Platform / Architecture

Target architecture · in development

One operating story. From hardware to workflow.

Ataraxia brings two product responsibilities together: Anvil prepares and maintains the private AI environment; Atelier is being designed to manage the agent workflows that run within it.
The Ataraxia platformTarget architecture · in development
AtelierAgent & workflow lifecycle
BuildGovernRunObserve
Deploy workflows ↓↑ Operational feedback
AnvilPrivate AI infrastructure
ProvisionConfigureMaintain
Compute & GPUs Data & storage
Customer-controlled environment

Use local models first, and only Frontier models when necessary, and approved

Clear boundaries.
Shared operational context.

An infrastructure orchestrator manages clusters, compute, networking, and platform services. An AI orchestrator coordinates models, tools, data, and approvals. Ataraxia’s design connects these layers without treating them as the same thing.

Private execution is the default product direction. Any external model endpoint needs an explicit policy and a clear account of what data can leave the environment.

Connected or fully air-gapped deployment, disconnected lifecycle management and multi-site administration give you the flexibility and security you need in restricted markets.

Responsibilities

Know what each layer owns.

LayerResponsibilityProduct
InfrastructureCluster provisioning, configuration, health, updates, and recoveryAnvil
Workflow developmentGuided design, tools, retrieval, testing, and versioningAtelier
ExecutionDeploy and run approved workflows with defined resource needsAtelier runtime on Anvil
Model routingSelect an approved endpoint under data, latency, and cost policiesAtelier Gateway
GovernancePermissions, approval checkpoints, and execution recordsAcross the workflow lifecycle
ObservationLink execution evidence to evaluations and reviewed changesAtelier Observe

The improvement loop

Evidence should lead to action.

The lifecycle pipeline drives continual improvement. The Observation agent is always looking for ways to improve.

  1. Observe execution

    Collect sources, tool calls, failures, latency, and resource use.

  2. Evaluate a concern

    Compare results against defined graph policies, expected behavior, and human feedback.

  3. Prepare a change

    Adjust the workflow graph, data retrieval, model choice, model training, or configuration in a new version.

  4. Review and redeploy

    Validate the change, apply policy checks, approve it, and follow its behavior in operation.

Build with us

Start with one useful workflow.

Bring your infrastructure, your operational constraints, and a problem you need solved. Together, we can define a focused pilot and what success should look like.

Discuss a design partnership