Pick one useful task.
Compare knowledge, coding, and application workflows. A model endpoint needs a suitable client and a review process to become a useful application.
Explore use cases →Learn
Make a practical start with private AI.
Start with work you can assess. These guides help you choose a workflow, check the complete data path, and decide what evidence would justify a wider deployment.
Understand the whole workflow.
Read how model and client fit, data boundaries, human review, and operational ownership affect the result.
Read the evaluation guide →Evaluate against evidence.
Use six checks covering a representative test set, data path, configuration, controls, operations, and the decision to proceed.
Use the free checklist →Before you install
Check fit, limits, and cost.
Private inference does not make every connected client or service private. Review where data travels, which hardware and runtimes are in the published support matrix, and what your team will operate.
Data and security
Trace the customer-local data plane, hosted services, optional connections, and trust boundaries.
Review the data flow →Supported deployment
Check the current hardware and runtime profiles before assuming that multi-host, Kubernetes, or HA/DR is available.
Check supported profiles →Trial and pilot
Compare a self-directed trial with a scoped paid pilot. Infrastructure and any external provider charges are separate.
Review plans and support →Technical documentation
Ready to plan an evaluation?
The Docs hub covers installation, inference, retrieval, licensing, security review, and operations. It also separates planned concepts such as Membrane, Measure Loop, and the company agent harness from current capabilities.
Discuss one workflow
Bring the task, expected result, data boundary, and current pain point to a 30-minute conversation. Confidential documents or code are not needed to book.
Book a demo →