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AWS Media and Entertainment Global

Hisense TV System

Enterprise Knowledge Search · IT Operations Automation · Marketing Personalization

Furthermore, the system needed to launch in the European market first to support rapid regional expansion, increasing the urgency.

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1.5 months to globally launch AI-intelligent recommendation system
AWS’s technical expertise and industry knowledge have been invaluable, and we are confident in the project's success and our ongoing collaboration." In just 1.5 months, Hisense TV's intelligent recommendation system moved from inception to testing and launch.
As the company expanded globally with 20 million registered users worldwide at the end of 2023, addressing the diverse needs of overseas users became crucial.
Enhanced Accuracy Improved TV and movie recommendations, boosting user experience

Reported impact

Business outcome

The intelligent recommendation system offers key advantages, including cost-effectiveness, scalability, and fault tolerance. The system builds a user profile library that caters to a wide range of tastes. Powered by robust data processing capabilities, it expands the existing user profile library in a secure and compliant way.

  • Cost Reduction
  • Customer Experience
  • Scalability
  • Speed to Market

Solution

Hisense TV System , founded in March 2019 by Hisense Group and other investors, focuses on developing smart TV operating systems and content platforms. As the company expanded globally with 20 million registered users worldwide at the end of 2023, addressing the diverse needs of overseas users became crucial. Together, they built an end-to-end intelligent recommendation system for overseas markets using AWS services such as Amazon SageMaker AWS Step Functions Amazon Elastic File System (Amazon EFS) Amazon Elastic Kubernetes Service Amazon S3 , and Amazon ElastiCache Amazon SageMaker handled AI model training and deployment, while AWS Step Functions developed data processing workflows. AWS experts were involved throughout the solution design and system-building process. Powered by robust data processing capabilities, it expands the existing user profile library in a secure and compliant way. Its microservice-based architecture enables

Data flow

workflow control is handled by AWS Step Functions; processing runs on Amazon SageMaker, Amazon EKS; results and state are stored in Amazon S3, Amazon ElastiCache for the knowledge search workload.

Solution architecture

5 components · 3 layers
  1. Compute
    • Amazon SageMaker Runs core compute or model execution for the knowledge search workload.
    • Amazon EKS Runs core compute or model execution for the knowledge search workload.
  2. Storage
    • Amazon S3 Stores application, analytics, or workflow state for the knowledge search solution.
    • Amazon ElastiCache Stores application, analytics, or workflow state for the knowledge search solution.
  3. Orchestration
    • AWS Step Functions Coordinates the knowledge search workflow and automation steps across services.

Architecture clues

  • Together, they built an end-to-end intelligent recommendation system for overseas markets using AWS services such as Amazon SageMaker AWS Step Functions Amazon Elastic File System (Amazon EFS) Amazon Elastic Kubernetes Service Amazon S3 , and Amazon ElastiCache Amazon SageMaker handled AI model training and deployment, while AWS Step Functions developed data processing workflows.

Evidence from the source

AWS experts were involved throughout the solution design and system-building process.
Facing the challenges of a tight timeline and a heavy workload and addressing Hisense TV’s team’s lack of AI engineering experience, AWS mobilized internal co-creation product experts from its Rapid Prototyping team to collaborate closely with Hisense’s technical team.
Furthermore, the system needed to launch in the European market first to support rapid regional expansion, increasing the urgency.
Other services provided storage, compute power, and caching capabilities.
The system implemented features like user profiling, content retrieval, and ranking and filtering, providing users with personalized video recommendations.
Together, they built an end-to-end intelligent recommendation system for overseas markets using AWS services such as Amazon SageMaker AWS Step Functions Amazon Elastic File System (Amazon EFS) Amazon Elastic Kubernetes Service Amazon S3 , and Amazon ElastiCache Amazon SageMaker handled AI model training and deployment, while AWS Step Functions developed data processing workflows.
Transparency Evidence & methodology 85/100 reference value · 90% confidence · 2 sources

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 value85/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence90%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency9/10
    2 source references 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. AWS

    Hisense TV System customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/hisense/
    Source type
    Vendor-published customer story
    Published
    Sep 26, 2024
    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
  2. Financial Times

    Hisense Visual Technology Co., Ltd., 600060:SHH profile - FT.com

    Independent secondary
    https://markets.ft.com/data/equities/tearsheet/profile?s=600060:SHH

    The profile lists 'Employees' as '15.04k'.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

Not separately sourced Industry, Region. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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