Mimir Tactical
Disconnected knowledge AI designed around local inference, source-grounded answers, and controlled knowledge packs.
View details →Keep control of the boundary
Private & Secure AI Deployment
Private AI architecture and deployment for teams that need control over data, models, identity, and operations across on-premises, restricted, or disconnected environments.
Discuss this serviceThe engagement
Solve the workflow, not just the technology.
Some workflows cannot send sensitive context to a public AI service. Others need predictable availability, local inference, explicit model governance, or the ability to operate with limited or no external connectivity.
We design the full operating system around private AI: document ingestion, retrieval, model serving, identity, role-based access, evaluation, observability, updates, and administrative workflows. The result is an architecture your infrastructure and security teams can understand and operate—not an isolated model demo.
Where it fits
Where we can help
- Source-grounded assistants over controlled manuals and procedures
- Local inference for field, defense, or restricted-network teams
- Private document intelligence and review workflows
- AI services deployed to a customer-managed platform
- Hybrid architectures that separate sensitive and non-sensitive processing
- Evaluation and governance layers for approved local models
What you receive
Practical deliverables
- Workload, data-boundary, and threat-model assessment
- Reference architecture and component selection
- Containerized model, retrieval, and application services
- Identity, role, audit, and administrative control design
- Evaluation, monitoring, update, and rollback approach
- Deployment automation and operational runbooks
Delivery approach
A controlled path to a useful result
- 01
Define the boundary
Document data classifications, connectivity, identity, hardware, accreditation, and operating constraints before choosing components.
- 02
Prove the workload
Test representative models and retrieval patterns against the actual corpus, users, latency targets, and hardware envelope.
- 03
Engineer the platform
Package the services, access controls, evaluations, logging, administrative workflows, and deployment automation.
- 04
Transfer operations
Validate recovery, updates, monitoring, and support workflows with the team that will own the system after launch.
Safety and operations
Built for real operations.
Reliability, governance, and human responsibility are treated as engineering requirements. The controls are connected to how the system will actually be used, supported, and changed.
- No assumption that private deployment alone makes a system secure
- Model, application, retrieval, identity, and platform risks are handled as one architecture
- Source-grounding and evaluation are designed for the specific operational corpus
- Updates and rollback are planned for restricted-connectivity environments
Relevant work
Delivery experience behind the service
Red Hat platform engineering
RHEL, containers, Ansible automation, and operational practices for supportable private infrastructure.
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Related guidance
Bring us the hard problem.
Share the workload, data boundary, connectivity, and operating constraints. We will help determine whether private AI is justified and what it will take to run well.
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