Bring the Platform to the Mission

The Complete Kubeflow Stack, Hardened for the Tactical Edge

Train, serve, and monitor models where your data lives—no cloud required. Reactor packages the industry-standard Kubeflow ecosystem into a portable, air-gapped platform for contested, disconnected, and austere environments.

Built on the Industry Standard for ML Workflows

Reactor is built on Kubeflow—the open-source ML platform developed by Google and adopted by AWS, Azure, and enterprises worldwide. We take this proven foundation and solve the deployment, integration, and hardening challenges that prevent it from operating at the tactical edge.

Enterprise ML platforms assume datacenter infrastructure and persistent connectivity. Your mission operates at the tactical edge. Reactor delivers the full ML lifecycle - data ingestion through model serving - on hardware you can carry into contested, disconnected, and austere environments.

Reactor delivers an opinionated, tested, deployable stack. We've made the architectural decisions, validated the component versions, and hardened the configurations—so your team focuses on actionable insights, not infrastructure.

CURRENT CHALLENGE

Enterprise ML Can't Deploy Forward

Current ML platforms assume datacenter infrastructure and persistent connectivity:

Cloud Dependency:
Training and inference require continuous network access
Infrastructure Requirements:
Kubernetes clusters, GPU farms, and storage arrays
Security Constraints:
Sensitive data cannot leave tactical environments
Operational Complexity:
MLOps requires specialized teams at central sites

OUR SOLUTION

AI Factory That Fits in a Pelican Case

Reactor packages the complete ML lifecycle into a single deployable unit:

✓ Single-Device Deployment:
Complete platform on one edge compute device

✓ Fully Disconnected:
Train and serve models without any connectivity

✓ GitOps-Managed:
Declarative config with offline update bundles

✓ Zero-Trust Security:
mTLS, encrypted secrets, air-gapped by design

Full Stack

ML Lifecycle

Single

Device
Deployment

0

Cloud
Dependencies

100%

Offline
Capable

Why Reactor?

Single-Device, Full Stack

The complete Kubeflow ecosystem on one edge compute device. No racks, no datacenter, no component sprawl.
Air-Gapped by Design

Train and serve models without connectivity. Not a cloud platform adapted for edge—engineered from the ground up for disconnected ops.
NVIDIA-Optimized

Native support for DGX Spark and Jetson platforms. Distributed training operators for PyTorch, TensorFlow, and XGBoost pre-configured.
GitOps-Native

Flux-managed declarative configuration. Every component, every secret, every policy version-controlled and auditable.
Zero-Trust Throughout

mTLS via Istio, SPIFFE/SPIRE identity, Vault-managed secrets, OPA policy enforcement. Security architecture, not security features.
Secure Reachback

When networks are available, tunnel-based management without exposed ports. Sync models, pull updates, maintain oversight—on your terms.