Baidu AI Cloud Media and Entertainment China Translated from Chinese

Tianmai Juyuan

Enterprise Knowledge Search

Tianmai Juyuan needed to organize and search a massive broadcast-video archive quickly enough to support timely publishing, short-video creation and intelligent content retrieval.

MCA analysis efficiency improved 3-5x over manual tagging.
New TV programs can be turned into short videos and published within 3 minutes.
Structured-tag counts increased about 10x.
The archive includes 500+ broadcast channels, more than 10 years of content, 20 million hours, 200 million clips and 6 PB of data.

Reported impact

Business outcome

Tianmai Juyuan gained a combined search-and-production platform that speeds asset discovery, improves tagging quality and makes it much easier to turn live broadcast content into short video quickly.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Tianmai Juyuan used Baidu AI Cloud to transform a giant broadcast archive into a searchable, production-ready media asset platform. The foundation is Baidu's MCA media-content-analysis service, which applies multimodal recognition across video, speech, text, public figures, objects and scenes and then outputs structured tags. Those tags power Yunsou, Tianmai's cloud media-asset search tool, and make it possible to search live and archived content precisely across domestic mainstream media. Baidu also supported a production path beyond search: the micro-editing workflow can ingest live TV streams, automatically clip them, transcode them and publish short videos within minutes. From an architectural standpoint, object storage retains the large video corpus, speech recognition and content analysis extract searchable structure, and the clipping layer turns tagged assets into publishable short-form content. That gives Tianmai a combined archive, search and production stack rather than just a passive content repository.

Data flow

Broadcast video and live streams are stored in BOS, analyzed by MCA and speech-recognition services to generate structured tags, then surfaced in Yunsou search and fed into the micro-editing workflow for clipping, transcoding and distribution.

Solution architecture

4 components · 3 layers
  1. Compute
    • Media Content Analysis (MCA) Extracts structured tags from video, audio and visual content for search and editing.
    • Real-Time Speech Recognition Converts spoken program content into searchable text within the media workflow.
  2. Storage
    • BOS Object Storage Stores the very large video archive used for search and clipping workflows.
  3. Serving
    • Micro-Editing Platform Ingests live streams, clips them automatically and publishes short-form video outputs.

Architecture clues

  • Live-stream ingestion and clipping are built into the production path
  • Multimodal tags include scenes, people, places, entities and keywords
  • The platform combines archive storage, search and short-video production on one stack

Evidence from the source

Compared with manual tagging, MCA improved analysis efficiency by 3-5 times and structured-tag volume by about 10 times (original: 对比人工视频标注,MCA的分析效率提升了3-5倍,结构化标签数量提升了10倍左右).
MCA analyzes video, speech, text, public figures, objects and scenes and outputs structured tag information (original: MCA能提供音视频内容分析能力。能从视频、语音、文字、公众人物、物体、场景等多个维度进行识别……输出精准的结构化标签信息).
Micro-editing can ingest, clip, transcode and publish new TV content into short videos within 3 minutes (original: 通过微剪能进行实时快速收录和剪辑,自动化拆条、转码,3分钟内就能将新播出的电视节目创作成短视频并发布).
Tianmai Juyuan stores more than 500 channels, 10 years of programs, 20 million hours, 200 million items and 6PB of content (original: 收录全球500多套国内外电视频道,存储了超过10年、2000万小时、2亿多条、6PB的全量节目库和精华片段库).
Transparency Evidence & methodology 100/100 reference value · 89% 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
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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 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

    Tianmai Juyuan customer story

    First-party
    https://cloud.baidu.com/customer/case/tianmaijuyuan.html
    Source type
    Vendor-published customer story
    Published
    May 15, 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 15, 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.