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

Optional external model access is a separate, explicit policy decision.

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.

Disconnected lifecycle management and multi-site administration are roadmap objectives, subject to engineering validation.

Responsibilities

Know what each layer owns.

LayerResponsibilityProduct direction
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 policiesPlanned Atelier capability
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 planned lifecycle keeps a person accountable for consequential changes.

  1. Observe execution

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

  2. Evaluate a concern

    Compare results against a defined test set, expected behavior, and human feedback.

  3. Prepare a change

    Adjust the workflow, retrieval, model choice, 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 worth solving. Together, we can define a focused pilot and what success should look like.

Discuss a design partnership