Key Insights

Designed and delivered a secure OTA and telemetry foundation, enabling remote updates, fleet visibility, and reliable operation of AI systems under real-world connectivity constraints.

About the Client

A global leader in compact and heavy equipment, focused on integrating advanced technologies to enhance operator experience, machine intelligence, and fleet performance.

The Challenge

Scaling AI systems from prototype to production required more than the application itself, it required a reliable way to operate and manage a growing fleet in the field.

The client needed an OTA platform capable of delivering software, ML models, and configurations remotely, with strong security and auditability within their AWS environment.

The solution also needed to support remote diagnostics, critical command execution, environment separation (DEV/QA/PROD), and structured telemetry collection to enable monitoring, troubleshooting, and continuous improvement.

Marvik’s Approach

• ‍Secure OTA Infrastructure: Designed mechanisms to deliver software, models, and configurations remotely with full auditability and control.‍
• Fleet Telemetry & Observability: Built structured telemetry pipelines enabling real-time monitoring, diagnostics, and data-driven decision-making.‍
• Environment & Access Control: Implemented strict separation across DEV/QA/PROD environments, along with secure access and operational safeguards.‍
• Resilience for Real-World Conditions: Ensured reliable operation under intermittent connectivity, a key constraint in jobsite environments.

The Results & Impact

Enabled a production-grade foundation to manage Jobsite Companion in the field:

• Faster and safer delivery of updates across devices, even with unreliable connectivity.
• Centralized fleet telemetry for diagnostics, monitoring, and operational decision-making.
• Reduced operational risk through secure access, auditability, and environment separation.
• A platform designed for long-term scalability and flexible ownership.

Why This Matters

In connected heavy equipment, production readiness depends on operational control: the ability to update, monitor, and troubleshoot reliably at scale.

By establishing a robust OTA and telemetry backbone, this project enabled the transition from isolated prototypes to scalable, real-world AI deployments across distributed fleets.