ABOUT THE COMPANY
Metafraze is a fast-growing enterprise in the education industry, delivering digital learning and multilingual content to students and institutions across multiple regions. Their platform depends on consistent and accurate translation workflows for training material and learner communication.
Prior to engaging RSNA Cloud Connect, the customer operated fully outside of AWS with manual and fragmented translation systems that relied heavily on human translators. These legacy workflows made it difficult to scale and ensure quality across multiple languages. The organization wanted to leverage Generative AI (Gen AI) to automate multilingual translation, improve accuracy, and enable context-preserving localization at scale.
THE CHALLENGE
The organization needed to modernize rapidly but had no AWS footprint, no AI infrastructure, and shifting technical requirements. Translation workflows were inconsistent, slow, and dependent on repetitive human review cycles.
They faced several key challenges:
- Inconsistent translation quality due to manual, non-contextual translation processes.
- Slow localization cycles, delaying new course launches by weeks.
- Limited visibility into translation accuracy and cost.
- No elasticity to scale content translation for new regional markets.
- Absence of automation for contextual adaptation, cultural nuances, and terminology preservation.
The business needed a production-ready Gen AI deployment that could automate translation, maintain accuracy across multiple domains, and integrate with their existing content management system — all within a rapid timeline.
THE RSNA CLOUD CONNECT APPROACH
RSNA Cloud Connect partnered with the customer using a high-touch Gen AI delivery model built on Amazon Bedrock and AWS-native AI services. The team co-designed a modern architecture that blended Generative AI, human-in-the-loop review, and cloud-native automation.
Key delivery principles included:
- Rapid prototyping and iterative prompt tuning for model accuracy.
- Continuous alignment between business context and AI outcomes.
- Accelerated deployment through Bedrock-managed LLMs (Claude 3 and Titan Text).
- Embedded responsible AI guardrails for safe, bias-free translation.
- Clear ownership and risk management through CI/CD pipelines on AWS.
This iterative approach enabled the customer to move from a Greenfield environment to full-scale Gen AI production in just 1.5 months, establishing measurable business impact immediately after go-live.
THE SOLUTION
RSNA Cloud Connect deployed a secure, scalable Gen AI-powered translation and localization system on AWS — now known internally as MetaFraze AI. The solution uses Amazon Bedrock to orchestrate large language models (LLMs) for real-time translation, summarization, and context-aware adaptation.
Key Solution Components
- Generative Translation Models: Amazon Bedrock foundation models (Claude 3 and Titan Text Express) generate context-preserving translations for education content across 20+ languages.
- Prompt Orchestration Engine: AWS Lambda and API Gateway dynamically select and optimize prompts per subject area and language complexity.
- Custom Translation Memory & Search Index: Built using Amazon Aurora PostgreSQL and OpenSearch to ensure consistency, terminology retention, and quick retrieval.
- Human-in-the-Loop Validation: Amazon SageMaker Ground Truth integrates for expert review and feedback loops.
- Secure Data and Access Management: Amazon Cognito and AWS KMS ensure compliance with regional privacy standards.
AWS Services Used
- Amazon Bedrock – LLM orchestration and managed Gen AI inference
- Amazon SageMaker – model evaluation and HITL integration
- Amazon Aurora (PostgreSQL) – translation memory database
- Amazon OpenSearch Service – semantic retrieval for language context
- Amazon S3 – storage and versioning of translated assets
- AWS Lambda / API Gateway – automation and microservice scaling
- Amazon CloudWatch / AWS Cost Explorer – performance and cost tracking
- By late August, the organization was:
- Fully live on AWS with production-grade Gen AI workflows
- Actively consuming Bedrock model APIs for translation and summarization
- Generating ARR from new regional language offerings
- Positioned for continued scale and AI-driven innovation
LOOKING AHEAD
With the new Gen AI foundation in place, the customer is positioned to expand into additional AI-driven capabilities:
- Generative voice localization using Amazon Polly and Bedrock multimodal models.
- Adaptive learning agents that generate quiz and exam content per curriculum.
- Automated summarization and grading workflows for e-learning assessments.
- Continuous fine-tuning via feedback loops to improve translation fidelity over time.
- The collaboration created a repeatable blueprint for how educational organizations can modernize translation, content generation, and global delivery using AWS Bedrock and responsible Gen AI practices.
- What began as a Greenfield cloud journey has evolved into a foundation for long-term scalability, cost efficiency, and Gen AI–driven innovation in the global education space.