AI & Machine Learning
Generative AILLM ApplicationsRAG ArchitectureAutonomous AgentsLangChainPrompt EngineeringVector EmbeddingsSemantic SearchComputer Vision (OpenCV)Whisper & Multimodal AI
Systems Architecture & Philosophy
Production AI applications require more than static API wrappers. They demand robust schema normalization, low-latency client state, multi-model fallback redundancy, and verified execution proof.
I build end-to-end software systems that combine mathematical AI foundations with high-velocity product execution.
My work spans multimodal document intelligence engines, real-time computer vision utilities, offline-first client CRMs, and autonomous agent workflows. Every project is shipped with live demo links, source code, and verified performance benchmarks.
Strict Pydantic JSON schema validators and self-correction loops to eliminate hallucinations in production.
Encrypted client-side persistence and Zustand reactive state for instant 0ms user interactions.
Multithreaded Python & OpenCV processing delivering 60 FPS real-time webcam frame analytics.
Simulate real-time multi-agent reasoning, hybrid vector retrieval, and structured JSON output.
"Extract normalized invoice line items, tax breakdown, and vendor IBAN from complex irregular PDF."
Sample document coordinates and construct layout bounding boxes
BM25 keyword search + ChromaDB dense embeddings retrieval
Validate against Pydantic schema with automated self-correction
Emit sanitized JSON with 99.2% extraction confidence
{
"vendor": "Acme Logistics Inc.",
"invoice_no": "INV-2026-894",
"total_amount": "$14,850.00",
"tax": "$1,240.00",
"validation_status": "VERIFIED_VALID",
"confidence": 0.994
}Architectural Arsenal
Technologies, frameworks, mathematical foundations, and AI toolchains utilized to engineer scalable intelligent applications.
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