AI ShuPai Enterprise Data Asset Platform
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Methodology & Trust

Keep enterprise data, rules and outcomes in your control. Make execution traceable and verifiable.

In the age of accessible AI, advantage comes from operational discipline rather than model size. AI ShuPai structures policy applications, intellectual property, contracts and project operations into enterprise-owned data assets, then executes work through controlled business agents with rules, evidence and human review.

  • 4Core business use cases
  • PrivatePrivate deployment supported
  • TraceableEvidence retained at key steps

We do not build AI that only chats,
or software that only stores data.

Traditional digitization · ERP / CRM / OA

Systems of record

Information moves from paper and human memory into software. Data becomes searchable, but the work still depends on people.

ShuPai intelligence · trusted business agents

Systems of execution

AI understands enterprise data and carries out bounded work: reviewing contracts, preparing applications and supporting business operations.

From scattered files to trusted business outcomes

  1. 01
    Scattered filesContracts, policies, application materials, patents, finance and project documents live across disconnected locations.
  2. 02
    Structured data assetsExtract, clean and model information into enterprise-owned, maintainable data objects.
  3. 03
    SOP and rule-driven executionControlled agents operate within business rules; key steps remain inspectable and reviewable.
  4. 04
    Business outcomesProduce reviewable outputs for policy matching, certifications, intellectual property and project operations.
  5. 05
    Ledger update and improvementWrite accepted results back to formal ledgers and build a traceable, reusable feedback loop.

Enterprise AI must be controlled, inspectable and reviewable

ShuPai applies rule boundaries, evidence records and human checkpoints to agent execution, with deployment options that can keep enterprise data inside the customer environment.

  • ◈
    Execution within rule boundariesAgents follow SOPs and rule packs with human approval at critical checkpoints.
  • ▤
    Evidence at key stepsFiles → objects → rules → outcomes, with traceable sources for critical decisions.
  • ◫
    Human confirmationPeople review material criteria before formal outputs are produced, keeping responsibility clear.
  • ⬢
    Private deployment supportedLocal deployment can support offline operation, subject to the actual configuration.

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AI ShuPai · Product Demo

How enterprise files become reviewable outcomes

Upload files → read information → organize materials → human review → generate outcomes

This simulated workflow uses sample data and does not represent a specific client or application result. Actual outcomes depend on source completeness, deployment configuration and human review. The demo interface is currently presented in Chinese.