Institutional control
Design around school identity, permissions, approved knowledge, policies and operational ownership rather than unmanaged individual accounts.
ZhiAn YunKe is the proposed China-focused education-AI company described in the consolidated business plan. Its thesis is that schools need more than access to powerful models: they need infrastructure, institutional knowledge, assessment, governance, training and accountable operations around those models.
Empower schools with secure, private and governable AI capabilities while keeping institutional control over data, access, knowledge and educational use.
Design around school identity, permissions, approved knowledge, policies and operational ownership rather than unmanaged individual accounts.
Make governance requirements part of architecture and workflow design rather than documentation added after deployment.
Build around teaching, assessment, safeguarding, professional development and school operations.
Develop school technical teams, local graduates and partner capacity instead of creating permanent dependence on one specialist.
Aim for understandable infrastructure, licence, hosting, support and training costs rather than uncontrolled per-user experimentation.
Allow approved local and open models to improve over time without forcing institutions to rebuild their entire AI environment.
The compute, storage, networking, model-serving and operational layer that makes shared institutional AI possible.
User-facing tools plus Knowledge, Assess and Agent capabilities designed around school workflows.
Identity, permissions, audit, consent, retention, filtering, model policy and review through a common control layer.
Training for the people who teach with, govern, administer and support the environment.
LMS, SSO, APIs and school knowledge connect AI to institutional systems rather than leaving it as a separate website.
Assessment, pilot, deployment, support, model evolution and eventual migration or expansion form one operational relationship.
Begin with use cases, data, governance, users and operating constraints before deciding which models or hardware belong in the solution.
Validate a bounded set of workflows, measure adoption and operational burden, then expand only when evidence supports the next step.
AI can assist teaching, assessment and operations, but consequential academic, safeguarding and institutional decisions remain human responsibilities.
Institutional identity, policy, knowledge and workflows should remain durable even as approved model providers and serving technology change.
Prove useful workflows and governance inside one institution.
Share selected infrastructure, policy and services while preserving campus-level boundaries.
Explore common capacity, governance, model evaluation, training and assessment services for multiple schools.
Develop universities, Graduate Academy, Host Schools and an evidence-based Innovation Lab around the operating platform.
Corporate structure, leadership titles, commercial targets and registration details remain planning matters until formally established. Those items are preserved in the project record and investor/planning materials rather than presented here as established company facts.