Grace Enterprise

Private AI infrastructure for sensitive company work.

Grace brings local models, approved cloud providers, internal knowledge and company agents into one controlled platform — on-premises, in your private cloud or in a hybrid setup.

On-premisesPrivate EU cloudHybrid deploymentSSO & rolesAuditable usage
Why Grace Enterprise

One platform instead of uncontrolled point solutions.

Teams should be able to use powerful AI without sending confidential data through a patchwork of disconnected tools. Grace creates one central, traceable and administrable workspace.

Controlled model access

Policies by team, project or data class. Local models for sensitive work, cloud models only where they are approved.

Company knowledge

Internal documents, policies and project context remain in your infrastructure and are exposed to agents deliberately.

Traceable operations

Agent runs, approvals, tools, costs and decisions remain visible instead of disappearing into isolated chats.

Shared agents & workflows

Reusable agents, skills and pipelines are defined once and distributed in a controlled way across the company.

Roles & policies

SSO, groups, permissions, provider rules and tool approvals reflect existing governance structures.

Cost controls

Budgets, provider policies and usage reporting prevent uncontrolled shadow spend.

Deployment models

Your infrastructure. Your security level.

Grace Enterprise runs the same product core in three deployment modes. Companies choose the architecture that fits privacy, performance and operational requirements.

Maximum controlOn-premises / Grace Local

Models, database, knowledge sources and agents run entirely inside the company network. Designed for sensitive data and restricted environments.

Dedicated environmentSelf-hosted private cloud

A dedicated Grace instance inside the customer infrastructure or with a selected EU hosting provider, with scalable GPU and RAM capacity.

Flexible by data classHybrid

Sensitive work stays local. Approved tasks may use Claude, OpenAI, Mistral or other cloud providers under policy control.

Enterprise capabilities

Built for operations, governance and scale.

SSO & central identity

Connect existing identity providers and control access through roles.

Audit logs

Trace model usage, tool calls, approvals and administrative changes.

Provider and model policies

Define which models, data paths and tools are permitted in each context.

Tenants & workspaces

Separate environments for teams, customers or business units.

Internal knowledge sources

Documents, repositories and policies become searchable under controlled access.

Administration & updates

Backup, rollout, versioning and system health from one control plane.

One shared core

Grace Personal remains. Enterprise builds on top.

Grace is not developed as three disconnected products. Personal, Cloud and Enterprise share chat, model abstraction, agents, tools, pipelines, knowledge and configuration. Enterprise adds the modules companies need for secure operations.

Shared product core

  • Multi-agent workspace
  • Local and external models
  • Pipelines, tools and skills
  • Project knowledge and decision log
  • Cost and approval controls

Enterprise extensions

  • SSO, roles and tenants
  • Audit and central policies
  • Private deployments
  • Company-wide agents
  • Administration, backup and support
Privacy & control

Local operation supports privacy — it does not replace governance.

Grace reduces unnecessary data transfer and makes data paths visible. Actual compliance still depends on configuration, contracts, retention rules, access controls and the selected models. Private deployment supports privacy — it is not a certificate by itself. Details: Privacy policy.

Data controlStorage location and model path remain configurable.
TransparencyLocal and external processing are clearly distinguished.
ApprovalsTools and sensitive actions can require explicit approval.
EU deploymentPrivate cloud installations can be placed deliberately in EU infrastructure.

Run Grace Enterprise as a pilot in your environment.

Start with one team, selected models and one concrete workflow. Together we define deployment, governance and success criteria.