Baidu AI Cloud Public Sector China Translated from Chinese

Beijing Shijingshan District (北京市石景山区)

Shijingshan District wanted to turn broad interest in AI into an operating model that local enterprises, developers, and talent programs could actually use, but it lacked a shared platform to combine tooling, training, ecosystem activity, and vertical-industry scenario incubation.

Baidu committed to holding 4 high-quality forums and exchange events in the district each year.
Two Baidu AI product-launch events had already been held in Shougang Park by early 2024.

Reported impact

Business outcome

Shijingshan gained a reusable district-level AI operating model that helps local firms access tools, scenario incubation, and training without building every enabling capability themselves.

  • Productivity Improvement
  • Revenue Growth
  • Speed to Market

Solution

Baidu AI Cloud and Shijingshan District set up the Beijing Qianfan Large Model Industry Innovation Base as a public-service engine for local AI adoption. Instead of centering on one municipal workflow, the base combines a technical platform, enterprise incubation, talent cultivation, and scenario-opening activities so local organizations can get both tools and guidance. Qianfan development and application capabilities form the platform core, while the surrounding operating model is organized as 'one base, three engines': a public AI service engine for model tools, an industry engine for enterprise collaboration and scenario incubation, and a talent engine for practical training. This structure lets the district connect local firms that already experiment with PaddlePaddle and other AI resources to a more complete path for workshops, joint product creation, and large-model pilots in manufacturing, education, healthcare, energy, transport, finance, culture, and city management.

Data flow

Local enterprise and developer needs are funneled into the innovation base, where Qianfan tools provide model-development and application-building capabilities. Training programs, scenario incubation, and public events then feed talent, prototypes, and pilot projects back into district industries such as manufacturing, healthcare, finance, transport, and city management.

Solution architecture

4 components · 3 layers
  1. Compute
    • Qianfan Model Development Platform Provides large-model development tools for enterprises and developers participating in the district's innovation base.
  2. Serving
    • Qianfan Application Development Platform Lets local users turn large-model capabilities into AI-native applications within the public-service base.
    • AI Talent Engine Delivers practical training programs so local talent can learn and adopt large-model tools.
  3. Orchestration
    • Large Model Industry Innovation Base Acts as the operational center for local enterprise incubation, scenario opening, and AI ecosystem events.

Architecture clues

  • Target verticals include manufacturing, education, healthcare, energy, transport, finance, culture, and city management.
  • The base is organized around a 'one base, three engines' operating model rather than one standalone software application.
  • The program explicitly combines enterprise incubation, public events, tooling, and talent programs.

Evidence from the source

Baidu committed to four quality forums or exchange events each year in the district (original: 百度将保障每年在石景山区举办4场优质论坛及交流活动).
Baidu describes the model as 'one base, three engines' (original: 通过"一基地三引擎"服务模式,营造人工智能创新创业氛围).
Shijingshan built the country's first Baidu Qianfan large-model industry innovation base (original: 全国首个百度智能云千帆大模型产业创新基地落地).
The base is meant to provide a technical platform, enterprise incubation, and talent cultivation (original: 包括搭建大模型技术平台、孵化运营平台场景,引进与孵化科技企业,以及引进与培育人才等).
Transparency Evidence & methodology 66/100 reference value · 87% 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 value66/100
  • 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 specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 specificity8/10
    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

    Beijing Shijingshan District (北京市石景山区) customer story

    First-party
    https://cloud.baidu.com/customer/case/model.html
    Source type
    Vendor-published customer story
    Published
    May 13, 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 May 13, 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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