Self-hosted AI infrastructure

Your AI.Your hardware.Your rules.

zOvermind coordinates language models, media generation, and specialized workflows across hardware you own. Local-first by default. Optional cloud overflow, only with your own keys.

Get launch updates See how it works

Private pilot work begins with NVIDIA DGX Spark and GB10 systems.

Routing topology
LLMLocal endpoint
MediaOwned GPU
EdgeCapability node
CloudExplicit opt-in
zOvermindControl plane
Proven across owned nodes: Spark has served voice from Orin, while a separate GPU worker completed media jobs under the cluster.

The operating problem

Owning the hardware should not mean owning every integration failure.

01

One workload, too many control panels.

Model servers, media tools, automations, and device agents each expose a different endpoint and a different operational story.

02

Capacity exists, but placement is manual.

A second node adds hardware, then adds routing work. The fleet needs to understand declared capability, not rely on fragile hostnames.

03

Cloud fallback is often a hidden default.

Private infrastructure deserves an explicit contract. Local stays local unless an operator enables a provider, supplies a key, and chooses a policy.

One infrastructure layer

Give every workload a stable place to land.

zOvermind sits below your chat apps, workflow builders, and custom code. Those tools connect to the platform instead of one fragile machine.

One platform API layer

Serve local language and media workloads behind stable, OpenAI-compatible endpoints. Downstream tools stay decoupled from the node and engine doing the work.

https://your-stack.local/v1

Cross-node job placement

Delegate supported work by declared capability and available capacity. Voice synthesis has run on Orin and image generation on a separate NVIDIA GPU worker, while clients kept one stable entry point.

control planevoice nodemedia node

Local media pipelines

Coordinate image, video, music, and voice workloads on the GPUs you operate.

Live operational topology

See services, nodes, health, and placement as one system instead of a collection of disconnected containers.

Cloud by explicit choice

Enable providers individually with your own credentials, then choose which eligible workloads may use them.

How it works

Your tools see one platform. You keep control of where the work runs.

01

Start on supported hardware.

The first private pilot is being prepared for NVIDIA DGX Spark and GB10 systems, with the platform and selected workloads configured together.

Pilot first
02

Point tools at a stable API.

Connect chat clients, workflow systems, MCP-compatible tools, and custom code to the stack rather than a single model host.

OpenAI-compatible
03

Let policy place supported work.

Delegate eligible jobs to capable nodes without changing the client endpoint. Cloud remains a separate opt-in route with its own credentials and policy.

Node aware

Specialized modules

One platform. Workflows shaped for the job.

Infrastructure matters when it carries real work. zOvermind modules combine local models, governed tools, and domain-specific review steps without turning every use case into a separate AI island.

Healthcare pilot

Case management

The NCM module turns mixed workers' compensation records into sourced report drafts that a nurse can review, correct, attest, and export.

  • PDF, Word, scan, and phone-photo intake
  • Source-aware report drafting and review
  • PHI-local workflow by default
Customer templates require separate validation.
Working modules

Education and training

The education module creates isolated hands-on cyber ranges with teacher oversight and connects local AI to guided Arduino and ESP32 project workflows.

  • Local AI tutoring with instructor controls
  • Isolated, randomized cyber exercises
  • Edge hardware projects and workbench tools
Designed for on-premises learning environments.
Workflow validation

Manufacturing and fabrication

The fabrication module carries custom artwork from design through surface layout, material planning, quoting, calibration evidence, and controlled release.

  • Revision-bound design and placement plans
  • Material estimates and production artifacts
  • Operator-reviewed calibration and release gates
Physical production remains machine-specific and operator-approved.

Cloud by choice

Cloud is explicit, not automatic.

You decide provider by provider, policy by policy, and workload by workload. With no provider enabled and no valid key configured, cloud routing is unavailable.

Default routelocal_only
External providersdisabled
Provider credentialsnot configured
Optional overflowoperator enabled

Hardware direction

Begin with what is proven. Grow the matrix with evidence.

zOvermind is designed for heterogeneous infrastructure. The first customer path is narrower: a configured DGX Spark pilot. Join the list and tell us which hardware should be validated next.

Proven internally

NVIDIA GPU workers

Cross-node media execution is proven on an x86 NVIDIA worker. General packaging is still being validated.

Proven internally

Jetson edge nodes

Orin onboarding, hardware telemetry, and remote voice execution have been exercised on real hardware.

Roadmap demand

Apple Silicon and CPU nodes

Detection, packaging, and runtime behavior still require hardware proof.

Launch list

Run AI on your own terms.

Get one useful message when zOvermind pilot access opens. Tell us what hardware you run so the validation roadmap follows real demand.