Tomorrow’s depot, already operating

We developed a live demonstration environment that showcases end-to-end vehicle service workflows using automation, AI, and integrated technician support. From entry to exit, the system identifies incoming vehicles via license plate recognition, assesses service needs, such as refueling or charging, and generates a structured work order in real time. Upon departure, automated visual inspection detects surface anomalies and triggers follow-up actions where needed.

The demonstration integrates parts of the IBM Maximo suite like Manage, Mobile, Assist, and Visual Inspection into a unified operational flow. Core technologies include structured light for dent detection, AI-powered predictive maintenance, and technician-guided workflows. All components are designed to meet the demands of safety-critical, enterprise-scale environments.

This is not a simulation. It is a functioning, real-world model of next-generation depot operations engineered for reliability, data integrity, and seamless integration with existing systems.

What sets the solution apart

1. Non-disruptive by design Fully interoperable with existing enterprise platforms such as Oracle, SAP, and Workday. No replacement of current systems required.
   

2. Modular and scalable Built from industry-specific building blocks, with support for future-ready extensions like spatial awareness, AI-assisted scheduling, and autonomous workflows.
   

3. Fast to deploy The complete demo was configured and running in under two weeks, using augmented training and standardized deployment tooling.
   

4. Proven resilience Initially trained and validated in Jyväskylä, the demo was held in Kaarina, demonstrating its adaptability across diverse conditions and sites.

Walking through the demo

As a vehicle enters the depot, its arrival is captured by two standard iPhones, demonstrating how easily the system can operate with off-the-shelf hardware.

When it comes to MAXIMO based visual inspection, hardware flexibility is foundational. It can integrate with existing camera infrastructure like security, maintenance, or operational feeds without the need for specialized hardware.

For organizations running edge devices, it supports on-device inference, allowing AI models to execute locally with minimal latency. This is particularly useful in constrained or high-security environments.

Thermal imaging is also supported, enabling early detection of faults in components such as EV chargers or high-voltage wiring. Heat anomalies are automatically captured and fed into the maintenance pipeline for action.

In parallel, the system supports LiDAR data and composite video models, creating layered inspection insights from multiple sensor inputs. As LiDAR resolution continues to improve, the platform is positioned to extract even greater value from this modality.

Where robotic platforms are present, existing onboard cameras can be utilized for inspection tasks, with no additional integration burden.

In all cases, the system remains hardware-agnostic and platform-adaptive, designed to meet existing infrastructure where it already is.

This demonstration reflects Technosmart’s commitment to operational efficiency, safety, and technological resilience. Our thanks to all participants, contributors and organizers, with special recognition to the DWHS team for delivering a high-fidelity result under a compressed timeline.

Smart maintenance is no longer conceptual. It is operational.