End-to-end AI processing pipeline combining secure audio upload, real-time transcription, language detection, and AI-powered assessment using AWS Lambda, Transcribe, Comprehend, and Bedrock. Audio files flow from the Thrive Mobile Application through S3 pre-signed URLs, triggering Lambda functions that orchestrate Amazon Transcribe for speech-to-text, Amazon Comprehend for language detection, and Claude models via Bedrock for translation and assessment. Asynchronous notifications via SQS/SNS keep caseworkers informed while operational metadata and AI recommendations persist in Amazon RDS and S3. This architecture demonstrates serverless event-driven design with managed AI services, eliminating infrastructure overhead while scaling to handle variable workloads. Fork this diagram on Diagrams.so to customize Lambda triggers, adjust Bedrock model selection, or integrate additional AWS services like EventBridge or Step Functions. The pattern exemplifies best practices for multi-step AI workflows: decoupling processing stages, using S3 as a data lake, and leveraging managed services to reduce operational complexity.