Lifecycle-First Edge AI for Industrial Fleets (LIFE-AI)
| Reference number | |
| Coordinator | RIoT Secure AB |
| Funding from Vinnova | SEK 8 000 000 |
| Project duration | April 2026 - March 2028 |
| Status | Ongoing |
| Venture | Advanced digitalization - Industrial needs-driven innovation |
| Call | Industrial applied AI by advanced digitalization 2026 |
Purpose and goal
LIFE-AI enables secure, scalable Edge AI adoption through lifecycle management for distributed fleets. It supports shared, sustainable, and secure AI infrastructure by enabling real-time decisions near data, reducing dependence, improving robustness, and compliance readiness. Results: lifecycle-first architecture, repeatable deployment pipeline, and industrial validation with Flox. KPIs include stable performance, robust updates, high availability, reduced regression risk, and faster deployment.
Expected effects and result
LIFE-AI delivers a lifecycle-first approach for secure, scalable Edge AI in industrial fleets. Expected results include a reference architecture, a repeatable deployment pipeline, and validated pilots with Flox. Effects include improved robustness, stable long-term performance, safer updates, reduced regression risk, higher availability, and faster deployment, enabling sustainable and compliant industrial AI operations.
Planned approach and implementation
LIFE-AI follows a phased, evidence-driven approach: define use cases, KPIs, and architecture; build an end-to-end pipeline (optimize, deploy, monitor, rollback); run pilots with controlled updates and validation; and finalize results with KPI evidence and an adoption kit. Implementation integrates partner capabilities (RIoT Secure, Embedl, Flox) through continuous validation in 3 (three) use cases, with iterative improvement.