
Contributed to the UBC-CIC/AI-Learning-Assistant by developing and enhancing document ingestion and processing features over a two-month period. Focused on implementing OCR-based text extraction using Tesseract and PyMuPDF, enabling the system to process both image-based and text-based documents and store extracted content in S3. Improved the deployment pipeline with Docker and AWS Lambda, integrated multilingual OCR support, and strengthened API security through AWS WAF. Enhanced the frontend with markdown rendering for AI responses using React and TypeScript. Prioritized observability by enabling AWS X-Ray and streamlined deployments with ECR, resulting in a more robust and scalable cloud-based application.
July 2025 monthly summary for UBC-CIC/AI-Learning-Assistant: Delivered OCR-enhanced data ingestion to improve extraction accuracy and coverage across varied document types. Implemented Tesseract-based text extraction with a direct extraction path and a robust fallback for low-text pages. Updated dependencies and Dockerfile to include Tesseract language data, enabling multilingual text extraction and streamlined deployment.
July 2025 monthly summary for UBC-CIC/AI-Learning-Assistant: Delivered OCR-enhanced data ingestion to improve extraction accuracy and coverage across varied document types. Implemented Tesseract-based text extraction with a direct extraction path and a robust fallback for low-text pages. Updated dependencies and Dockerfile to include Tesseract language data, enabling multilingual text extraction and streamlined deployment.
June 2025 monthly summary for UBC-CIC/AI-Learning-Assistant focusing on delivering core ingestion, UI, security, and observability improvements that drive business value and system reliability.
June 2025 monthly summary for UBC-CIC/AI-Learning-Assistant focusing on delivering core ingestion, UI, security, and observability improvements that drive business value and system reliability.

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