Business AI. On your terms. Around your data.
Local, on-premises or private hosted AI can give you more control over where models run and how business information is used. We design the deployment around the work—not the hype.
Private AI is a separately scoped implementation: models and supporting applications deployed in a selected environment with defined data access. Local AI can run on client-owned hardware; private hosted AI runs in a selected hosting environment. The right choice depends on capability, capacity, data requirements and ongoing operating cost.
More deployment control. Real capability tradeoffs.
Private AI requires hardware or hosting capacity, maintenance and evaluation. Smaller local models may offer different quality or speed than third-party systems. Offline AI also needs local source data and a controlled update process. We compare those limitations openly; no model or architecture offers guaranteed accuracy or absolute secrecy.
Choose the boundary before choosing the model.
A useful AI design starts with the information involved and the people who should be able to use it. We map the intended workflows and data paths before deciding whether local processing, private hosting or an approved third-party platform makes the most sense.
Local and on-premises models
Evaluate models that can run on client-owned systems. Hardware memory, processing capacity and model size affect speed and capability; local deployment is not automatically equivalent to the largest hosted models.
Private RAG and business knowledge
Retrieval-augmented generation (RAG) can supply selected documents to a model when answering questions. Scope source access, indexing, permissions and document refresh so the system uses the right knowledge for the right users.
Governed data flows
Decide what enters the system, where prompts and outputs are retained, and whether any external services are involved. Deployment location alone does not establish confidentiality or prove compliance.
Useful, tested workflows
Evaluate answers against real tasks and known reference material. AI can be wrong, including when it uses your own documents, so important decisions still need human review.
Implementation and ongoing guidance are different services.
A custom private AI deployment needs its own scope for infrastructure, integration and maintenance. It can sit alongside your current IT provider. Our existing Managed AI service covers recurring policy, approved-tool guidance, licensing, employee training and consultation—not a bundled private AI build.
Pilot before scaling
Choose a bounded use case, test representative documents and measure whether the output helps the team. Agree what success looks like before investing in a wider deployment.
Plan operations
Identify who manages model updates, storage, backups and capacity. Establish a way to handle changing source documents and support users as their workflows evolve.
Keep the right integrations
Some tasks still benefit from third-party AI or existing productivity tools. A hybrid approach can be sensible when its data flows and dependencies meet your requirements.
Common questions
Can AI run without sending our documents to a public AI service?
A local deployment can be designed to process documents without a public AI service. The full design must account for models, applications, retrieval, logs, updates and integrations. We review those paths rather than assuming every component is local.
What is private RAG?
Private retrieval-augmented generation connects a model to selected business material in a defined environment. It retrieves relevant context for an answer; it does not guarantee correctness or replace access permissions and human review.
Can private AI work offline?
Some local model workflows can operate offline if models, documents and dependencies are available locally. We assess the application and design update and recovery processes before proposing disconnected operation.
Is private AI included with Managed AI?
No. Private AI infrastructure, custom integrations and implementation are separately scoped projects. Managed AI includes policy, approved-tool guidance, platform selection and licensing, training and ongoing consultation; it is standard with Managed IT or available standalone.