AWS Software / SaaS Global

Gan.AI

Marketing Personalization

The founders wanted to empower everyone, from singular real-estate agents to global enterprises, to easily create hyper-personalized messages for targeted, meaningful marketing. It was a good opportunity for them to build the top of the marketing funnel and collect data to further engage potential customers.” faced the challenge of significantly reducing latency in video creation.

3,000 million unique videos created in 3 hours
The campaign resulted in over 100 million impressions, 10 million views, and 2.5 times increase in brand awareness.
Using Studio, Agoda crafted more than 200 20-second ads where Ayushmann directly interacted with viewers using messages tailored to their dream vacation destinations.
With up to 3,000 concurrent end users each generating custom videos in less than a minute, the framework can produce a million unique videos in just 3 hours.

Reported impact

Business outcome

With up to 3,000 concurrent end users each generating custom videos in less than a minute, the framework can produce a million unique videos in just 3 hours. As a component of its real-time offering, also offers brands a live chatbot, complete with custom audio. Key reported metrics include seconds average time to create custom video 50; concurrent videos generated 3,000; 3,000 million unique videos created in 3 hours.

  • Revenue Growth
  • Scalability

Solution

Gan.AI Enables Real-Time Creation of Personalized Videos to Drive Brand Engagement Startup Gan.AI uses generative AI to create personalized videos with perfect audio, lip-sync, and custom visuals. The company has been using Amazon Web Services (AWS) since its launch in 2021, and recently implemented AWS machine learning fully managed services for real-time video generation. Using Amazon SageMaker with multi-model endpoints, and AWS Lambda , Gan.AI can now create thousands of videos simultaneously with ultra-low latency. The business has unlocked new use cases internally and has increased the stickiness of existing customers. Following discussions with its AWS account team, the company chose to inference its deep learning models on GPU instances. In particular, the use of multi-model endpoints would offer the speed required for large-scale projects in real time.

Data flow

Source data enters the Gan.AI workflow through its application services, processed with Amazon SageMaker, AWS Lambda, Amazon ECS.

Solution architecture

3 components · 1 layer
  1. Compute
    • Amazon SageMaker Supports model training, fine-tuning, evaluation, or managed inference for the customer workload.
    • AWS Lambda Runs event-driven compute for real-time processing without server management.
    • Amazon ECS Runs containerized workloads without self-managing orchestration control planes.

Architecture clues

  • Sahil Sharma, brand lead for India market at Agoda, says, “Agoda’s AI campaign took personalization at scale to the next level by using ’s speech generation, lip syncing, and image personalization technology. ’s technolo
  • Suvrat Bhooshan, chief executive officer at , says, “Amazon SageMaker helped us host multi-model endpoints without a high cold-start latency of 5−6 minutes.
  • This was especially useful for scaling in a business such as ours with a mix of deep learning and CPU-intensive workloads.” can now simultaneously inference and scale hundreds of custom generative AI models with ultra-lo
  • To learn more, visit aws.amazon.com/ai/generative-ai Amazon SageMaker helped us host multi-model endpoints without a high cold-start latency of 5−6 minutes.
  • Using Amazon SageMaker with multi-model endpoints, and AWS Lambda , Gan.AI can now create thousands of videos simultaneously with ultra-low latency.

Evidence from the source

3,000 million unique videos created in 3 hours
Following discussions with its AWS account team, the company chose to inference its deep learning models on GPU instances.
The campaign resulted in over 100 million impressions, 10 million views, and 2.5 times increase in brand awareness.
The founders wanted to empower everyone, from singular real-estate agents to global enterprises, to easily create hyper-personalized messages for targeted, meaningful marketing. It was a good opportunity for them to build the top of the mar
Using Studio, Agoda crafted more than 200 20-second ads where Ayushmann directly interacted with viewers using messages tailored to their dream vacation destinations.
With up to 3,000 concurrent end users each generating custom videos in less than a minute, the framework can produce a million unique videos in just 3 hours.
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
    8 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
    6 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

    Gan.AI customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/gan-ai/
    Source type
    Vendor-published customer story
    Published
    Jul 10, 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.