Distributed AI infrastructure
Kubia gives enterprises one platform to simulate, deploy, manage, and operate AI models and data services across enterprise edge, 5G MEC, and distributed intelligent devices.
Digital Simulation·Distributed MLOps·Edge Data Services
The market problem
Edge AI forces organizations to coordinate three separate technology stacks — hardware, software and data infrastructure, and machine-learning models — while also managing design, prototyping, component selection, integration, testing, deployment, monitoring, maintenance, and updates.
Devices, accelerators, gateways, and sites with different capabilities and lifecycles.
Runtimes, orchestration, streaming, storage, and connectivity across locations.
Training, packaging, promotion, versioning, and behavior in the real world.
What it creates
Kubia turns fragmented edge-AI projects into a repeatable platform operation.
Platform overview
Simulate, deploy, and stream data — one control plane across every environment.
Validate models, environments, and multi-agent behavior before hardware ships.
Train, deploy, monitor, and update models across decentralized infrastructure.
Stream and route data between devices, edge sites, and the cloud.
How Kubia works
Define the business outcome, operating environment, models, data, hardware, and software requirements.
Test model behavior, environmental interactions, infrastructure requirements, and system interoperability.
Package and distribute models, applications, and data services across the required edge environments.
Monitor models, software, hardware, data flows, reliability, and business outcomes.
Collect edge data, retrain models, promote new versions, and safely update distributed deployments.
Architecture
Kubia does not ask you to abandon existing AI tools. It extends those investments into distributed environments.
Layer 01
Layer 02
Layer 03
Edge-AI complexity
A working edge-AI system combines business outcomes, services, hardware, software, machine learning, data, testing, integration, sourcing, and ongoing operations. Every one of those decisions has to hold together across many sites and device types.
Kubia reduces time to value while decreasing dependence on bespoke platform engineering and services.
Business outcome
Architecture and design
Prototype and simulation
Component selection
Sourcing and integration
Testing and validation
Deployment
Run and operate
Use cases
Illustrative applications of the Kubia platform across distributed environments.
Detect failures earlier and continuously improve models using data collected from operating infrastructure.
Deliver the right model from the nearest available infrastructure.
Operate and continuously improve AI across thousands of distributed devices.
Business outcomes
Reduce custom engineering for each deployment
Shorten the path from prototype to production
Use existing cloud, AI, data, and infrastructure investments
Manage multiple models across diverse hardware
Improve reliability through consistent monitoring and updates
Collect real-world data for continuous model improvement
Support new digital services and AI-enabled business models
Reduce dependency on ongoing custom integration services
Ecosystem
Kubia gives ecosystem participants a common platform for building reusable edge-AI offerings instead of rebuilding a solution for every customer.
Ship devices with a managed software and model layer already in place.
Replace bespoke per-customer builds with a repeatable delivery platform.
Package hardware, software, and models into deployable edge-AI offerings.
Turn MEC footprints into addressable destinations for enterprise AI workloads.
Extend model development and data tooling into distributed environments.
Operate AI across sites and devices with one consistent set of controls.
Supporting technology we built
Alongside the distributed AI platform, Kubia owns and builds the spatial digital twin (SDT): a virtual one-to-one representation of a physical space, generated with Gaussian Splatting from existing security camera infrastructure. It is the perception layer that makes edge AI aware of where things actually are.
See how Kubia can help your organization move AI from centralized experimentation into real-world edge environments.
