Digital AI Archivist for Government Agency
Challenge: A federal regulatory agency struggled with fragmented data across multiple ticketing systems and siloed knowledge bases, making mandated reporting time-consuming and error-prone.
Solution: Implemented an intelligent document processing system using Retrieval-Augmented Generation (RAG) and Content-Aware Generation (CAG), backed by PostgreSQL with advanced embedding layers and vector databases. The solution automatically analyzes and synthesizes information across disparate systems.
Technologies: Python, LangChain, PostgreSQL, Pinecone VectorDB, OpenAI Embeddings, FastAPI
Results: 70% reduction in report generation time, 95% accuracy in data extraction, projected savings of $1,000,000 over 3 years through improved efficiency and reduced manual processing.
Mental Health Access Analysis Platform
Challenge: Healthcare administrators needed comprehensive insights into mental health service accessibility for equity-deserving groups across rural Nova Scotia, where traditional data collection methods were insufficient.
Solution: Developed an AI-powered analytical tool that processes demographic data, service utilization patterns, and geographical barriers to identify gaps in mental health care access for marginalized communities.
Technologies: Python, TensorFlow, Tableau, GeoPandas, Azure ML Studio, Power BI
Results: Identified 23 underserved communities, enabled targeted resource allocation improving access for 15,000+ residents, 40% increase in service utilization among equity-deserving groups within 6 months.
AI-Powered Recruitment Assistant
Challenge: A technology firm sought to streamline their recruitment process while showcasing candidates' technical capabilities and reducing time-to-hire for specialized AI and programming roles.
Solution: Created an innovative RAG-based conversational interface allowing recruiters to query candidate profiles using natural language. The system demonstrates candidates' AI expertise through the application itself, serving as both a screening tool and portfolio piece.
Technologies: LangChain, ChromaDB, GPT-4, React, TypeScript, Docker, Kubernetes
Results: 60% reduction in initial screening time, 85% recruiter satisfaction rate, 45% improvement in candidate-role matching accuracy, average time-to-hire reduced from 45 to 28 days.
