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Kubia

Use cases

Built for AI in the physical world.

Distributed AI shows up wherever models have to run on moving assets, remote sites, telecom infrastructure, or large device fleets. These examples are illustrative applications of the Kubia platform.

Critical Rail Infrastructure

Rail operators run models on moving assets and along fixed infrastructure, where connectivity is intermittent and conditions change constantly. Kubia can coordinate the models, the data they generate, and the updates they need.

Primary outcome

Detect failures earlier and continuously improve models using data collected from operating infrastructure.

How Kubia supports it

  • Multiple models deployed across trains and rail infrastructure
  • Continuous collection of video and vibration data
  • Visual track inspection
  • Detection of cracks, missing bolts, and other defects
  • Wheel predictive maintenance
  • Human-in-the-loop model feedback
  • Automated retraining, promotion, and redeployment

AI Model Roaming Across 5G MEC

Latency-sensitive services need inference close to the user or device. Kubia can place models across MEC locations and move them as demand and location change.

Primary outcome

Deliver the right model from the nearest available infrastructure.

How Kubia supports it

  • Low-latency AI services
  • Location-aware model deployment
  • Personalized models that move with users or devices
  • Smart switching between MEC locations
  • AR and VR applications
  • Federated learning
  • Globally distributed data collection

Intelligent Distributed Device Fleets

Smart vending is a clear example: thousands of connected units, each running multimodal models, each generating data worth learning from. The same pattern applies to kiosks, cameras, industrial controllers, and other intelligent devices.

Primary outcome

Operate and continuously improve AI across thousands of distributed devices.

How Kubia supports it

  • Large geographically distributed device fleets
  • Multimodal models using audio, video, and sensor data
  • Model deployment based on location and usage
  • Continuous model updates
  • Remote monitoring and alerting
  • Predictive maintenance
  • Data collection for experimentation
  • Multiple AI use cases running on the same device

Have a distributed AI problem that doesn't fit a cloud pattern?

Bring us the environment, the models, and the constraints. We will walk through how the lifecycle would run on Kubia.

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