AI ShuPai Enterprise Data Asset Platform
中文 Contact Process Demo
Enterprise Trusted AI Applications Lab · Methodology-driven

Turn scattered enterprise files into
executable data assets
and verifiable business outcomes

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.

Process Demo See AI Work Inside a Process
  • 4Core business use cases
  • PrivatePrivate deployment supported
  • TraceableEvidence retained at key steps
SIMULATION · SAMPLE DATA

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.

Trusted AI Runtime

Switch between business use cases to see how a trusted agent reads files, extracts fields, validates evidence, waits for human confirmation and produces reviewable outputs. This is controlled execution, not open-ended chat.

Simulation Sample data illustrates the workflow and does not represent a specific client or actual application result. Outcomes depend on source quality, deployment configuration and human review.

Evidence chain · files → objects → rules → outcomes
▤R&D files
◫Data objects
◈Rule pack
✔Business outcome
Agent execution log High-Tech Certification
Human review: commercialization criteria
Extracted fields · structured output
    Output Material gap list and application evidence chain

    Four layers for sustainable enterprise AI

    01

    Enterprise Data Foundation

    Structure contracts, application materials, patents, finance and project documents into maintainable, reusable enterprise data assets.

    Private deployment supported
    02

    Trusted Business Agents

    Execute tasks under SOPs and rule packs, with key steps inspectable, controlled and traceable instead of opaque generation.

    Controlled intelligence
    03

    Process and Rule Engine

    Encode operational workflows and enterprise criteria into rule packs so agents can expose gaps and preserve verifiable evidence.

    SOPs as assets
    04

    Formal Outcome Ledger

    Write reviewed outcomes back to formal ledgers and build a traceable, reusable operating-data loop that improves over time.

    Reviewable · traceable

    Four high-value entry points for enterprise AI

    Frequent and essential01

    High-Tech Enterprise Certification

    Organize R&D projects, commercialization evidence, personnel systems, intellectual property and financial criteria into a material gap list and evidence chain.

    Qualification development02

    Specialized & Innovative SME

    Map core business, innovation capability and operating-quality data against evaluation criteria and maintain a long-term development ledger.

    Asset portfolio03

    Intellectual Property

    Connect patent discovery, technical-feature analysis and filing roadmaps to the R&D process, turning IP into a reusable operating asset.

    Policy opportunities04

    Project and Policy Matching

    Compare enterprise profiles with national, provincial, municipal and county policies, highlighting potentially relevant windows and eligibility conditions.

    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.

    Enterprise Trusted AI Applications Lab

    Co-developed with Tangshan University through industry-academia collaboration, the lab supports ShuPai's methodology, technical standards and demonstration environment for trusted enterprise AI.

    Industry-academia collaborationTrusted AI methodologyPrivate deploymentDemonstration lab

    See how AI turns enterprise files into reviewable outcomes

    Share your operational need and we will outline a practical approach to enterprise data assets and trusted business-agent deployment.

    Process Demo

    Customers in China can scan the WeChat QR code to contact us directly.

    WeChat contact QR code

    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.