Built an automated document ingestion pipeline that monitors uploaded files, extracts structured content using LlamaParse, generates embeddings, and indexes them into a vector database for Retrieval-Augmented Generation (RAG) applications.
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Designed automated workflows connecting APIs, AI models, and business systems to reduce repetitive work and improve operational efficiency
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Developed a lightweight AI chat widget that can be embedded into websites. The widget connects to a custom AI backend and provides intelligent customer support based on business documentation.
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Built a Retrieval-Augmented Generation (RAG) chatbot that answers strictly from uploaded documents using semantic search and vector embeddings. Designed to minimize hallucinations and provide accurate, context-aware responses.