S/Runtime
Learning and workforce AI
Unify student services, HR, workforce training, research administration, policy, and knowledge workflows with controls for education records, privacy, and human oversight.
Each example starts with the work that slows down today, then names the context, controls, and outcome a pilot would need to verify.
Advisors and service teams need policy, program, student-record, financial-aid, and support context safely.
S/Runtime assembles role-appropriate context and routes sensitive recommendations through human review.
Controls to verify: FERPA scopes; Disclosure logs; Advisor roles; High-impact review.
Policies, grants, research workflows, HR guidance, and program requirements are scattered across teams.
The context layer maps owners, effective dates, source authority, and related workflows for trusted answers.
Controls to verify: Source authority; Policy freshness; Research data scopes; Access review.
HR and training teams need to personalize guidance while protecting employee data and avoiding uncontrolled decisions.
Agents draft recommendations, reminders, and case summaries with role limits and human approval for sensitive actions.
Controls to verify: Employee data minimization; Manager scopes; Approval gates; Audit trails.
The pilot scope should identify the applicable policy, the accountable reviewer, and the evidence required to reconstruct a consequential run.
Focus: Education records, student PII disclosure, consent, exceptions, and written agreement expectations.
S/Runtime: Student-record scopes, disclosure logging, consent-aware workflows, and vendor-access evidence.
Focus: Children's personal information and parental consent workflows where applicable.
S/Runtime: Age-aware data handling, restricted context packs, and approval gates for youth-facing workflows.
Focus: Regional privacy rights, sensitive data, purpose limitation, deletion, correction, and access requests.
S/Runtime: Privacy request routing, regional policy scopes, retention controls, and data lineage reports.
Focus: Workforce data privacy, role-based access, human oversight, and high-impact decision review.
S/Runtime: Human-in-the-loop decisions, candidate/employee data minimization, and AI-use transparency evidence.
Use workflow run traces and reviewer outcomes to compare “Advisor response time” with the agreed pre-pilot baseline.
Use workflow run traces and reviewer outcomes to compare “Policy search reduction” with the agreed pre-pilot baseline.
Use workflow run traces and reviewer outcomes to compare “Student case backlog” with the agreed pre-pilot baseline.
Use workflow run traces and reviewer outcomes to compare “Training completion support” with the agreed pre-pilot baseline.
Architecture session
We will map the context, systems, decisions, controls, actions, and success measures together.