Open-source model selection and testing
This capability can combine gpu inference and model serving with focused work on sensitive data cannot leave a controlled environment.
Services and solutions
What private ai can include
Each engagement is shaped around the real system, users, constraints, and desired outcome for companies running ai on their vps, cloud environment, dedicated server, local infrastructure, or controlled network.
Local, VPS, cloud, and hybrid inference
This capability can combine containerized deployment and private apis with focused work on hosted ai terms do not meet policy needs.
Private APIs and internal application access
This capability can combine authentication and network controls with focused work on existing hardware is poorly matched to the model.
GPU, memory, storage, and capacity planning
This capability can combine capacity tests and observability with focused work on private demos lack monitoring or recovery.
Access control, logging, and monitoring
This capability can combine model artifact and update management with focused work on model updates break dependent workflows.
Model updates, backups, and scaling
This capability can combine backup, failover, and scaling plans with focused work on costs and latency are not measured under load.
Technical and operational coverage
GPU inference and model servingContainerized deployment and private APIsAuthentication and network controlsCapacity tests and observabilityModel artifact and update managementBackup, failover, and scaling plans
What shapes scope
Complexity follows the system, not a menu price.
- 01Sensitive data cannot leave a controlled environment
- 02Hosted AI terms do not meet policy needs
- 03Existing hardware is poorly matched to the model
- 04Private demos lack monitoring or recovery
- 05Model updates break dependent workflows
Direct help from Faith Forge Labs
Sensitive data cannot leave a controlled environment? Discuss the evidence and next step.
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