See every node
One inventory across every location, with CPU, memory, GPU/accelerator, storage, and connectivity health. Failed workloads restart or reschedule automatically.
Fleet and node management
Siden Beam AI
Deploy AI models to the edge, faster.
Run your whole edge AI fleet from one console. Every site, every node, every model version, with one-step rollback when an update misbehaves.
Built for operations inMiningManufacturing & RoboticsEnergy / Oil & GasDefenseSecurityRetailTransportation & LogisticsAviation & Maritime
The problem
Deploying AI is easy to start. Scaling it across the edge is hard.
11%
of edge AI deployments reach full production.
Spectro Cloud, State of Edge AI (320 respondents)
73%
name model lifecycle (updates, accuracy drift, version consistency) as a blocker, the most-cited of any.
Fierce Sensors Edge AI Survey with the EDGE AI Foundation, April 2026 (74 completed)
Teams wire together separate tools for orchestration, model serving, and fleet management, and still struggle to keep every node on the right version.
What Beam AI does
One console for the whole fleet.
Push a trained model to many sites and keep it running, up to date, and under control.
Ship to a footprint, not a box
Group nodes into footprints by location, hardware class, or label, then deploy a model to the whole footprint. Each model is served behind OpenAI-compatible or KServe v2 endpoints.
Model deployment and serving
Undo in one step
Every model is a versioned artifact with a record of what runs where. Push updates over the air and roll back in one step if something goes wrong.
Model lifecycle
Know it's working
Request volume, latency, and error rate per model, next to node health, with alerts when something needs attention.
Observability
Every operation is an API call. Deploy, check status, update, and roll back from CI/CD, or let AI agents do it through the MCP server.
REST API
CLI
MCP server
Business value
Turn Beam AI into business results.
Accelerate time to value
Move AI models from development into production faster, without lengthy deployment cycles.
Reduce operating costs
Cut manual deployment effort, simplify remote management, and limit unnecessary cloud data transfers.
Scale without added complexity
Expand AI deployments across locations and devices without managing each environment separately.
Improve operational resilience
Support local AI inference and reduce dependence on continuous cloud connectivity.
Why Siden
Built by a team that already runs the edge.
Before Beam AI, Siden built and operated edge platforms in homes, on aircraft, and on vessels, over networks that drop, stall, and come back. Beam AI is built on that experience, for AI.
Member of
How it works
One workflow. Every site.
- EnrollAdd a node; it appears with its hardware profile.
- RegisterUpload or point to a trained model.
- DeployTarget a node or a footprint.
- ServeApps call it locally on a standard API.
- ObserveWatch requests, latency, errors, and health.
- Update or roll backPush updates over the air, or roll back in one step.
You declare what should run; every node converges on it. The control plane holds which models, at which versions, run on which nodes. A node that loses its link keeps operating and catches up when connectivity returns.
Industries
Built for industries that can’t wait for the cloud.
Mining
Applications
Equipment health and safety monitoring on haul trucks, conveyors, and processing plants
Outcome
Less unplanned downtime and safer sites
Manufacturing & Robotics
Applications
Visual quality inspection, predictive maintenance, and vision models on robots
Outcome
Less downtime, higher quality, and one update rolled out to every line at once
Energy / Oil & Gas
Applications
Remote equipment monitoring and anomaly detection at wells, pipelines, and substations
Outcome
Faster issue detection and fewer field visits
Defense
Applications
Sensor fusion and situational awareness on forward-deployed systems
Outcome
Faster decisions at the tactical edge, with inference that keeps running when disconnected
Security
Applications
Video analytics, perimeter monitoring, and access control across sites
Outcome
Faster threat detection and fewer false alarms reaching operators
Retail
Applications
In-store analytics, shelf and inventory monitoring, and loss prevention
Outcome
Fewer stockouts and faster insight into store operations
Transportation & Logistics
Applications
Fleet analytics, asset tracking, and yard and warehouse monitoring
Outcome
Greater visibility and faster decisions
Aviation & Maritime
Applications
Onboard AI inference with intermittent connectivity
Outcome
Lower cloud dependency and better onboard services
Works with
Your models, your hardware, standard APIs.
