Enterprise AI should not require a trade-off between capability and control.
Most AI platforms assume your data can leave your environment, your applications can depend on external APIs, and your organization can accept a changing model roadmap.
For many enterprises, governments, regulated industries, and critical infrastructure operators, that is not acceptable
Backboard makes it possible to deploy advanced AI systems while maintaining control over:
Data residency
Keep sensitive data within your approved cloud region, private network, or physical environment.
Model choice
Run approved open-weight, proprietary, or custom models based on performance, cost, privacy, and policy requirements.
Infrastructure ownership
Deploy in the cloud, private cloud, on-premise, edge environments, or air-gapped systems.
Operational governance
Define how models, tools, memory, retrieval, and data access behave across your organization.
Long-term portability
Avoid building your organization around a single model provider or closed AI platform.
Governance built into the architecture.
Enterprise AI cannot be governed after deployment. Governance has to be part of the system itself.
Backboard is built to give organizations more control over how AI systems are configured, accessed, monitored, and scaled.
Designed for enterprise control
Governance and data
Approved model policies
Configurable data retention
Audit-ready system activity
Access and identity
Identity and access management integration
Controlled tool and data access
Separation between users, teams, applications, and organizations
Infrastructure
Private networking options
Environment-specific deployment controls
The goal is simple: enable teams to move quickly without creating unmanaged AI risk.
Built for the workloads that cannot leave your control.
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Government and Public Sector
Deploy secure AI systems for internal knowledge, case management, citizen services, intelligence workflows, and operational automation while maintaining sovereignty over sensitive information.
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Financial Services and Insurance
Use AI for internal research, compliance workflows, customer operations, underwriting support, document analysis, and employee copilots without exposing regulated data to unmanaged systems.
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Healthcare and Life Sciences
Build private AI tools for research, operations, clinical knowledge workflows, and internal productivity while retaining control over sensitive and proprietary information.
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Critical Infrastructure and Industrial Operations
Run intelligent systems close to operational environments, including edge and disconnected deployments, where uptime, latency, privacy, and control matter.
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Large Enterprises
Create a shared AI foundation that allows business units and technical teams to build useful applications without fragmenting data, infrastructure, security, and governance.
FAQ Section
What does sovereign AI mean?
Sovereign AI means an organization retains meaningful control over the data, models, infrastructure, policies, and operations behind its AI systems. It allows organizations to use AI without giving up control of sensitive information, deployment location, or long-term technology decisions.
Can Backboard be deployed on-premise?
Yes. Backboard is designed to support cloud, private cloud, on-premise, and disconnected deployment models based on an organization’s technical and security requirements.
Can we use our own models?
Yes. Backboard is model-agnostic. Organizations can use approved proprietary models, open-weight models, internal models, or custom post-trained models depending on their use case and deployment environment.
Can Backboard operate in air-gapped environments?
Backboard is designed to support disconnected and restricted environments where external internet access is not permitted. Final architecture depends on the specific hardware, model, security, and operational requirements of the deployment.
Does this replace our existing cloud or AI providers?
No. Backboard can operate as the AI application and orchestration layer across your existing infrastructure, model providers, and internal systems. It gives your organization a more unified way to manage AI without requiring a single-vendor strategy.