
Over two months, SSM contributed to the UBC-CIC/AI-Learning-Assistant repository by engineering a robust document ingestion pipeline that leverages Python, AWS Lambda, and Docker. SSM implemented OCR-based text extraction using Tesseract, enabling accurate processing of image-based and multilingual documents, with extracted content stored in S3. The work included refactoring the API Gateway stack to use PyMuPDF for direct text extraction and integrating security enhancements such as AWS WAF for SQL injection mitigation. SSM also improved frontend rendering of AI-generated markdown using React and TypeScript, and enhanced observability and deployment workflows with AWS X-Ray and ECR integration.

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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