Baidu AI Cloud Public Sector China Translated from Chinese

Tangshan High-tech Zone (唐山高新区)

Tangshan wanted a practical way to turn its heavy-industry base and regional policy advantages into an AI-led growth engine, but it needed shared infrastructure, talent development, data-labeling capacity, and reusable large-model tooling before local enterprises could adopt AI at scale.

More than 200 people now work in large-model data-labeling roles.
The ecosystem has formed more than 30 large-model products.
The platform supports more than 50 characteristic industry scenarios.

Reported impact

Business outcome

Tangshan created a regional AI enablement backbone that accelerates enterprise adoption, grows local data and model capacity, and helps traditional industries move toward large-model-based digital transformation.

  • Productivity Improvement
  • Revenue Growth
  • Speed to Market

Solution

Tangshan High-tech Zone worked with Baidu AI Cloud to build an industry-oriented large-model enablement system rather than a single point application. The program combines a Baidu AI industrial park, a joint venture company, a data-labeling base, and the Phoenix Zhigu large-model enablement center so local enterprises can access tooling, talent, and implementation support in one place. Qianfan's model-development and application-development capabilities act as the public service engine for local innovation, while the broader plan links data elements, pretrained large models, and heterogeneous compute into an industrial platform that serves steel, ports, robotics, public services, and other Tangshan-specific sectors. The operating model is as much about enablement as software: local firms receive technical guidance, scenario incubation, and access to shared infrastructure so they can move from interest in AI to actual large-model applications without assembling every capability on their own.

Data flow

Industry data and labeling resources are collected through the local enablement base, then fed into Qianfan development and application workflows. Enterprises and public-sector teams use the industrial park and Phoenix Zhigu center to build, test, and operationalize vertical large-model applications for sector-specific scenarios such as steel, ports, robotics, public services, and city operations.

Solution architecture

5 components · 4 layers
  1. Ingest
    • AI Data Labeling Base Supplies labeled data and workforce capacity needed to support local large-model adoption.
  2. Compute
    • Qianfan Model Development Platform Provides the large-model development environment used to incubate regional AI applications.
  3. Serving
    • Qianfan Application Development Platform Lets local organizations package and deploy AI-native applications on a shared public platform.
    • Phoenix Zhigu Large Model Enablement Center Supports scenario incubation and vertical-model enablement for Tangshan's target industries.
  4. Orchestration
    • Baidu Smart Cloud (Tangshan) AI Industrial Park Acts as the physical and organizational hub for enterprise incubation and AI-industry collaboration.

Architecture clues

  • A local joint venture was created to operate part of the regional AI industry buildout.
  • The program combines industrial-park operations, data labeling, model enablement, and talent development rather than focusing on one application.
  • The target sectors include steel, ports, robotics, public services, and city-management scenarios specific to Tangshan.

Evidence from the source

More than 200 people are already working in large-model data labeling, with more than 30 ecosystem products supporting over 50 scenarios (original: 大模型数据要素标注从业人员超过200人…形成钢铁、港口大模型生态产品30余款,满足超过50余个特色行业场景的需求).
Tangshan and Baidu are jointly building an AI industry system to support both industrial digitization and AI industrialization (original: 合力建设人工智能产业体系,合力推进唐山市人工智能数字产业化与产业数字化转型发展).
The initiative includes an AI industrial park, a joint venture, a data-labeling base, and the Phoenix Zhigu large-model enablement center (original: 落地一个人工智能产业园…成立一家人工智能合资企业…建立人工智能数据标注基地…建立人工智能"凤凰智谷"大模型产业赋能中心).
Transparency Evidence & methodology 66/100 reference value · 82% 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 confidence82%
  • 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 evidence15/20
    3 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

    Tangshan High-tech Zone (唐山高新区) customer story

    First-party
    https://cloud.baidu.com/customer/case/tangshan.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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