Use Case Intelligence RSS Search use cases Search
AWS Financial Services Singapore

Crypto.com

Marketing Personalization

To stay competitive in the evolving web3 landscape, the company sought to leverage generative AI to enhance its platform and improve internal productivity and customer experience. However, the company faced several challenges: manual setup of development environments on Amazon EC2 slowed deployment, its infrastructure wasn’t optimized for large AI models, and content moderation was needed to safeguard brand quality.

Read the original story Explore all use cases
40% minutes to generate 30 high-quality video frames
Crypto.com is a crypto exchange and comprehensive trading platform serving 100 million users in 90 countries.
This speed improvement allows the development team to react to changes more efficiently, reducing new features launch time by approximately 50 percent.
decrease in deployment and scale out time 40%

Reported impact

Business outcome

This speed improvement allows the development team to react to changes more efficiently, reducing new features launch time by approximately 50 percent. In addition to faster content generation, implemented a more efficient reusable tagging system for video assets, allowing for faster search and filtering of video libraries, which has greatly improved the team’s workflow. Key reported metrics include reduction in large ML model loading times 50%; decrease in deployment and scale out time 40%; 40% minutes to generate 30 high-quality video frames.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Crypto.com Builds AI Engine to Accelerate Video and Image Production by Using Generative AI on AWS Crypto.com is a crypto exchange and comprehensive trading platform serving 100 million users in 90 countries. The company faced challenges productionizing its infrastructure for AI-driven visual content generation prototypes, slowing deployment and limiting scalability. By building its visual content generation engine using a fully managed infrastructure for generative AI on Amazon Web Services (AWS), Crypto.com automated workflows, reduced large ML model loading times by 50 percent, and accelerated video and image production, cutting the time to launch new features by 50 percent.

Data flow

Source data enters the Crypto.com workflow through its application services, stored or indexed in Amazon ECR, processed with Amazon Bedrock, Amazon SageMaker notebooks, Amazon SageMaker HyperPod, and delivered through Amazon SageMaker Asynchronous Inference.

Solution architecture

8 components · 3 layers
  1. Compute
    • Amazon Bedrock Hosts the foundation models and inference APIs that power the generative AI features.
    • Amazon SageMaker notebooks Provides notebook-based experimentation for model development and tuning.
    • Amazon SageMaker HyperPod Provides clustered infrastructure for large-scale foundation model training.
    • Amazon EC2 G5 Instances Provides accelerated GPU compute for AI inference or rendering workloads.
    • Amazon SageMaker Supports model training, fine-tuning, evaluation, or managed inference for the customer workload.
    • Hugging Face Provides access to pretrained models that the customer adapts for domain use cases.
  2. Storage
    • Amazon ECR Stores and distributes container images for application workloads.
  3. Serving
    • Amazon SageMaker Asynchronous Inference Handles long-running inference jobs outside synchronous request paths.

Architecture clues

  • By building its visual content generation engine using a fully managed infrastructure for generative AI on Amazon Web Services (AWS), Crypto.com automated workflows, reduced large ML model loading times by 50 percent, an
  • By using Amazon SageMaker and its suite of tools—such as Amazon SageMaker notebooks in JupyterLab for exploring data and test large image and video models, and Amazon SageMaker Asynchronous Inference endpoints for servin
  • In addition to faster content generation, implemented a more efficient reusable tagging system for video assets, allowing for faster search and filtering of video libraries, which has greatly improved the team’s workflow
  • Sunny Fok Head of AI Innovation Technology, Crypto.com Crypto.com is a crypto exchange and comprehensive trading platform serving 100 million users in 90 countries.
  • Throughout the video training journey, scientists from the AWS Generative AI Innovation Center (GenAIIC) have provided continuous support to the AI & Innovation team. integrated open-source models from platforms like Hug

Evidence from the source

40% minutes to generate 30 high-quality video frames
By using Amazon SageMaker and its suite of tools—such as Amazon SageMaker notebooks in JupyterLab for exploring data and test large image and video models, and Amazon SageMaker Asynchronous Inference endpoints for serving requests— automate
Crypto.com is a crypto exchange and comprehensive trading platform serving 100 million users in 90 countries.
This speed improvement allows the development team to react to changes more efficiently, reducing new features launch time by approximately 50 percent.
To stay competitive in the evolving web3 landscape, the company sought to leverage generative AI to enhance its platform and improve internal productivity and customer experience. However, the company faced several challenges: manual setup
Transparency Evidence & methodology 95/100 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 value95/100
  • 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 impact25/25
    5 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
    5 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • 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. AWS

    Crypto.com customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/case-study-crypto-2/
    Source type
    Vendor-published customer story
    Published
    Dec 9, 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

Date evidence

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
AWS Mar 21, 2024 Crédit Agricole Consumer Finance Financial Services France Crédit Agricole Consumer Finance wanted a reusable AI environment that business teams could use to deploy machine learning models for higher-value client targeting, beginning with a use case to optimize LifeTime Value… 27 candidates from a number of departments were able to get their hands on CEOS for the first time during a… Marketing Personalization AWS cloud · CEOS · Kubernetes AWS Oct 13, 2023 WaFd Bank Financial Services United States Supporting financial needs for a diverse customer base requires innovative solutions that put the customer experience at the center. WaFd realized that regional banks, though excellent at building customer… 15 million Customer Service AutomationMarketing Personalization AWS WAF · Amazon Comprehend · Amazon Lex Databricks Jul 24, 2026 Highstreet Insurance Partners Financial Services United States To meet that challenge, Highstreet created Ora, a proprietary, unified digital intelligence experience on Databricks that brings together trusted data, analytics, intelligent automation, and Al in a single environment. Imagine the daunting task of integrating billions of disparate data points, from over 200 local agency… Data Lake / Data Warehouse ModernizationMarketing Personalization Delta Lake · Lakeflow Jobs · Unity Catalog