
During two months on the UBC-CIC/AI-Learning-Assistant repository, SSM developed and enhanced a robust document ingestion pipeline, focusing on OCR-based text extraction using Python, Tesseract, and AWS Lambda. SSM implemented direct and fallback extraction paths to handle both standard and image-based documents, storing results in S3 for downstream processing. The work included integrating multilingual OCR support, refactoring API Gateway stacks with PyMuPDF, and improving deployment workflows via Docker and ECR. SSM also strengthened security by adding WAF protections and enhanced observability with AWS X-Ray, demonstrating depth in cloud development, serverless architecture, and document processing without introducing any regressions.
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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