Services and capabilities
Each engagement is shaped around the actual users, operating constraints, system ownership, and desired outcome for companies running ai on their vps, cloud environment, dedicated server, local infrastructure, or controlled network.
Open-source model selection and testing can combine GPU inference and model serving with a defined response to “Sensitive data cannot leave a controlled environment.” Scope identifies the responsible owner, affected journey, and evidence required before release.
Local, VPS, cloud, and hybrid inference can combine containerized deployment and private APIs with a defined response to “Hosted AI terms do not meet policy needs.” Scope identifies the responsible owner, affected journey, and evidence required before release.
Private APIs and internal application access can combine authentication and network controls with a defined response to “Existing hardware is poorly matched to the model.” Scope identifies the responsible owner, affected journey, and evidence required before release.
GPU, memory, storage, and capacity planning can combine capacity tests and observability with a defined response to “Private demos lack monitoring or recovery.” Scope identifies the responsible owner, affected journey, and evidence required before release.
Access control, logging, and monitoring can combine model artifact and update management with a defined response to “Model updates break dependent workflows.” Scope identifies the responsible owner, affected journey, and evidence required before release.
Model updates, backups, and scaling can combine backup, failover, and scaling plans with a defined response to “Costs and latency are not measured under load.” Scope identifies the responsible owner, affected journey, and evidence required before release.
Technical and operational coverage
What shapes scope
Direct help from Faith Forge Labs
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