Category: Success Story

GetMoved, an AI platform for the moving industry developed by the Cyber Co Startup, has been awarded free access to GPU resources on the Leonardo supercomputer at CINECA in Italy — its third EuroHPC JU allocation through the Fast Lane access. The reason for changing from the LUMI to Leonardo supercomputer is in that Cyber CO has joined NVIDIA Inception, NVIDIA’s programme for technology startups and should now use NVIDIA graphical casrs, like the ones at Leonardo AI factory. Together the three awards have carried GetMoved’s computer vision work from the first experiments to production training runs.

GetMoved turns a short smartphone walkthrough of an apartment into a structured household inventory, a volume and weight estimate, and a moving cost estimate. The platform combines a customer marketplace, an ERP for moving companies, and the inventory engine in a single product. The detection models cover 78 object classes and are supported by a complete training pipeline — dataset preparation, annotation, training, evaluation, and continuous retraining.

Leonardo provides compute continuity as that pipeline moves toward production. NVIDIA Inception adds access to NVIDIA’s model catalogue and development tooling, along with compute credits; the company has opened an organisation on NVIDIA Build and NGC. On the technical roadmap, the current detector is being evaluated against a vision-language model, NVIDIA Cosmos, in phases with a decision gate before each replacement step.

The platform launched in the New York and New Jersey market in June 2026 and works with licensed moving companies on a non-exclusive basis.

A research proposal submitted under the EuroHPC Joint Undertaking Development Access call (EHPC-DEV-2026D06-045) has been successfully evaluated and officially approved, marking a continued commitment to HPC-driven atmospheric research in Montenegro.

The project, titled “HPC Development for Very-High-Resolution Atmospheric Reanalysis over Montenegro (1994–2008) Using the WRF-NMM Modeling System and ERA5 Reanalysis Data”, has been granted 4,000 node hours on the LUMI-C partition for a period of 12 months. This represents a continuation of the previously completed project on the LUMI HPC system, which successfully delivered very-high-resolution atmospheric simulations over Montenegro using the same modelling framework.

The research builds upon the NMM (Nonhydrostatic Mesoscale Model) dynamic core, originally developed by Prof. Zavisa Janjic, and aims to produce very-high-resolution atmospheric simulations reaching horizontal resolutions of up to 400 metres over the Montenegrin domain, driven by ERA5 reanalysis data. Building on the results and experience gained in the previous allocation, the project continues to advance ongoing research in numerical weather prediction and atmospheric modelling on state-of-the-art HPC infrastructure.

This allocation enables IHMS to further advance research in numerical weather prediction and atmospheric modelling, leveraging the capabilities of state-of-the-art HPC infrastructure, said Angel Marcev, Principal Investigator (PI) of the project, from the Institute of Hydrometeorology and Seismology of Montenegro.

The National Competence Centre (NCC) Montenegro provided support throughout the application process within the EuroHPC Joint Undertaking framework.

CyberCo, a Montenegro-based technology company and owner of the GetMoved software platform IP, has been awarded EuroHPC AI Factory resources for the second time through the EuroHPC Fast Lane call, with the support from UoM team from NCC Montenegro. The newly approved allocation provides 15,000 GPU hours on LUMI-G, one of Europe’s most powerful AI supercomputing environments, supporting the continued development of GetMoved’s distributed AI-powered logistics and indoor scene understanding platform.

“Large-scale AI model fine-tuning, distributed computer vision training, volumetric object estimation and optimization of advanced indoor inventory recognition workflows from smartphone-recorded videos are the core focus of this phase of development. The project is focused on improving AI models capable of detecting household objects, estimating their dimensions, calculating approximate volume and weight, and generating logistics-ready inventory data for moving and transportation operations,” said Bojan Djakovic, Principal Investigator (PI) of the project.

The project also includes optimization of distributed GPU pipelines, multi-object tracking, embedding generation and large-scale video processing workflows using EuroHPC infrastructure.

This new allocation represents another important milestone for CyberCo and GetMoved as the company continues building scalable AI technologies for logistics, mobility and intelligent inventory automation.

The Montenegrin company ONEAI has successfully carried out a project on the EuroHPC infrastructure, utilizing the LUMI-G supercomputer to develop advanced AI models in the field of ECG signal analysis. Following the successful completion of the previous cycle, the AIHeal project has been granted renewed access to computational resources for the upcoming period.

During the previous project, the team developed a dynamic HPC pipeline that enables training personalized models for each individual patient, with the capability for continuous retraining based on new data. The key innovation lies in establishing an integrated system that connects the existing infrastructure with the supercomputer:

• data is automatically collected from the AIHeal system,
• sent to the LUMI supercomputer for training,
• after processing, the models are returned to the production environment and registered in the MLflow system.

This approach enables the creation of “digital twin” patient models, where each model is tailored to the specific characteristics of the individual, significantly improving anomaly detection accuracy compared to generic models.

By leveraging the LUMI infrastructure, the AIHeal team achieved significant acceleration in the model training process, particularly through parallelization across multiple GPU units and the execution of a large number of experiments simultaneously. This enabled faster identification of optimal models and improved the overall quality of the solution.

The project results confirm that HPC resources play a crucial role in the development of scalable and personalized AI systems in healthcare, especially in the context of real-time biomedical data analysis.

The newly approved access to EuroHPC resources will be used for further model improvement, performance optimization, and expansion of the system to cover a broader range of cardiovascular and other health-related indications.