Put AI inside an environment designed around your data, applications, access requirements, and operating team. 3PS brings private model hosting, software, GPU infrastructure, integration, and security into one implementation.
500+ customers served.
Backed by 100+ professionals and technical resources across our delivery network.
Bring the data, access requirements, use case, and hosting constraints.
Define the compute, models, permissions, integration, evaluation, and operating responsibilities.
We confirm the scope, deliverables, fees, and responsibilities before work begins.
DISCUSS THIS ENGAGEMENTPrivate AI is an architecture decision, not a label on a chat interface. The scope includes where prompts, documents, indexes, model files, outputs, and logs are stored, who can access them, and which external services the workflow still depends on.
We can design for on-premises infrastructure, a private cloud, or a controlled hybrid environment. The right boundary depends on the use case, data requirements, integration, capacity, and operational constraints. An on-premises deployment still needs deliberate security and maintenance.
Select the hosting approach around the task, available resources, model licensing, and evaluation. Build the interface or application through which people use the capability, including how limitations and review requirements appear in the workflow.
Connect the approved documents and business systems used to ground answers. Preserve source permissions through retrieval, define how updates reach the system, and evaluate whether responses are useful and supported by the available information.
Establish authentication, authorization, secrets handling, network access, retention, and logging. Map where sensitive data moves, what administrators can access, and how tool permissions are controlled.
Engineer compute, storage, networking, deployment, monitoring, backup, and model lifecycle procedures. Define the owners of application changes, source updates, capacity, patching, and recovery.
Record where data and model services operate and which approved connections cross that boundary.
Use representative tasks, permission checks, useful-answer evaluation, and expected failure cases.
Document operating procedures, release controls, capacity monitoring, and the responsibilities after launch.
Private hosting alone does not establish compliance, reliable answers, or complete isolation. Those requirements must be designed and evaluated against the actual environment. Hardware and licensing are selected after workload and operational requirements are understood.
No. Private AI can use controlled cloud infrastructure, on-premises systems, or a hybrid design. The engagement defines the data boundary, access model, service dependencies, and operating requirements.
Yes, where the source systems and permissions support the integration. The design must address access, document freshness, retrieval quality, and the difference between finding information and authorizing an action.
Bring the use case, data requirements, source systems, and hosting constraints. We will map the complete environment and the decisions needed to build it.
DISCUSS A PRIVATE AI DEPLOYMENT