Where does the system run?
We identify the client environment and the systems it must connect to. Private AI can be deployed in a client cloud account, VPC, or on-prem hardware.
Security & data handling
Trust begins with a defined boundary.
For every engagement, the deployment environment, data access, and AI use need to be explicit. We design those decisions into the work rather than leave them as assumptions.
Security principles for scoping and delivery. Specific controls are agreed for each engagement.
Principles
Questions we resolve before a system goes live.
Which data can each tool use?
We agree on data boundaries before choosing models, agents, and integrations. Data residency is part of the design when the use case requires it.
How is model behavior examined?
Evaluation suites, inference audit trails, and version pinning provide a basis for review and release decisions in private AI work.
What does acceptance require?
We define checks for the intended behavior, including relevant failure modes, before implementation and deployment.
Who is accountable for release?
Senior engineers own architecture, review, and deployment decisions. Agents support the build under that responsibility.
Engagement-specific controls
Make requirements explicit.
Security and regulatory obligations differ by organization and workflow. We discuss the required controls during scoping and document the agreed boundary for the work. This page is a statement of operating principles, not a certification or compliance attestation.
Discuss your requirementsA conversation with the people doing the work
Start with the boundary.
Tell us where the system must run and what information it may use.