Two Pillars
Physical AI
Intelligence that senses, decides, and acts in the physical world — cognitive layers for industrial assets, stateful modeling, and real-time anomaly detection.
Edge AI
Intelligence that runs where the data is born — quantized TFLite models on microcontrollers, sub-100ms inference latency, and hybrid cloud-edge systems.
Digital Twins
Where the pillars meet: a live model of the physical system, fed by edge data and reasoned over in real time — state and behavior, not just metrics.
Pillar 1: Physical AI — Digital Twin Systems
Industrial Digital Twins run on a five-layer, event-driven stack that bridges low-latency field hardware with cloud-scale analytics. My hands-on production experience covers the foundation of that stack — the physical field layer through the data pipeline that feeds the twin.
1. Physical Field & Control Layer
PLC/SCADA systems, smart sensors (vibration, temperature, pressure), actuators, and embedded controllers (microcontrollers, FPGA).
2. Edge Processing & Gateway
Protocol normalization (OPC UA → MQTT), edge inference and aggregation, local store-and-forward caching, and device authentication (mTLS).
3. Data Pipeline & Storage Layer
High-throughput ingestion into time-series stores (InfluxDB/Timescale), spatial data (PostGIS), event streaming (Kafka/Pulsar), and object storage for CAD/BIM assets.
4. Twin Processing & Analytics Engine
Physics-based simulation and ML-driven anomaly detection, extended with LangGraph reasoning agents in my LLM-powered Digital Twin project.
5. Visualization & Operational UI
Operator-facing dashboards and natural-language interfaces that surface twin state, diagnostics, and recommendations in real time.
Featured Project: LLM-Powered Digital Twin
A real-time Digital Twin architecture that integrates IoT sensor data with rule-based system modeling and an LLM-powered reasoning agent.
- Live Digital Twin state modeling & Health evaluation
- Natural language interaction with physical systems
- Explainable diagnostics and recommendations
- RAG-powered reasoning & LangGraph-based orchestration
Pillar 2: Edge AI — Intelligence Where the Data Is Born
Cloud round-trips are too slow, too costly, and too fragile for physical systems that must react in milliseconds. I deploy quantized models directly onto MCU-class hardware, so detection and decisions happen on the device — online or off.
Industrial Edge AI Gateway
GRU-based inference on a dual-core microcontroller with TFLite Micro and FreeRTOS isolation — real-time anomaly detection for industrial safety with sub-100ms inference latency.
On-Device Inference
Quantized TFLite models for real-time sensor telemetry on MCU-class hardware — model size, latency, and power treated as design constraints, not afterthoughts.
Edge AI or Traditional Algorithms?
Rule-based logic still wins many edge problems. A practical comparison of when deterministic algorithms beat quantized models — and when they don’t — from real deployments.
The Cognitive Layer: LLM Systems
LangChain, LangGraph, and RAG are the reasoning layer over the two pillars — the part that explains what the physical system is doing and why. They support the edge and the twin; they do not replace physics or control logic.
LangChain & RAG
Context-aware reasoning over live system state, vector databases (FAISS, Chroma) for technical documentation, and grounded retrieval.
LangGraph
Stateful multi-node agent workflows, event-driven reasoning loops, and Digital Twin agents with persistent memory.
Digital Twin Agents
Agents that sit on top of the twin and answer the operator’s real questions — what is happening, why it is happening, and what should be done next.
Technical Stack
Edge & Embedded
- Hardware: Microcontrollers, embedded processors, FPGA
- OS: Embedded Linux, FreeRTOS, Bare Metal
- ML: TensorFlow, TFLite (Quantization), Scikit-learn
Physical Systems & IoT
- Modeling: Digital Twins, Edge AI Frameworks
- Protocols: MQTT, Modbus, BACnet, TCP/IP, SIP
Cognitive Layer — AI & LLM
- Frameworks: LangChain, LangGraph
- RAG: FAISS, Chroma, Vector Pipelines
Cloud & Platform
- Infrastructure: AWS (IoT Core, Lambda), Azure
- Backend: FastAPI, Django, Spring Boot
- Storage: Redis, InfluxDB, PostgreSQL, MongoDB
- Architecture: Microservices, Event-Driven
- Container: Docker, Kubernetes basics
Career Highlights
- [Edge AI] Physical AI Framework (Epic Safety): Architected an Edge AI framework on microcontrollers using quantized TFLite models for fire suppression, achieving sub-100ms inference latency for industrial safety.
- [IoT Platform] IoT Device Management (VTech): Engineered a global platform on AWS IoT Core, leading cross-functional teams to design high-performance device-side agents.
- [Scale] Mass Deployment (NTT): Delivered 200K+ Android tablets to NTT Japan; architected custom home launchers and SIP/RTP multimedia frameworks.
- [Embedded] Multimedia SDK Pioneers (Varovision): Developed MPEG-2 TS demuxers and motion detection algorithms for early mobile DTV and portable media players.
Architectural Philosophy
- LLMs as Cognitive Layers: Language models should reason, explain, and assist—not replace physics or control logic.
- State Over Data: Digital Twins must model behavior and evolution, not just metrics.
- Production First: Observability, tracing, and evaluation are mandatory for real-world AI systems.
- Human-Centric AI: Systems should communicate clearly with operators and engineers.