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Baidu AI Cloud Public Sector China Translated from Chinese

All-China Federation of Trade Unions

Customer Service Automation · Enterprise Knowledge Search

The All-China Federation of Trade Unions needed a unified AI foundation that could support legal aid, rights protection, and other worker services at national scale instead of leaving union service digitization fragmented across departments and regions.

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Access to effective information improved from weekly to hourly.
Delivery cycle fell from 3-6 months to under 1 month.
Legal-service efficiency improved 10x.
System build time was reduced 80%.

Reported impact

Business outcome

The federation created a nationally governed AI foundation for union services, improved the speed and precision of legal-support scenarios, and reduced the time needed to deliver new AI service systems.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

The All-China Federation of Trade Unions built an AI support system with Baidu AI Cloud around a '1+4+N' architecture. The '1' is a union industry model designed to become a common intelligence base for union work. The '4' platform layers are a union large-model compute-scheduling platform, service-management platform, knowledge-operations platform, and AI-native application-support platform. Together those layers let the federation run model capabilities horizontally across departments and vertically across provinces with common standards and governance. On top of that foundation, 'N' applications can be added for labor legal aid, 12351 rights-protection services, worker innovation assistance, life services, health, education and training, and cultural scenarios.

Data flow

Union knowledge, service content, and worker-service requests flow into the shared industry model and associated knowledge-operations platform. The system then serves applications such as legal aid and rights protection, while standardized model support is distributed across departments and provincial organizations.

Solution architecture

3 components · 3 layers
  1. Compute
    • Qianfan ModelBuilder Supports development of the shared union industry model and the model-driven service stack.
  2. Serving
    • Union Industry Model Acts as the common intelligence layer used across national and regional union service scenarios.
  3. Orchestration
    • Qianfan AppBuilder Supports creation of AI-native worker-service applications on top of the shared model foundation.

Architecture clues

  • The deployment is designed for both horizontal use across departments and vertical rollout across provinces
  • The first example scenario includes legal answers, compensation calculation, and personalized case assessment for workers
  • The platform uses a '1+4+N' architecture with separate compute, service-management, knowledge-operations, and AI-native app-support layers

Evidence from the source

In the legal-aid scenario, the system improved effective information support from weekly to hourly and shortened overall system delivery from 3-6 months to under 1 month (original: 得到有效信息支撑从周级到小时级…整套系统及应用的交付周期从3~6个月大幅缩短到1个月以内).
The architecture is described as one industry model, four key platforms, and N applications including legal aid and 12351 rights-protection services (original: 建设一套1+4+N的人工智能应用支撑体系…N包括但不限于智能职工法律援助、智能12351维权服务).
The federation launched an action plan to build a unified and efficient AI 'union industry model' foundation and use AI services to support hundreds of millions of workers (original: 统筹构建全面统一、先进高效的人工智能“工会行业模型”底座。利用AI+服务，满足亿万职工群众需求).
The foundation achieved national standardization and provided model support horizontally across departments and vertically across provinces and cities (original: 实现了全国统一标准、统一规范、统一要求，实现了基于工会行业模型横向各部门、纵向各省市的大模型支撑能力).
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Baidu AI Cloud

    All-China Federation of Trade Unions customer story

    First-party
    https://cloud.baidu.com/customer/case/gonghui.html
    Source type
    Vendor-published customer story
    Published
    Sep 24, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Sep 24, 2025 · Baidu AI Cloud · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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