Commercial platform
May fit when its contractual data handling, available controls and capabilities meet your requirements. Review the specific product and plan rather than assuming all hosted AI behaves the same way.
Private AI deployment
Use AI on terms your business can accept.
Your information has value. Your customers have expectations. We help you decide where AI should run, what it can access and how the implementation will be operated.
You may need a privately hosted model, an on-premises system or better control over the tools you already use. The useful starting point is the requirement: what can leave your environment, who can access it and what the application must do.
Discuss your project →From requirement to working system
What we work through with you.
A documented deployment decision and, where commissioned, an implemented environment tested against agreed requirements. Capability, cost and operating responsibilities belong in the same conversation.
Define the boundaries
Map prompts, source documents, outputs, logs and backups. Establish where each may be stored or processed and who may access it. Translate business requirements into checks the implementation can be tested against.
Compare workable options
Assess suitable commercial products, private cloud hosting and on-premises models. Compare quality on representative work, integration options, provider terms, infrastructure requirements and support needs.
Implement the agreed environment
Configure the selected services and access controls, connect the approved data sources and build missing components within scope. Document external dependencies and any actions that still reach third-party services.
Test and prepare for operation
Check representative tasks and access boundaries. Agree monitoring, updates, recovery and escalation responsibilities before handover, including who will respond when the system needs attention.
The choices behind the implementation
Make the tradeoffs clear.
Private cloud
Can provide more control over deployment and model selection. Hosting, identity, network access and ongoing maintenance still need owners and a budget.
On-premises
May fit a requirement to process information inside your environment. Hardware capacity, model capability, updates and support must be evaluated; local hosting alone does not establish security or compliance.
Before you commit
Questions worth working through.
Does proprietary data mean we need our own model?
No. Proprietary information can sometimes be used through an existing product or a controlled retrieval integration without training a new model. We assess data handling and access requirements before recommending the approach.
Can the system run without sending data to external AI providers?
We can scope an approach around that requirement. It must account for the whole system, including tools, telemetry, updates and integrations—not only the model. Offline operation and its capability tradeoffs need explicit testing.
Is private AI cheaper than cloud AI?
It depends on workload, utilization, hardware, model quality and support. A server has costs even when idle. A useful comparison includes the cost of successful work, human review and maintenance, not just token pricing.
Do you certify regulatory compliance?
No. We can implement and document agreed technical controls. Your legal and policy owners determine the applicable obligations and acceptance requirements; an implementation is not a compliance certification.
A concrete next step
Tell us what must stay under your control.
We agree the scope, deliverables, timing and fees before work begins. A focused configuration or integration request does not automatically require a full custom build.