Keep sensitive AI workloads under your control.
Private AI is not one product. It is a set of decisions about model custody, data movement, hardware, identity, retrieval, updates, and operational responsibility.
Private or local AI keeps selected models, data paths, and inference inside infrastructure controlled by the organization. It is useful when privacy, sovereignty, latency, resilience, or cost predictability outweigh public API convenience.
Placement options
- On-premises inference and retrieval
- Private cloud model serving
- Hybrid routing by data class or task
- Disconnected and edge environments
- Open-weight model evaluation and hardening
Size for the workload, not the benchmark.
We benchmark representative tasks, concurrency, latency, context size, and quality before recommending hardware. A smaller model with better retrieval can outperform a larger model carrying irrelevant context.
Control includes maintenance.
Private deployment transfers more responsibility to the owner. Update strategy, vulnerability response, model lifecycle, capacity, observability, and rollback are part of the architecture.
Questions before deployment
Does private AI mean no cloud at all?
No. Many strong architectures are hybrid. Sensitive retrieval and inference can remain private while approved cloud services handle low-risk tasks, burst capacity, or specialized models.
Can open-source models meet enterprise requirements?
They can for many workloads, but the answer depends on task quality, language, hardware, licensing, support, and security requirements. We evaluate those factors rather than treating open source as automatically superior.
Bring us the mission, not a shopping list.
In the first conversation we map the operational outcome, constraints, deployment environment, and the shortest credible path to evidence.
Book an AI deployment consultation