Transforming data into intelligence through IoT connectivity, AI-driven automation, and machine learning solutions that give your business a competitive edge.
End-to-end intelligent systems connecting physical devices to cloud platforms with AI-powered analysis and automated decision-making.
I design and develop IoT ecosystems, integrate machine learning models into real-world applications, and build AI-powered automation pipelines. Whether you need a network of smart sensors reporting to a dashboard, a predictive maintenance system, or a computer vision solution for quality control, I deliver systems that work reliably in production environments.
"Making machines smarter so your business can operate faster and more efficiently."
82+
Projects
Delivered
45+
Happy
Clients
13+
Years of
Experience
18+
Countries
Served
Skills
My Expertise
Internet of Things (IoT)
Building connected device ecosystems that collect, transmit, and act on real-world data:
- IoT Architecture Design – End-to-end system design from edge devices through gateways to cloud backends
- MQTT & CoAP Protocols – Lightweight messaging for reliable device-to-cloud communication
- Real-Time Dashboards – Web-based monitoring interfaces showing live sensor data, alerts, and historical trends
- Edge Computing – On-device data processing with Raspberry Pi and ESP32 to reduce latency and bandwidth
Artificial Intelligence & Machine Learning
Applying AI and ML to automate decisions, detect patterns, and extract value from your data:
- Predictive Analytics – ML models for forecasting demand, detecting anomalies, and predicting failures
- Computer Vision (OpenCV, TensorFlow) – Object detection, image classification, and visual quality inspection
- Natural Language Processing – Text classification, sentiment analysis, and chatbot integration
- Model Deployment – Packaging and deploying ML models as APIs or embedded on edge devices
Why Choose Me?
What makes my IoT/AI/ML approach deliver real results:
- Hardware-to-Cloud Expertise – I understand the full stack from microcontroller firmware to cloud APIs, which means no integration gaps between teams.
- Practical AI Implementation – I focus on AI solutions that work in production, not just in notebooks — optimized for your hardware constraints and data realities.
- Domain-Agnostic Solutions – Experience across agriculture, manufacturing, smart home, healthcare monitoring, and industrial automation domains.
How I Work
Work Process
Problem Definition & Data Assessment
Clearly define the problem to be solved by IoT or AI. Assess available data sources, sensor requirements, and connectivity options. Identify whether the project requires edge processing, cloud AI, or a hybrid approach.
System Design & Proof of Concept
Design the complete system architecture including hardware selection, communication protocols, data pipeline, and AI/ML model approach. Build a proof of concept to validate the core technical assumptions before full development.
Development & Model Training
Develop firmware, data pipelines, and cloud services in parallel. Collect training data, train and evaluate ML models iteratively, and integrate them into the production system. Perform end-to-end integration testing.
Deployment & Continuous Improvement
Deploy the system to the field environment. Monitor model performance and system health. Set up data pipelines to continuously improve model accuracy over time. Provide support for scaling the deployment as your needs grow.
Topics
Common Questions
What is an IoT system and how can it benefit my business?
An IoT system connects physical devices (sensors, actuators, machines) to the internet, enabling remote monitoring, automated alerts, and data-driven decisions. Benefits include reduced manual inspections, faster incident response, predictive maintenance, and real-time operational visibility.
Do I need a large dataset to benefit from machine learning?
Not always. Traditional ML algorithms can work with relatively small datasets. For deep learning or computer vision tasks, more data is required. I assess your existing data and recommend appropriate techniques — including transfer learning when labelled data is limited.
What communication protocols do IoT devices use?
IoT devices commonly use MQTT for lightweight publish-subscribe messaging, CoAP for constrained environments, HTTP/HTTPS for cloud APIs, and Modbus or CAN bus for industrial sensors. I choose protocols based on your bandwidth, power, and reliability requirements.
Can AI/ML be deployed on edge devices?
Yes. I use TensorFlow Lite, ONNX, and optimized model techniques to deploy ML models on edge devices like Raspberry Pi and ESP32. Edge AI reduces latency, lowers cloud costs, and enables offline inference for time-critical applications.
What industries do your IoT/AI solutions serve?
I have built solutions for agriculture (soil and climate monitoring), manufacturing (machine health and quality control), smart buildings (energy and access management), healthcare (patient monitoring), and logistics (asset tracking and fleet management).
How is IoT data stored and visualized?
IoT data is typically ingested through MQTT brokers or APIs, stored in time-series or relational databases, and displayed via real-time web dashboards. I build custom dashboards showing live sensor feeds, historical charts, alerts, and downloadable reports tailored to your operations.
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