Founder

PeachBot was founded on a simple belief: artificial intelligence should be grounded in real-world challenges, guided by safety, rigor, and deep domain understanding.

My professional background spans biomedical systems, applied AI, edge computing, and IoT-driven platforms. Over time, I observed a recurring gap—many promising AI concepts struggle when transitioning from controlled labs into real-world environments.

PeachBot exists to address this gap. Systems are designed to be researched and validated close to data sources—on devices, at the edge, and within environments where latency, privacy, reliability, and safety matter most.

My contributions are limited to research direction, architectural design, and long-term technical vision, and exclude operational, commercial, or managerial responsibilities.

---

Areas of Expertise

• On-device and edge AI architectures
• Telemedicine and digital healthcare systems
• AI-assisted diagnostics and biosignal analysis
• Precision agriculture and environmental intelligence
• Ethical and privacy-first AI design

Innovation & Intellectual Property

PeachBot platforms incorporate proprietary system architectures focused on modular AI pipelines, secure data handling, and privacy-preserving decision-support systems.

Published Patent Application (India): “Plug-and-Play On-Device Clinical Intelligence System with Privacy-Preserving Local Processing and Tiered Safety Assistance” — Indian Patent Application No. 202541127477, published in the Official Patent Gazette (23 Jan 2026).

Current Focus

I am currently pursuing formal business education in Singapore to strengthen my understanding of management, strategy, and organizational leadership, while remaining a shareholder and non-executive advisor to PeachBot.

The technical architectures and research concepts described here represent intellectual work developed prior to 2026 or within non-commercial research contexts.

---

Research Contributions & Technical Work

• Edge AI Architectures: SBC-based, low-latency, privacy-preserving AI systems
• Human-in-the-Loop AI: Hybrid intelligence with expert oversight
• Digital Health Systems: Telemedicine and clinical workflow architectures
• Computational Biology: AI-assisted biological data analysis pipelines
• Responsible AI: Biosafety, governance, and ethical system design

Research Outputs

This page serves as a research and technical portfolio for academic, incubator, and innovation-ecosystem discussions.

• Dedicated Edge-AI SBC Systems for Ecological Monitoring (Working preprint, 2025–2026)
• Edge-Based GNN Execution for Protein Interaction Networks (Working preprint, 2025–2026)