Principle
Data boundary first
Every workflow starts by marking what can leave the environment, what must stay private, and what requires explicit approval.
Security
Private AI starts with boundaries, not promises.
Deltabits does not claim fake certifications or one-size-fits-all privacy. We design each workflow around the data it touches, the actions it can take, and the approvals it needs.
Principle
Client-controlled infrastructure
Pilots can run on AWS, a private cloud, or client-owned provider accounts so the business controls where critical context lives.
Principle
Human approval for critical action
Payments, destructive updates, customer-facing sends, and high-risk operations can be gated before execution.
Principle
Readable audit trails
Agents and automations should leave behind enough context for an operator to understand what happened and why.
Deployment modes
Choose the right privacy posture per workflow.
Mode 01
Private/local model
Use self-hosted or local models when the workflow touches sensitive internal context.
Mode 02
Client-owned provider keys
Use approved model APIs through accounts and limits controlled by the client.
Mode 03
Hybrid routing
Keep private data local while lower-risk tasks use approved external services.
Control
Human approval gates
Hold customer-facing, financial, destructive, or high-impact actions for review.
Need a security review before the audit?
Send the workflow and current tool stack. We can identify obvious data-boundary concerns before scoping an implementation.
Contact DeltabitsStart with the audit
Find the work your business should stop doing manually.
Get a workflow map, private AI architecture, connector plan, and pilot roadmap before committing to a build.