Managed AI Operations

Ongoing operation of production AI systems (model serving, data pipelines, retrieval, agents and applications) against defined service levels; continuous monitoring, evaluation and drift management consistent with the MLOps/LLMOps discipline described at Layer 06; incident response and resolution under a defined process; and a regular outcome review against the business metric the system was funded to move, not only technical uptime.

Entry: a system already in or near production, typically delivered through a Forward-Deployed Engineering engagement or an existing customer-built system being transitioned into managed operation, with a defined operating baseline established during onboarding. Ongoing: scheduled service-level and outcome reviews at a cadence agreed with the customer. Exit or renewal: a defined review point at which the customer confirms continuation, transition to an internal team, or scope change.

A named Scignal operations lead accountable for service levels and outcome reporting; CoralDC infrastructure operations for the underlying Layer 01/02 environment where CoralDC-provisioned infrastructure is in use; a defined escalation path to the customer's own technical and business stakeholders.

Operation follows the audit, evaluation and access-control discipline established at Layer 06 continuously, not as a periodic check; every incident, drift event and evaluation result is logged and reviewable; service levels and outcome metrics are reported on a defined, agreed cadence.

A documented operating runbook, a defined service-level agreement appropriate to the system's risk and business criticality, and a working evaluation and monitoring baseline in place before ongoing operation begins.

This is the operating expression of the brochure's "deliver, own the outcome" commitment: Scignal remains accountable for adoption, resilience and return on the initiative for as long as the managed operations engagement continues, not only through a fixed warranty period after go-live.