Grano

Customer Success Story

Grano

AI-powered image tagging and alt-text generation for Finland's leading digital asset management platform EMMi

Industry
Technology
Services
AI Solutions Cloud Architecture Event-Driven Architecture
Technologies
Amazon Bedrock AWS Lambda Nova Models Generative AI

The Challenge

EMMi DAM's existing tagging process relied on manual input and basic automation, leading to inconsistent tagging and lack of accessibility-compliant alt-text for digital assets across 250+ brands.

Our Solution

Cloud2 built an event-driven AI pipeline on AWS Bedrock with Nova models. When images are uploaded to EMMi, Lambda triggers Bedrock to generate semantic tags and descriptive alt-text automatically. No user intervention needed.

Results

250+

Brands using AI-enhanced EMMi platform

Zero manual

Fully automated image tagging and alt-text

Accessibility

Compliant alt-text for all digital assets

About Grano

Grano’s EMMi platform is Finland’s leading digital asset management (DAM) solution, trusted by over 250 brands and around 200 companies nationwide. EMMi simplifies digital asset management, centralizing all digital assets, facilitating seamless workflows, and providing powerful integrations and metadata handling capabilities.

The Challenge

EMMi’s existing tagging process relied primarily on manual input and basic automation, which presented challenges such as inconsistent tagging and inefficiency. There was also a need to automatically generate accurate alternative texts (alt-text) to ensure accessibility compliance.

Our Solution

Cloud2 chose AWS Bedrock and Nova models for their robust performance, reliability, and cost-effectiveness. Using an event-driven architecture on AWS:

  • Upon uploading images into EMMi, the event-driven process initiates automatically
  • AWS Lambda functions trigger AWS Bedrock’s Nova models to analyze image content
  • Semantic tags and descriptive alt-text are generated and written back into EMMi without user intervention

Read the full story | Visit EMMi.fi

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