The QSense Platform is an enterprise-grade, fully managed SaaS product, built with industry-leading cloud data management and custom Railinc components. The QSense Platform supports data ingestion through multiple methods, including managed file transfer, asynchronous messaging, and web service APIs.

The QSense Platform utilizes custom developed services for data processing, using standard programming languages and cloud frameworks. These flexible services allow the platform to process a variety of data formats for multiple purposes, such as Extract-Transform-Load (ETL), business rules, analytics, and calculations. The use of industry-standard data management platforms allows for easy integration with a wide range of artificial intelligence/machine learning (AI/ML), business intelligence, analytical, and data visualization tools.

The QSense Platform has robust micro-level capabilities that can be customized, configured, assembled, mixed and matched to create options to support any future business need. Some examples of these key micro-capabilities for the QSense Platform include:

• Data ingestion – ingests data in different formats, sizes and velocities into various data solutions
  including data lakes, data warehouses, data marts, databases, or document stores.
• Data enrichment – incorporates and augments the data with additional information from other
  related data sources, or outputs from other processes such as artificial intelligence and machine
  learning algorithms.
• Data insight – applies processes and algorithms to large data sets or event streams to derive
  actionable insights. The algorithms can vary from traditional regression analysis to more
  advanced machine learning methods. The output of the insights can be text-based, actionable
  recommendations or graphical representations.
• Data velocity – processes data in real-time or in batches at various velocities and frequencies.
• Data speed control – controls and throttles the speed of data processing, which can be useful
  when downstream systems cannot handle the high throughput and velocity of the incoming data.

Features