Baidu AI Cloud Public Sector China Translated from Chinese

Nanwei Software Co., Ltd.

Enterprise Knowledge Search · Real-time Analytics

Nanwei needed a better way to help Fengning government users explore and analyze operational data because dashboards and cockpits could only display fixed views, and custom cross-team analysis cycles were too slow for daily command and decision support.

Reported impact

Business outcome

Nanwei gave Fengning a conversational government-analytics assistant that speeds up data analysis, supports city-governance decisions, and makes cross-system data more usable for public-sector teams.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Nanwei built an intelligent 'ask the data' system for Fengning government on top of Baidu AI Cloud's government large-model stack, including Qianfan AppBuilder, Qianfan ModelBuilder, and Baidu Jiuzhou government-native products. The system embeds a dynamic cockpit into the IOC environment so users can ask questions in natural language instead of depending on fixed BI screens. The underlying model layer understands government rules and logic across multiple systems, fuses those data sources, and generates conversational analysis outputs tailored to daily work, dispatching, and policy decision scenarios. Rather than running a one-off report flow, the platform turns government data into an interactive assistant that can return charts and analysis on demand. Architecturally, the deployment joins model-driven reasoning, cockpit visualization, and cross-system data fusion so public-sector analysts can move from static dashboards to dialogue-based insight generation.

Data flow

Government data from multiple systems is fused into the dynamic cockpit environment, where users ask questions in natural language and the model layer interprets policy context and query intent. The system then generates charts and analysis responses for decision support, dispatch, and daily management.

Solution architecture

3 components · 3 layers
  1. Compute
    • Qianfan ModelBuilder Provides the model-development layer for understanding government rules, logic, and data-query intent.
  2. Serving
    • Baidu Jiuzhou Government Native Products Provide the government-specific model and cockpit capabilities used in the IOC deployment.
  3. Orchestration
    • Qianfan AppBuilder Supports the conversational analysis workflows used by government users in the ask-the-data application.

Architecture clues

  • Cross-system data fusion is combined with model reasoning tailored to government rules and logic
  • The deployment centers on a dynamic government cockpit instead of fixed BI-only displays
  • The system is designed for decision analysis, command dispatch, and daily work queries

Evidence from the source

Existing dashboards and cockpits could only show basic data and could not summarize government data effectively in real time, while cross-team collaboration made analysis cycles long (original: 现有数据产品…仅能展示基础数据,不能实时有效的进行政务相关数据的汇总…业务部门提出需求后需跨团队协作,导致分析周期长).
Nanwei used Baidu AI Cloud's government large model to build an intelligent ask-the-data system for Fengning, enabling conversational analysis of government data and reshaping traditional BI usage (original: 南威软件基于百度智能云政务大模型打造智能问数系统,实现政务数据对话式分析,颠覆传统BI模式).
The ask-the-data solution can answer government-data questions conversationally, fuse multiple systems, and understand Fengning's government rules and logic (original: 依托大模型对话生成式数据分析能力…融合政府数据和多系统数据,理解智慧丰宁政务规则和逻辑).
The dynamic cockpit increased IOC insight and decision capability and was built as a customized solution jointly tailored to real user needs (original: 利用百度智能云政务大模型的动态驾驶舱功能,实现了数据驱动的智能化管理,大大提升了IOC(智能运营中心)的业务洞察和决策能力).
Transparency Evidence & methodology 71/100 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 value71/100
  • 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 scale10/10
    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

    Nanwei Software Co., Ltd. customer story

    First-party
    https://cloud.baidu.com/customer/case/nanwei.html
    Source type
    Vendor-published customer story
    Published
    Jun 19, 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 Jun 19, 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.