Data sovereignty and controllability challenges
When core data, model capabilities, and application ecosystems depend on external platforms, data sovereignty, security compliance, and long-term controllability face challenges.
Industry Pain Points
Fragmented systems make collaboration difficult
Data across government, education, healthcare, energy, transportation, and other key systems is fragmented, making unified national-level intelligence and cross-department collaboration difficult.
Models lack sufficient local understanding
General foundation models often struggle to fully understand local language, regulations, culture, industrial structure, and governance logic, leading to unstable implementation outcomes.
A systematic implementation path is missing
AI applications can remain as point tools, lacking a systematic implementation path from national strategy and public services to industry production sites.
Solution
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Build a sovereign AI fact base
Integrate government data, industry knowledge bases, local corpora, business systems, and public service data to form unified, trusted, governable local business understanding.
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Create an autonomous and controllable model foundation
Support local training, private deployment, permission management, security audit, and continuous evaluation to keep data, models, and service capabilities locally controlled.
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Deploy a national-level multi-agent system
Build professional Agents that can collaborate across policy analysis, public services, education, healthcare, industrial governance, emergency response, and other scenarios.
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Connect the perception-judgment-execution-feedback loop
Connect government systems, industry platforms, and end applications so AI moves from assistant tool into real governance, service, and production sites.
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Co-build a local AI ecosystem
Work with governments, universities, enterprises, and developer ecosystems to continuously accumulate models, applications, talent, and industry experience locally.
Expected Outcomes
Autonomous and Controllable Capability
Data, models, and key AI capabilities become more autonomous and controllable, forming national-level AI infrastructure that can evolve long term.
Public Service Efficiency
Public service efficiency improves, and education, healthcare, government services, and other public-facing scenarios gain more inclusive and personalized intelligent services.
Key Industry Upgrade
Key industries gain stronger prediction, dispatch, governance, and risk-response capabilities, driving intelligent industrial upgrading.
Continuous Ecosystem Growth
Local AI talent, applications, and industrial ecosystems continue to grow, forming a replicable and scalable sovereign AI model.