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Application + Platform Development | Fleet Management

AI AT THE WHEEL: Driving Revenue & Trust Through Predictive Fleet Intelligence

In fleet management, speed and precision affect operational efficiency, customer satisfaction, and competitiveness. Organizations depend on data from connected vehicles and devices to understand performance, anticipate service needs, and make timely decisions. As the number of connected devices grows, legacy platforms can struggle to handle increasing data volumes while delivering the responsiveness operators and customers expect.

For one fleet management leader, the platform had to support data from more than 250,000 IoT devices while addressing the need for predictive maintenance, a better customer experience, and stronger data management. The engagement brought these connected needs together: using operational data more proactively, making the platform easier to use, and strengthening the underlying data infrastructure.

At a glance

Industry

Fleet management

Scale

More than 250,000 IoT devices

Predictive capability

Maintenance predictions using black-box recorder data and driving behavior

Customer experience

Tablet-friendly UI/UX redesign informed by customer feedback

Data infrastructure

Migration from PostgreSQL to Cassandra

The Challenge

Managing Data at Fleet Scale

The Challenge

One fleet management leader found that its outdated platform could not keep pace with the volume and speed of data generated across its operations. Managing data from more than 250,000 IoT devices placed significant pressure on the platform, limiting its ability to deliver the real-time analytics and low-latency operations needed to remain competitive. As competitors embraced AI-driven analytics and predictive maintenance, the company faced increasing pressure to deliver the speed and accuracy required for more proactive service.

The move to the cloud introduced additional complexity. Data-storage challenges and evolving compliance regulations made it harder to manage information and forecast important trends. These were connected business and technology issues: the company needed a platform that could support timely insights, reliable data management, and a customer experience that kept pace with changing expectations.

The Solution

Engineering Predictive Maintenance and a Stronger Platform

The Solution

Aditi took a holistic, customer-first approach that addressed predictive capabilities, user experience, and the data-management foundation together. Rather than treating each issue in isolation, the work focused on how the platform could use operational data more proactively while improving the customer experience.

  • Predictive maintenance: Aditi implemented a predictive maintenance model using black-box recorder data to analyze driving behavior and predict when maintenance would be required. This gave the fleet management leader a way to anticipate issues before they arose, creating an opportunity to reduce costly downtime and operate more efficiently.
  • Customer-focused UI/UX: Aditi redesigned the user interface and experience using direct customer feedback. The tablet-friendly, intuitive design simplified the user journey and enhanced functionality, helping attract new users while improving the experience for existing customers.
  • Data infrastructure modernization: Aditi migrated the company’s infrastructure from PostgreSQL to Cassandra. The migration improved data replication and backup reliability while addressing performance and regulatory-compliance requirements.

Business Outcomes

New Service Opportunities and Greater Customer Trust

The predictive maintenance model has enabled the fleet management leader to unlock new revenue streams and provide new services based on predictive insights. This shift toward proactive service delivery has created opportunities to deepen customer relationships and position the company for continued growth.

The redesigned UI/UX improved the user experience and supported customer satisfaction and adoption across multiple segments, strengthening the platform’s ability to attract and retain customers.

The PostgreSQL-to-Cassandra migration improved data replication and backup reliability. Together with work to address security and compliance requirements, these changes give the organization greater confidence in handling sensitive information and help reinforce customer trust.

What Changed

The engagement connected three areas of change: anticipating maintenance needs, simplifying the customer experience, and strengthening the data foundation. The company gained a predictive maintenance capability based on driving behavior, a tablet-friendly interface informed by customer feedback, and data infrastructure migrated from PostgreSQL to Cassandra. Together, these changes supported more proactive services, improved usability, and more reliable data management.

Technology Environment

Technology Environment

IoT devices, Black-box recorder data, Predictive maintenance model, PostgreSQL, Cassandra, Tablet-friendly UI/UX

Technology Environment

  • IoT devices
  • Black-box recorder data
  • Predictive maintenance model
  • PostgreSQL
  • Cassandra
  • Tablet-friendly UI/UX

Looking to make operational data more useful, modernize a customer-facing platform, or support more proactive services?

Aditi brings together platform engineering, data capabilities, and customer-focused design to help address complex business and technology needs.

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