IoT Development with AI
Connected systems that sense, process, and act in real time
We build end to end IoT systems from sensor to dashboard. Hardware integration using MQTT, Zigbee, and LoRaWAN protocols. Edge inference on Raspberry Pi and NVIDIA Jetson. Cloud connectivity through AWS IoT Core and Azure IoT Hub. AI at the edge for predictive maintenance, quality inspection, and environmental monitoring. Every system ships with real time dashboards, alerting, and the ability to act on data the moment it arrives, not hours or days later.
What is included
Sensor Integration and Protocol Engineering
We select and integrate the right sensors and communication protocols for your environment. This includes temperature, humidity, vibration, pressure, proximity, and camera sensors connected via MQTT, Zigbee, LoRaWAN, BLE, or Modbus depending on range, power, and bandwidth requirements. For brownfield factories with existing PLCs, we connect via OPC UA without replacing your current hardware.
Data Pipeline and Storage Architecture
IoT generates high volume, high velocity data that traditional databases cannot handle efficiently. We design and implement time series storage (InfluxDB, TimescaleDB), data lake ingestion (S3, ADLS), stream processing (Kafka, Kinesis), and data retention policies that balance storage costs with historical analysis needs. Raw data is preserved for model training while aggregated data feeds dashboards and reports.
Edge Computing and Edge AI
We deploy AI models directly on edge devices (Raspberry Pi, NVIDIA Jetson, ESP32) so your system can make decisions at the source without waiting for a round trip to the cloud. Use cases include real time defect detection on production lines, anomaly detection on vibration sensors, and vehicle counting on camera feeds. Edge inference reduces latency to milliseconds and keeps working even when internet connectivity is intermittent.
Predictive Maintenance and AI Analytics
We train and deploy machine learning models on your sensor data to predict equipment failures before they happen. The system learns normal operating patterns and flags anomalies: unusual vibration signatures, temperature drift, pressure irregularities, or power consumption changes. Alerts are sent to your maintenance team with the predicted failure type, estimated time to failure, and recommended action. Reduces unplanned downtime by catching problems days or weeks before they cause outages.
Cloud IoT Platform Setup
We configure your cloud IoT backbone on AWS IoT Core, Azure IoT Hub, or Google Cloud IoT. This includes device provisioning and identity management, secure message routing, device shadow/twin for state management, rules engine for automated actions, and integration with your downstream systems (databases, analytics, ERP). The platform handles thousands to millions of device messages per second with built in security and scalability.
Real Time Dashboards and Alerting
We build operations dashboards (Grafana, custom React dashboards) that display live sensor readings, equipment health scores, production metrics, and environmental conditions. Alerting rules fire via email, SMS, Slack, or PagerDuty when readings breach configurable thresholds. Historical data views let your team analyse trends, compare shifts, and identify patterns across time ranges.
Technologies We Use
MQTT
Zigbee
LoRaWAN
BLE
Modbus
ESP32
Grafana
Python
Docker
TensorFlow
Who this is for
Manufacturing plants
You want to add predictive maintenance to production lines, monitor equipment health in real time, or automate quality inspection using computer vision on the factory floor.
Logistics and fleet operators
You need real time tracking and condition monitoring across your fleet: vehicle location, cold chain temperature, fuel consumption, driver behaviour, and cargo status.
Facility and building managers
You manage commercial buildings, warehouses, or campuses and need smart environmental monitoring (HVAC, energy, occupancy, air quality) with automated controls and compliance reporting.
Our Process
Discovery
Site assessment, sensor selection, and architecture design (week 1 to 2)
Prototype
Hardware integration and edge device programming (week 3 to 5)
Build
Cloud platform setup and data pipeline build (week 6 to 8)
Launch
AI model training and edge deployment (week 9 to 10)
Support
Dashboard build, alerting configuration, and go live (week 11 to 12)
Ready to build with generative AI?
Book a free scoping call and get a tailored proposal within 48 hours.
Do you supply the hardware (sensors, gateways, edge devices)?
We recommend and spec the hardware, but procurement is handled by your team or your preferred vendor. We program and configure all devices once they arrive. For proof of concept projects, we can provide development kits (Raspberry Pi, Jetson Nano) to get started quickly.
Can you connect to our existing factory equipment (PLCs, SCADA)?
Yes. For brownfield environments with existing industrial equipment, we connect via OPC UA, Modbus, or direct PLC integration without replacing your current hardware. The IoT layer sits alongside your existing systems.
How much data does an IoT system generate and what does storage cost?
It depends on the number of sensors and sampling frequency. A typical factory floor with 50 sensors sampling every 5 seconds generates roughly 2 to 5 GB per day. We design storage with tiered retention: hot storage for recent data (7 to 30 days), warm for historical analysis (1 to 2 years), and cold archive for compliance. Monthly cloud storage costs for a mid scale deployment typically run Rs.5,000 to Rs.20,000.