Baidu AI Cloud Automotive China Translated from Chinese

Li Auto

Application Refactoring

Li Auto needed stable, high-efficiency cloud services to support fast-growing in-vehicle digital content, intelligent-cockpit interaction, and always-on vehicle services without compromising safety, user experience, or the pace of product innovation.

Reported impact

Business outcome

Li Auto strengthened the cloud-backed digital-service ecosystem around its intelligent electric vehicles and improved the interaction foundation for safer, richer in-cabin user experiences.

  • Customer Experience
  • Scalability
  • Speed to Market

Solution

Li Auto uses Baidu AI Cloud as part of the cloud base behind its intelligent electric-vehicle service ecosystem. The source highlights three core products: BCC for stable compute resources, Qianfan AppBuilder for application delivery, and Qianfan ModelBuilder for model-development capability. Together these services support the large and growing cloud-service demand created by content updates inside the cockpit and by interactive services delivered to drivers and passengers across many travel scenarios. Baidu positions the stack as the underlying platform that provides stable, efficient, and intelligent cloud services, while Li Auto applies it to improve both safe-driving support and intelligent-interaction experience.

Data flow

Vehicle-service requests and digital content updates are handled through Baidu's cloud platform, where compute and model services support application delivery back into Li Auto's intelligent cockpit and related service scenarios.

Solution architecture

3 components · 2 layers
  1. Compute
    • BCC Provides the stable cloud-compute foundation used for Li Auto's vehicle-facing digital services.
    • Qianfan ModelBuilder Adds model-development capability for intelligent interaction and service evolution in the cockpit ecosystem.
  2. Orchestration
    • Qianfan AppBuilder Supports application development and delivery for interactive in-car and travel-related cloud services.

Architecture clues

  • Qianfan application and model platforms are positioned alongside core compute rather than as isolated experiments
  • The case is framed around cloud support for large volumes of cockpit content and travel-scene interaction
  • The same stack supports service expansion across multiple Li Auto models

Evidence from the source

Baidu says growing in-car content upgrades and travel-scene interaction create significant cloud-service demand, which the platform addresses with stable, efficient, intelligent underlying services (original: 车载系统里大量的内容升级…都会产生大量的云服务需求…百度智能云充分发挥云计算技术及平台能力,为理想汽车提供稳定、高效、智能的底层云服务).
Li Auto relies on Baidu AI Cloud's stable and efficient cloud-service platform to optimize intelligent-cockpit interaction and vehicle-service capability (original: 理想汽车依托百度智能云稳定高效的云服务平台,优化智能座舱交互体验与车载服务能力).
The source explicitly lists BCC, Qianfan AppBuilder, and Qianfan ModelBuilder as the relevant products (original: 相关产品/解决方案 云服务器BCC 千帆应用开发平台 千帆模型开发平台).
Transparency Evidence & methodology 70/100 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 value70/100
  • 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 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

    Li Auto customer story

    First-party
    https://cloud.baidu.com/customer/case/lixiang.html
    Source type
    Vendor-published customer story
    Published
    Jun 20, 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 20, 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.