Models in
- ONNX
- GGUF
- safetensors
- TensorRT
APIs out
- OpenAI-compatible
- Open Inference Protocol (KServe v2)
Runtimes
- ONNX Runtime
- Ollama / llama.cpp
- vLLM or Triton on GPU nodes
Runs on
- Linux edge nodes
- K3s or MicroShift
- CPU/NPU devices to GPU servers
Beam AI deploys and serves the models you have trained. It does not train them, and it is not tied to one cloud.
FAQ
Questions, answered.
The basics
What is Siden Beam AI?
A control plane for deploying, serving, and operating AI models across a fleet of edge devices, from one console.
Does Beam AI train models?
No. Beam AI deploys and serves the models you have trained. It does not train them, and it is not tied to one cloud.
How do we get started?
Join the waitlist. We'll be in touch about next steps, including a pilot on your own nodes.
How it works
What are the moving parts?
Two. A central control plane (console plus API) holds the desired state: which models, at which versions, run on which nodes. A lightweight agent on each edge device carries out instructions locally and reports status back.
Which model formats are supported?
ONNX, GGUF, safetensors, and TensorRT.
Generative AI, or just vision and predictive models?
Both. Language models are served behind an OpenAI-compatible API, and vision and predictive models behind the Open Inference Protocol (KServe v2).
Can we run more than one model on a node?
Yes. Multiple models run side by side on a node, each behind its own standard API, and can be added or removed on demand.
How do updates and rollbacks work?
Every model is versioned. You push updates over the air to a footprint, and roll back in one step if something goes wrong.
Can we automate deployments?
Yes. Every operation is an API call, so you can deploy, check status, update, and roll back from CI/CD, or let AI agents do it through the MCP server.
What do we see for monitoring?
Request volume, latency, and error rate per model, next to node health (CPU, memory, GPU, storage, and connectivity), in one console, with alerts when something needs attention.
What if a workload crashes on site with no one there?
Beam AI detects the failed workload and restarts or reschedules it automatically. Minimal on-site work is a core design goal.
Hardware and connectivity
What hardware does it run on?
Linux edge nodes, on K3s or MicroShift, from CPU and NPU devices to GPU servers.
How small can a node be?
Small CPU or NPU devices with no accelerator work, as long as they run Linux and a container runtime.
Do we have to buy your hardware?
No. Bring your own. Beam AI is hardware-agnostic, from GPU edge servers to small devices.
What happens when a device loses its connection?
It keeps running the model it has. When connectivity returns, it catches up to the latest desired state automatically.
Security and data
Where does our data go?
Inference runs locally, so raw data such as video and sensor streams stays at the edge. The control plane holds operational state (which models and versions run where) and telemetry such as request rates and health, not your raw inputs.
Can Beam AI run in disconnected or secure environments?
Beam AI is built for constrained and intermittent networks. Nodes keep serving their models through long outages and catch up automatically when the link returns. The control plane runs in Siden’s secure cloud, and data is encrypted in transit. For specific security requirements, talk to us.
Pilot and pricing
What does a pilot include?
A fixed-fee pilot on your own hardware, run alongside your team in four milestones: enroll your nodes, register a model, deploy it, then run a staged update across your footprints. We agree success criteria up front, and you keep everything we configure.
What do we need for a pilot?
A couple of Linux edge nodes to start, a trained model in a supported format, and a target use case. We handle the platform.
How is Beam AI priced?
Typically a platform fee plus a per-edge-node subscription, scoped to your fleet. Pilots are fixed-fee.
Your model. Your nodes.
Start with a pilot: see it deployed, served, observed, updated, and rolled back on your own fleet.