Use Case Intelligence RSS Search use cases Search
Baidu AI Cloud Consumer Goods China Translated from Chinese

Xiaoxun

Xiaoxun needed a more economical and stable cloud foundation for fast-growing child-watch services while also adding AI features that could increase user stickiness and make the devices more useful.

Read the original story Explore all use cases
Cloud-resource cost was reduced 30%.
The AI platform's daily call volume increased by more than 30%.

Reported impact

Business outcome

Xiaoxun reduced cloud cost, improved service stability and used embedded AI features to make its watches more engaging and differentiated for families.

  • Cost Reduction
  • Customer Experience
  • Scalability

Solution

Xiaoxun and Baidu AI Cloud carried out a full cloud migration and service redesign for the company's child-watch business. At the infrastructure layer, Baidu mapped Xiaoxun's workloads onto a cloud architecture that could scale, stay stable and reduce operating cost at the same time. That environment supports the watch platform's communication, video and service functions while allowing resources to expand or shrink as demand changes. On top of infrastructure, Baidu supplied media-cloud capabilities for video encoding, distribution and playback so calls and video experiences became smoother and more reliable for end users. The final layer brought AI into the device experience: image recognition, scene recognition and speech recognition help children identify objects, ask daily questions, hear stories, play music and consume encyclopedia-style content from the watch itself. The result is a three-part architecture—core cloud migration, media-cloud delivery and embedded AI functions—that improves both platform economics and product differentiation.

Data flow

Watch-service traffic runs on the cloud platform and media stack, while user voice and visual inputs flow into the recognition services and return child-friendly answers, stories and learning content back to the wearable device.

Solution architecture

6 components · 3 layers
  1. Compute
    • Media Content Analysis Supports the media-cloud layer that improves video quality and service delivery.
    • Image Recognition Helps the watch identify objects and scenes for child learning experiences.
    • Scene Recognition Adds contextual understanding to the watch's educational and assistant features.
  2. Serving
    • Content Delivery Network (CDN) Improves video distribution performance for child-watch users.
    • Speech Recognition Lets children interact with the watch through spoken questions and requests.
  3. Governance
    • Media Content Review Adds governance and safety controls to media workflows in the child-watch service environment.

Architecture clues

  • Resources can scale elastically with device-service demand
  • The project combines full cloud migration, media-cloud optimization and embedded AI features
  • The watch uses speech, image and scene recognition as built-in user features

Evidence from the source

After migration, the cloud environment reduced resource cost by 30% while still meeting current and future business needs (original: 在减少30%云资源成本的条件下，完全能够满足小寻当前和未来业务的发展需要).
Baidu built a whole-cloud migration plan matched to Xiaoxun's business architecture and optimized both cost and reliability (original: 制定了整体的上云方案，并与业务架构精准匹配……在业务架构、运营成本方面帮助小寻进行了不同程度的优化).
The watches use image, scene and speech recognition to help children learn about the world, ask questions and consume content (original: 接入图像识别、场景识别、语音识别等技术能力后，小寻儿童手表能帮助小朋友认识学习各种场景事务，解答日常生活问题，讲故事、播放音乐、朗读百科全书等).
Transparency Evidence & methodology 100/100 reference value · 84% 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 confidence84%
  • 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 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

    Xiaoxun customer story

    First-party
    https://cloud.baidu.com/customer/case/xiaoxunshoubiao.html
    Source type
    Vendor-published customer story
    Published
    May 9, 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 9, 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.

Related

Similar use cases

See all like this
Baidu AI Cloud Jun 20, 2025 HONOR Consumer Goods Global HONOR needed to strengthen the intelligence of its YOYO assistant and device experiences so users could complete richer interaction, productivity, and content-creation tasks without forcing the handset maker to build… Enterprise Knowledge Search ERNIE 3.5-128K · Qianfan AppBuilder · Qianfan ModelBuilder Baidu AI Cloud Jun 20, 2025 OPPO Consumer Goods China OPPO needed to bring trustworthy real-time information, personalization, multimodal understanding and developer tooling into its AI-phone strategy while still protecting user data and keeping assistant experiences… Customer Service AutomationDocument Processing Baidu Search-Sourced Retrieval · ERNIE Large Model · Qianfan AppBuilder Baidu AI Cloud Jun 16, 2025 Shifeng Culture Development Co., Ltd. Consumer Goods China Shifeng Culture wanted to turn traditional children's toys into interactive AI products, but needed help with large-model integration, low-latency interaction, security and scalable product delivery. Customer Service Automation Cloud Security Services · Intelligent Interaction Platform · Qianfan AppBuilder