenecks, misrouted intents, or failing nodes.

A structured feedback loop allows support teams to flag unhandled queries, while workflow performance is analyzed regularly to fine-tune models and add fallback intents. These insights guide data retraining, new feature rollouts, and refinement of existing logic, keeping the system aligned with user expectations.

5. Modular Architecture and Future-Ready Design

Niveus engineered the VTC with a modular, cloud-native architecture that supports seamless integration and continuous learning. Key elements include:

• LangGraph: Central orchestration engine for managing workflow logic, intent detection, and clarification steps.
• Salesforce Integration: Enables smooth live agent transitions and CRM syncing.
• Workflow Designer: Empowers business users to build and update workflows independently.
• Agentic AI Design: Combines multiple specialized models for different tasks, such as booking, document verification, and itinerary planning.

This future-ready approach allows rapid adaptation to evolving business needs and sets a strong foundation for continuous innovation in airline customer engagement.

Case Study 2 - AI Assistant for Internal Customer Operations – Architecture & Technical Deep Dive

Objective

The AI Assistant for Internal Customer Operations was designed to streamline repetitive, time-consuming support tasks typically handled by internal service agents. These tasks often involved retrieving booking records, refund status, policy details, and customer-specific actions spread across multiple disconnected systems. The goal was to build a conversational, intelligent assistant that could retrieve relevant information in real time and deliver it through a seamless voice or chat interface—reducing agent workload and improving response speed.

1. Architecture Diagram & System Overview

The solution was architected as a stateless, cloud-native system optimized for high concurrency and secure backend access. It leverages Google Cloud services for real-time data access, API orchestration, and large-scale deployment while ensuring smooth integration with the airline’s legacy systems.

2. Key Components:

1. Dialogflow CX
2. PaLM API (via Vertex AI)
3. Cloud Run & Cloud Functions
4. BigQuery & Looker
5. Cloud Endpoints & IAM

3. Technical Architecture Deep Dive: Enabling Scale and Security for 25,000+ Users

To support a workforce of over 25,000 internal users, the system was built on a cloud-native, stateless architecture designed for high concurrency and secure access:

• Cloud Platform: Deployed on Google Cloud Platform (GCP) using Cloud Run for stateless, auto-scaling compute to handle fluctuating workloads.
• Microservices: Core services (such as intent handling, querying, and response generation) were implemented as independent microservices, enabling modular scalability and efficient updates.
• Data Architecture: All operational and customer interaction data resides in BigQuery, allowing low-latency querying and high-throughput performance for real-time responses.
• Security:

AI & ML Innovations Behind the Solution

Several AI and ML components were integrated to enhance system intelligence, especially in multilingual and voice-based interactions:

• Multilingual NLP
• Response Generation
• Data Validation
• Reinforcement and Tuning

UX & Visualization: Insights for All Users

The assistant was designed to be intuitive for both technical and non-technical users:

• NL2SQL Interface
• Visualization
• UX Design

Governance, Monitoring & Responsible AI

To ensure trust, transparency, and continuous system improvement, several governance and monitoring mechanisms were implemented:

• Data Monitoring
• Error Logging
• Feedback Loops
• Audit Trails

4. Execution Highlights

• Legacy System Integration: The assistant connects directly with legacy airline systems through secured APIs, enabling agents to access historical bookings, refund statuses, and policy details without switching between multiple tools or interfaces. This tight integration simplifies day-to-day operations for customer support staff.
• Reduced Manual Handling Time: By automating repetitive lookup and validation tasks, the assistant significantly decreased the average handling time for internal queries. Agents can now retrieve information in seconds, allowing them to focus on more complex support cases and improving overall productivity.
• Real-Time Analytics for Continuous Optimization: Interaction data captured in BigQuery is continuously analyzed to identify bottlenecks, slow workflows, and missed intents. These insights feed into the workflow improvement loop, helping business users and system administrators refine intent models and expand automation coverage through the Workflow Designer.
• Voice and Chat Flexibility: The assistant supports both voice commands and chat input, making it accessible to different types of internal teams across the airline’s operations center. This omnichannel interface design improves accessibility and usability across varied environments.
• Scalability and Security: Built entirely on a stateless architecture and deployed via Cloud Run, the system scales dynamically based on load and supports concurrent use across departments. IAM-based access ensures that only authorized personnel can retrieve sensitive customer and booking data.

5. Benefits

What we delivered:

• Production-grade GenAI deployment on Google Cloud
• Secure, scalable architecture aligned with enterprise policies
• Improved agent and passenger satisfaction through reduced response times
• Cloud-native design for rapid enhancements and multilingual scalability
• AI observability and tuning pipeline for continuous optimization  

Partnership Impact

The partnership between Niveus and the airline was the foundation of this metamorphosis, built on trust, transparency, and co-creation. Our approach involved much more than a solution delivery service. We designed for shared ownership, alignment, and integration with the client’s intended vision and operational reality. By incorporating the consultative, agile engagement model, we were able to adapt to the changing priorities of an airline, relying on our agile deployment process to ensure that all AI capabilities focused on a real challenge: responding to multilingual passengers and enabling operational efficiency for internal teams. 

The collaboration allowed us to define success collaboratively and to validate the solutions we built with iterative, ongoing feedback loops. We built purposefully, honouring the client’s worldview while ensuring that the AI capabilities we built were not only effective but also contextually intelligent, along with a set of benchmarks. This set the stage for accelerated decision-making, implementation, accountability across both use cases, and opened up opportunities for innovation and for scale in both AI investments and operational deployment decisions. Such success has put the airline on a strong foundation in an adaptable AI ecosystem ready to be a leader in AI-led aviation.

Please note: The actual architecture diagrams are confidential. To get a better understanding of the above projects and the architecture diagrams, please get in touch with us.