Ridgeline AI

Sovereignty · who owns what

Your operating record stays yours.

Seven governed layers keep data, meaning, model choice, workflows and decision authority portable—so the organization can keep operating as technology changes.

Review the sovereignty model →
01 / Data boundary

Operational records Residency, retention and access remain explicit

  • Ownership
  • Residency
  • Export

Seven layers build from data boundary through compute, model choice, semantic definitions, workflows and decision rights to a retained operating record.

1 / 7
01 / assurance is a gradient

Chosen per workload, not sold as a toggle.

The same operating layer runs at three levels of assurance. Sensitive records and regulated data sit at the strong end of that gradient.

HostedZero-data-retention termsFastest path to a working operating layer. Model calls run under zero-data-retention terms: nothing stored, nothing trained on, nothing human-read.
Dedicated environmentIsolated tenancyA separate environment for your operation, with its own data boundary, keys, and network posture — suited to sensitive records and regulated workloads.
Your own cloudClient-controlledThe layer runs inside the environment you already control and audit. Residency, retention, and access follow your policy, not ours.
02 / who owns what

Stated plainly, in writing.

Yours

  • Your operating data
  • Your metric definitions and their version history
  • The decision ledger and outcome history
  • Any model tuned on your operations
  • Exportable in open formats, at any time, without fees

Never

  • Your data sold, shared, or used to train foundation models or another client's intelligence
  • Your prompts and outputs retained by model providers
  • Definitions or model versions changed beneath you without your approval
  • Export fees, egress penalties, or audit-as-revenue

Ours

  • The platform and runtime
  • The agent framework
  • The delivery teams
  • The product roadmap
03 / commitments

The exit door stays unlocked.

No model lock-in

The layer is model-agnostic. Your ontology, workflows, and record live outside any model, so models stay swappable inputs. A better model next year is an upgrade, not a migration.

Your record trains nothing but yours

Where a workflow earns a task-specific model tuned on your operations, that work stays yours. Your operating playbook does not become a competitor's feature.

No rules changed beneath you

Definitions are versioned and model versions are pinned per workflow. Changes route through your approval, on your calendar — never silently mid-season.

Human authority is retained

Recommendations carry their sources, and consequential actions are designed to require an authorized human decision. Sovereignty includes who is allowed to act.

Review the terms against your own governance model.