Case-study style examples focused on business problems, implementation approach, and production architecture across AI automation,
PDF extraction pipelines, SaaS RAG chatbot development, and healthcare routing optimization.
An AI-first pipeline that converts high-volume business PDFs into validated structured records for downstream workflows.
Business problem: Operations teams were manually reading invoices, forms, and reports, creating delays and inconsistent data quality.
Outcome: Significantly reduced manual document processing and made PDF to structured JSON output more dependable for finance and operations teams.
PythonFastAPILLMsOCRPostgreSQLDocker
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A retrieval-augmented SaaS knowledge assistant that answers customer and internal support queries using trusted product content.
Business problem: Support teams spent time repeatedly answering the same product and onboarding questions across channels.
Outcome: Improved support response speed and consistency while helping users self-serve answers from the SaaS knowledge base.
TypeScriptNode.jsLLMsVector DatabaseREST APIs
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A routing optimization system for healthcare operations that combines demand clustering with constraint-aware path planning.
Business problem: Manual route planning could not efficiently handle changing demand, service windows, and resource constraints.
Outcome: Enabled more efficient route planning and better decision support for healthcare logistics teams.
PythonOR-ToolsGeo APIsPandasLLM-assisted analytics
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