What challenges did Ateme face before AI implementation?

Generating one hour of subtitles in a given language could take up to 15 hours of manual work. The process was costly, fragmented, and difficult to scale across languages. Customers needed better accessibility and broader linguistic coverage.

What was the impact of the AI solution?

A few minutes are now enough to produce professional-quality subtitles. The cost to generate one hour of subtitles is less than a dollar. The solution improves access to new linguistic markets and supports regulatory accessibility requirements. The workflow is fully automated and scalable for broadcasters and streaming platforms.

Rank#2,123of 3,449

Source

GCPEvidence: Low40/100

Ateme automated multilingual subtitle generation using Vertex AI and Gemini

Use case typeAccessible learningUpdated Jun 13, 2026

Ateme is a French video technology company that automates large-scale multilingual subtitle generation for streaming and broadcast customers. The solution replaces a previously manual process that required transcription, translation, timecode spotting, and technical integration. Ateme integrated Google Cloud generative AI into its existing workflow so subtitles can be generated and delivered in minutes.

Organization: Ateme
Industry: Tech & Comms
Location: France
Published: January 2024

Reported outcomes

Strategic outcomes

Speed & agilityAutomated subtitle generation workflowMarket & geographic expansionAccess to new linguistic marketsRisk & complianceSupports accessibility compliance requirementsScale & capacityScalable multilingual subtitle production

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Ateme
Provider: GCP
Maturity: Unknown
Linked source: Google Cloud Customer Story

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1Media processing
2Content localization
3Accessibility automation

Generating one hour of subtitles in a given language could take up to 15 hours of manual work.
The process was costly, fragmented, and difficult to scale across languages.
Customers needed better accessibility and broader linguistic coverage.

Ateme added a processing step to its software platform that calls Vertex AI and Gemini models after transcoding.
The models perform audio transcription, timecode spotting, and subtitle generation.
Output is reformatted into SRT through an internally developed script and integrated into client workflows.
The application is containerized as microservices on Google Kubernetes Engine and uses Google Cloud Storage for media files.

Technologies

Vertex AI Gemini models Google Kubernetes Engine Google Cloud Storage

A few minutes are now enough to produce professional-quality subtitles.
The cost to generate one hour of subtitles is less than a dollar.
The solution improves access to new linguistic markets and supports regulatory accessibility requirements.
The workflow is fully automated and scalable for broadcasters and streaming platforms.

Architecture

Ateme's Titan File transcoding application runs as containerized microservices on Google Kubernetes Engine. After transcoding, the system calls Vertex AI and Gemini for transcription, timecode spotting, and subtitle generation. Media files are stored in Google Cloud Storage, and subtitle outputs are reformatted into SRT by an internally developed script before being integrated into the existing client workflow.

Sources & evidence1

Evidence: Low40/100Evidence strength