Complete lineageDeterministic policy guardrailsTotal human control

The enterprise does, through explicit configuration: permissions, risk classes, confidence thresholds and approval paths. AI may interpret information and recommend action, but business rules and accountable people determine what may be executed. An action that exceeds its authority cannot proceed — it escalates.

Authority is risk-based, not blanket. Routine, policy-approved actions proceed automatically. Uncertain, sensitive or high-impact decisions escalate to the person whose authority the risk class requires — with the relevant evidence already assembled, so approval is a decision, not a research task.

Only within boundaries the enterprise has explicitly approved: low-risk, reversible, policy-defined actions. Everything else requires the designated human authority. The design question is never "human or AI?" but "which authority does this class of decision require?"

The customer retains rights in the data it brings. Access inside Yebo OS is governed by the same identity model as everything else: role- and attribute-based access, permissions, delegation, entitlements and separation of duties — applied to people and AI systems alike.

Connections run through approved, controlled interfaces — APIs, events, data pipelines — with access scoped per workflow. Which systems may be read or written, by which workflows, under which approvals, is explicit configuration, and reads and writes of governed objects leave records.

Model choice is per task and per enterprise: Yebo is not dependent on one model vendor, and privately deployable models are part of the architecture. Data-handling, residency and model-exposure requirements are defined explicitly during assessment and deployment design for each enterprise — not assumed.

Cloud, private-environment, hybrid and on-premise pathways, selected according to security, latency, data-residency, sovereignty and operational requirements. Multi-tenant, dedicated or single-tenant isolation is chosen per customer risk and commercial requirements.

With an assessment, not a platform rollout: map one high-value decision or workflow — the systems, people, rules, risks and baseline performance involved. Then design, configure, integrate, govern and deploy that one workflow, measure the outcome, and expand only where evidence supports it.

Yes. Yebo maintains its own fine-tuned LLMs, SLMs and machine-learning models, and enterprises can choose to run them on-premise or in a private cloud. This keeps sensitive workflows off public model endpoints entirely, with model selection, hosting and data-exposure boundaries defined per workflow during deployment design.

The delivery model is built around one controlled workflow first, precisely so value and fit are demonstrated on a bounded scope before wider commitment. Timelines depend on the workflow chosen and the integration effort of the systems involved — which the assessment makes explicit up front.

A decision record: what was known, which rules applied, what confidence the system had, who approved, what was executed and what followed. Material decisions remain traceable to their inputs, policy context and responsible human owner.

Yes — that is the point of governing decisions as infrastructure. Execution traces, audit logs and decision records let teams examine whether the selected action reflected the available evidence and the applicable policy at the time it was taken.

Exceptions are first-class citizens: work the system should not decide escalates with its context instead of failing silently. Verification and observability — execution traces, control checks, system health, model performance — make incidents inspectable, and every governed outcome feeds back into improving the rules that route the work.