Case Study - Southeast Asian Airline - Recosense Labs Inc

Recosense Labs Inc

Case Study – Southeast Asian Airline

Here’s how Southeast Asian Airline achieved a cost reduction of 25% The aviation industry faces significant challenges in handling vast amounts of data and documents – Job cards, Task cards, Product manuals, Reports etc., which come in various formats, such as handwritten copies, images, scanned copies, and unstructured information. Additionally, Southeast Asian Airlines saw the data dispersed across different locations on the ground and in the air. Particularly in aircraft maintenance, crucial technical data is fragmented among engineers, making it challenging to maintain error-free records, ensure safety, and provide accessibility to all teams. 

The complexities of record-keeping are further compounded by the arduous task of retrieving specific information, which requires extensive time and human resources to search based on dates, incidents, chapters, and other parameters. Further, generating maintenance requirements and efficiently allocating human resources for timely delivery is time-consuming and resource-intensive

Southeast Asian Airline achieved an operational cost reduction by 25%


The biggest challenge in aircraft maintenance starts from handling documents in different formats - Handwritten copies, scanned copies, images, unstructured information etc and retrieving information from records take several hours of time and human resources.


Aerobot's intuitive knowledge base and recommendation engine make it the best companion for technical service engineers. The platform automatically identifies events, faults and defects, correlates Engine-Product-Part, produces reports of the troubleshooting actions taken to resolve a defect, and provides an optimal solution.


The implementation of this technology has led to cost reductions of 20% to 25%, as the efficiency gains have streamlined operations and reduced expenses. Additionally, manual efforts have been reduced by 22% to 30%, freeing up valuable resources and enabling employees to focus on higher- value tasks.

The goal was to leverage Artificial Intelligence and/or Machine Learning techniques to develop a robust system that can provide engineers with an optimal solution list. This list would be based on a comprehensive analysis of application data, engineering manuals, and maintenance records spanning 60 years.

The company’s main aim was to create an intelligent solution that utilizes historical data and domain expertise to generate recommendations and issue resolutions for engineers, enabling them to make informed decisions and optimize maintenance processes effectively. By harnessing the power of AI and ML, this system will streamline the decision-making process, enhance efficiency, and improve overall maintenance outcomes.

Southeast Asian Airlines adopted RecoSense Labs’ Virtual Engineering- AI-powered technical knowledge database, an issue resolution platform, to address these business challenges and improve efficiency.

Virtual Engineering is an AI-powered digital assistant designed for operational efficiency in manufacturing, supply chain, and logistics to accelerate issue resolution. The system automates learning of Standard operating procedures (SOP), Product-Part correlation, Mapping participating entities – vendors, teams, locations, divisions, etc. and financial metrics – invoice value, currency, scale, etc. from historical records.

The Virtual engineering system, as a Virtual Digital Assistant, supports Natural language Query from technicians, operations and maintenance teams, and service personnel to fetch auto-classified data/responses from multiple types of data sources (Reports, Job cards, Task cards, Invoices, Quotations, ERP Systems etc) for helping teams resolve issues. The system can classify damages, validate data, and automate audit and compliance of process reports.

Before deploying the AI solution, the airline company needed specific preparatory steps to ensure effective implementation. One of the essential requirements was the meticulous gathering of data from various touch points, encompassing a wide range of sources. These included but were not limited to, manufacturer documents such as the Fault Isolation Manual (FIM) and the Trouble Shooting Manual (TSM), as well as engineering ERP systems like AMOS and AIRCOM.

Additionally, data from Aircraft Health Monitoring applications and reliability reports was vital in this data collection process. By meticulously sourcing and integrating information from these diverse channels, the airline could ensure a comprehensive and robust dataset, forming the foundation for the AI solution’s accurate and informed decision-making capabilities. This proactive approach to data gathering was fundamental in enabling the successful deployment and utilization of the AI solution within the airline’s maintenance operations.

The process depicted involves utilizing the RecoSense AI solution, which is designed to handle both speech and text inputs. The solution initiates a web query to identify and gather relevant information regarding the issues at hand. It then systematically searches multiple data sources, including maintenance records, manufacturer records, engineering manuals, and application data. The RecoSense AI system generates an optimal solution list tailored explicitly for the operator by thoroughly analyzing this wealth of information. This comprehensive list serves as a valuable resource, equipping the operator with informed recommendations to address the identified issues effectively.

The AI bot automatically identifies the type of defects, maintains a historical record of defects and events, keeps track of the troubleshooting steps taken to resolve a defect, and correlates the key entities in the knowledge graph.

The solution operates on the principles of AI, algorithms, and data analytics. By processing the available data, it generates customized solutions tailored to the airline’s specific experience and events. These solutions can be seamlessly implemented across various applications, leading to tangible improvements such as increased on-time departure of aircraft and reduced direct maintenance costs.

  • Semantic Search
  • Knowledge graphs
  • Compliance management
  • Action recommendation
  • Report generation
  • Dashboard visualization

Implementing the AI-powered Virtual Engineering solution has yielded impressive results across various areas for Southeast Asian Airlines. One notable achievement is the significant reduction in processing time for reports, documents, task cards and even job cards. Each of these documents were in different formats like handwritten copies, images, scanned reports and other forms of unstructured information. Hence the manual process took an average of 1 to 3 hours to process the documents, but now, this time has been reduced to just a few seconds. This improvement has a ripple effect on customer satisfaction, as the time to deliver results has been accelerated by 20% to 30%, allowing customers to access information faster. 

Furthermore, the implementation of this technology has led to cost reductions of 20% to 25%, as the efficiency gains have streamlined operations and reduced expenses. Additionally, manual efforts have been reduced by 22% to 30%, freeing up valuable resources and enabling employees to focus on higher- value tasks.

Overall, adopting Virtual Engineering solution has proven to be a game-changer, revolutionizing the processing of reports and delivering a range of benefits for the engineering team.