Key Insights

Transitioned a Jobsite Companion system from proof-of-concept to a production-ready, secure, and scalable AI platform, enabling reliable voice interaction and fleet-level deployment across heavy equipment.

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

The Jobsite Companion began as a promising prototype designed to enhance machine interaction through voice-based AI. However, moving from PoC to production in industrial environments introduced significant complexity.

The system needed to operate reliably in noisy real-world jobsites, support multilingual interaction, scale securely across a growing fleet, and enable over-the-air updates and telemetry monitoring. Without architectural hardening and scalable cloud-edge infrastructure, the solution risked remaining an isolated innovation rather than a deployable product.

Marvik’s Approach

We partnered to design and execute a phased production-readiness roadmap aligned with field validation cycles and major industry milestones. Our approach focused on three pillars:

• Voice & AI System Evolution: Implemented and optimized RAG-based command handling, improved STT normalization, expanded multilingual support (EN/ES), and validated workflows under real jobsite conditions.
• Cloud & Edge Infrastructure: Designed secure OTA mechanisms, encrypted telemetry pipelines, and fleet-wide orchestration capabilities, enabling remote updates and full operational visibility.
• Testing & Security Hardening: Established performance KPIs, built golden datasets for AI modules, executed load and validation testing, and implemented end-to-end encryption and system lockdown measures.

This modular, phased approach ensured the system matured technically while remaining aligned with field feedback and operational milestones.

The Results & Impact

Delivered a hardened, modular AI platform ready for controlled fleet deployment.

Validated reliable voice interaction in real-world conditions, including noise and accent variability.

Established secure OTA updates, encrypted telemetry, and centralized monitoring dashboards.

Positioned the system for production release and scalable expansion across the equipment portfolio

Why This Matters

Industrial AI systems must operate under physical, operational, and security constraints that go far beyond typical software deployments.

By transforming a prototype into a production-grade, fleet-ready platform, this project demonstrates how embodied AI systems can move from experimentation to reliable, real-world deployment at scale.