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Scaleout Vision
Edge-ready, privacy-first computer vision AI for real-world impact
Scaleout Vision helps organizations efficiently, securely, and adaptively capture valuable edge-based image data from vehicles, drones, industrial equipment, and infrastructure. It overcomes common limitations like privacy concerns, network costs, hardware diversity, and limited labeled data.

Core Technology

Scaleout Vision combines several technologies for efficient and secure edge-AI training
Federated Self-Supervised & Continuous Learning

Enables training directly on edge devices with unlabeled data, providing adaptive model updates without extensive retraining.

Network-Efficient Protocols

Minimizes data transfer by exchanging only model parameters, ensuring functionality even with intermittent connectivity.

Hardware Optimization

Uses optimized algorithms and model distillation techniques for consistent performance across diverse edge hardware platforms.

Privacy-Preserving Architecture

Retains sensitive data locally, securely exchanging minimal model information with optional differential privacy safeguards.

Key Benefits

Privacy & Compliance
Secure local data handling protects sensitive information, ensuring regulatory compliance.
Cost & Efficiency
Reduces cloud dependence, minimizes bandwidth needs, and accelerates deployment through ready-to-use models.
Adaptive Performance
Continuously updates models to maintain accuracy and robustness using real-world data.
Scalable & Future-Proof
Easily integrates with emerging hardware technologies and adapts smoothly to evolving privacy standards.

Case Study: FEDAIR - NATO DIANA Challenge

Scaleout’s participation in NATO’s DIANA Challenge Programme demonstrates the impact of Scaleout Vision through the FEDAIR (Federated Aerial Intelligence for Recon) initiative, highlighting key defense capabilities:

Rapid Adaptation: Enables continuous edge learning, maintaining model effectiveness in dynamic conflict environments.

Resilient Connectivity: Ensures operational continuity without transmitting raw data, crucial in contested or disrupted network conditions.

Enhanced Security: Protects sensitive reconnaissance data by keeping it local, significantly reducing interception risks.

System Resilience: Decentralized architecture ensures sustained performance, even if individual nodes are compromised.

Explore how FEDAIR is advancing reconnaissance capabilities in challenging operational scenarios in this blog post »
Resources
Product Roadmap
  • V1 (Q3 2025): Federated self-supervised training for edge AI.
  • V2 (Q4 2025): Enhanced supervised learning and human feedback integration.‍
  • V3 (Q2 2026): Multi-modal sensing (Lidar, Radar), automotive-grade AI support.
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