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UnaGo - AI Operations PlatformUnaGo
Enterprise deployment

Autonomous AI agents, deployed on your terms. Your infrastructure. Your control.

Run UnaGo in a customer-controlled cloud or Kubernetes environment. Connect approved models and private systems, govern every action, and keep operational control close to your data.

Control by design

Keep control where it matters

Place autonomous execution inside the boundaries your security, data, and infrastructure teams already govern.

Data location and retention

Choose where business data, files, memory, logs, and backups are stored and how long they are retained.

Model choice

Use self-hosted, private managed, or approved external models according to workload and data policy.

Private integrations

Connect agents to internal APIs, databases, repositories, and line-of-business systems without exposing them publicly.

Identity and permissions

Apply enterprise identity, workspace roles, tool permissions, and company-scoped credentials.

Guardrails and approvals

Require human review before sensitive communications, spending, publishing, or irreversible actions.

Department workspaces

Separate each team’s data, agents, memory, files, and tools while keeping governance central.

Deployment options

Choose the operating model that fits your requirements

Balance infrastructure control, operational responsibility, and speed without changing the way agents work.

CapabilityCustomer-hostedPrivate managedUnaGo Cloud
Infrastructure ownership✓ Your teamShared operating modelUnaGo
Data boundary✓ Customer-definedDedicated environmentUnaGo-managed environment
Model options✓ Self-hosted, private, or approved externalPrivate or approved externalSupported cloud models
Internal systems✓ Private network accessPrivate connectivitySecure integrations
Operational responsibility✓ Highest control and responsibilityBalancedLowest customer overhead

Data privacy

Know exactly where data goes

Self-hosting gives your organisation control over the platform boundary. It does not automatically mean that no data leaves that boundary: configured external models, integrations, telemetry, or update services may still receive data. Full containment requires every dependency and egress path to be hosted or restricted inside the approved environment.

  • Keep storage, memory, files, logs, and credentials under customer-defined policies.
  • Control network egress and approve which models and systems may receive data.
  • Record agent activity, tool use, approvals, and outcomes for review.
  • Define backup, recovery, retention, and deletion around internal requirements.

Model freedom

Use the right model under one governance layer

Match privacy, capability, latency, and cost to each workload instead of routing every task through one provider.

Self-hosted models

Connect validated private or OpenAI-compatible model endpoints for sensitive or high-volume workloads.

Private managed endpoints

Use dedicated managed model environments when you need stronger isolation with less infrastructure work.

External frontier models

Use approved providers selectively when a task requires leading model capability.

Hybrid routing

Route agents and data classes according to policy, with clear evaluation and fallback rules.

Private execution

Connect agents to the systems your business depends on

Let autonomous teams operate across private and existing software while access remains limited, observable, and approval-controlled.

Internal APIs

Connect private services and custom applications through approved interfaces.

Governed data and knowledge

Use internal documents, databases, and approved datasets without copying them into disconnected tools.

Secure browser execution

Run browser work in isolated environments with controlled destinations, files, and credentials.

Secure cloud computer

Operate legacy or browser-only software where an API is unavailable.

Company-scoped secrets

Keep model and integration credentials inside the customer security boundary.

Approval-controlled actions

Pause before submissions, transactions, publishing, or external communications.

Deployment approach

From requirements to governed production Governed production.

Start with one high-value workflow, prove the controls, then expand across departments.

1

Define the boundary

Map data classifications, model policy, integrations, network egress, and operating responsibilities.

2

Deploy the platform

Install UnaGo in the agreed Kubernetes or private-cloud environment and connect enterprise identity.

3

Connect work

Add approved models, private systems, datasets, workspaces, and agent teams.

4

Validate and govern

Test quality, security, approvals, observability, backup, and recovery before scaling.

FAQ

Enterprise deployment questions

Clear answers about containment, models, operations, and internal connectivity.

Can UnaGo run without public internet access?

A fully offline or air-gapped configuration requires models, integrations, telemetry, licensing, and update paths to operate inside the approved boundary. Confirm these dependencies during deployment design.

Which self-hosted models can we use?

UnaGo is designed around configurable providers and private or OpenAI-compatible endpoints. The chosen model and inference server should be validated for tool use, context size, quality, latency, and hardware requirements before production.

Does any data leave our environment?

Only the services you configure should receive data. External models or integrations may receive selected request content, so model routing and network egress must follow your data policy.

Can departments use different models and tools?

Yes. Workspaces can separate department data, agents, memory, files, integrations, and permissions, with central governance above them.

Can agents connect to systems that are not publicly accessible?

A customer-controlled deployment can connect to approved internal APIs, databases, repositories, and browser-based systems within the permitted network boundary.

Who operates the self-hosted platform?

The operating model is agreed for each deployment. It should define responsibility for infrastructure, scaling, monitoring, backups, upgrades, models, integrations, security response, and support.

Plan around a real workload

Design an enterprise agent deployment around one business process. Start with your data boundary.

Bring a workflow, its systems, and its security requirements. We will map the deployment, model, integration, and governance options with you.