Global rail asset management platform

Customer Success Story

Trimble Nexala – intelligent rail asset management solutions

Trimble® Nexala remote diagnostic solutions utilise on-train equipment and cloud-based software to deliver real-time fleet-wide diagnostic information.

BoatyardX is the sole technical partner for Trimble for the design and build of their real time rail telematics software R3M. 





Key Challenges


  • The existing product was not scalable with each implementation requiring dedicated infrastructure and custom development
  • Costly and slow customer onboarding
  • Limited data-handling capability and reliance on legacy technology
  • Outdated user interface



  • Utilising our Discovery process, the team rapidly assessed and defined a holistic product modernisation and cloud migration strategy.
  • During the Delivery phase, we integrated talent from Colombia, Ireland, and Romania into the client’s team.
  • Over 18 months, BoatyardX supported multiple release cycles, improving the multi-tenant architecture continually.

Business Benefits


  • Compete in wider markets for new and larger customers with enhanced onboarding times.
  • New cloud-based application with leading-edge multi-tenant architecture.
  • Exponential increase in ability to handle high volumes of IoT sensor data.
  • Enhanced modern user experience.

Company Overview

A specialist in the area, Nexala – the wholly owned rail arm or Trimble, have been at the forefront of rail telematics for over 10 years. With the continual advancement of IoT devises and the ever-growing number of data points to be collected, Trimble required a rethink of their core technology to unlock two main strategic imperatives:

  • Increase performance x10 to allow for increased amounts of data per train and
  • Build the application in a multi-tenant environment to allow multiple customers to be configured on the same application instance.

We chose BoatyardX as our partner …..

says Trimble Director, Insert Name here.

The Challenge

A modern train can have up to 2000 ‘channels’ or data points per IoT message. A message is broadcast every second per train. A moderate fleet may have 100 trains – this example ads up to 720 million data points per hour. A traditional web service and relational database was not going to handle the processing and storage of such a large amount of data at such high frequency.

While the sheer numbers may be daunting in terms of ability to ingest and store data, the most significant challenge was providing a real-time rules engine that can appraise all the messages in line with the customers’ requirements.

Unlike some other similar but different streaming use-cases, the rules being applied to the incoming messages are user created and dynamic i.e., they are not loaded into the application through code at deployment – rather added, amended, or removed by the client at any stage.

The Solution

BoatyardX took the client’s existing application and used it as a guide for functional requirements. Additionally, the BoatyardX UX Design team, carried out several workshops with end users and created a whole new user flow and user interface.

Our technical design team carried our several spikes and POCs before proposing a Big-Data streaming architecture. This required not only technical alignment with the client but buy-in that some areas of the new application will work differently from a user’s perspective. This exercise under covered many undesirable user behaviours that were having a detrimental impact on performance – which we designed out. 

The new application is built using Flink streaming processors, KSQL and Kafka along with parquet storage of Avro converted messages.  The rules engine is streamlined to deal with real-time events while other areas of the application cater for analytics carried out over stored data.

BoatyardX Services


  • Product discovery and business solution definition
  • Product architecting and planning 
  • Full-stack development 
  • UI/UX design 
  • Reference Cloud Architecture deployment 
  • Data engineering / data science 
  • DevOps 
  • SDLC management
  • Business & technology strategy support



  • Architecture: BoatyardX reference architecture 
  • Frontend: Vue.js
  • Backend: Java SpringBoot Microservices
  • DevOps: BoatyardX Containerized Architecture; AWS Managed Kubernetes Service (EKS)
  • Data:Postgresql, Kafka, Flink Streaming, Enrichment Rules Engine; KSQL Streaming Database, AWS S3 Datalake; AWS Glue Crawler, ETL, AWS Athena (Presto) Query Engine
  • Testing: Selenium AQA
  • UI/UX design: Figma, Miro