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AI-Powered Legal Document Translation Platform for Refugee Support
  1. case
  2. AI-Powered Legal Document Translation Platform for Refugee Support

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AI-Powered Legal Document Translation Platform for Refugee Support

miquido.com
Non-profit
Legal
Human Resources

Challenges in Refugee Legal Document Processing

Refugees face significant barriers due to linguistic and cultural complexities in legal documentation. Current manual translation processes are time-consuming, error-prone, and lack contextual understanding of legal terminology across diverse languages and cultural frameworks.

About the Client

NGO supporting refugees and migrants in Germany with employment verification and legal assistance

Key Project Goals

  • Develop AI-powered translation tool for rapid legal document processing
  • Ensure cultural context preservation in translations
  • Create accessible multilingual interface for refugees and volunteers
  • Implement scalable cloud-based solution for global accessibility

Core System Requirements

  • GPT-3.5 powered document translation engine
  • Cultural nuance adaptation module
  • Real-time PDF processing and conversion
  • Multilingual chatbot legal advisor
  • Document summary generation
  • Cross-platform mobile/web accessibility

Technology Stack

OpenAI API (GPT-3.5)
React.js for frontend
Node.js backend
AWS cloud infrastructure
PDF processing libraries

System Integrations

  • Document validation APIs
  • User authentication system
  • Cloud storage for document management
  • Analytics dashboard for usage tracking

Performance Criteria

  • Sub-5 second translation response time
  • 99.9% system availability
  • GDPR-compliant data security
  • Horizontal scalability for peak usage
  • WCAG 2.1 accessibility compliance

Expected Outcomes

Enables 80% faster legal document processing, reduces volunteer workload by 60%, and improves refugee access to critical services through accurate, context-aware translations. The solution will support 15+ languages and handle 10,000+ concurrent users during peak demand periods.

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