IoT systems, AI-connected.
Cutting-edge applications for modern hardware-connected systems — from automotive telemetry and smart-home orchestration to industrial condition monitoring and AI-driven energy management.
Hardware without intelligence is just machinery.
The value in connected devices is not the sensor — it's the insight extracted from the stream. IndiaNIC bridges hardware and intelligence so your device fleet becomes a live data asset, not a management burden. See how IoT fits into our broader engineering capabilities.
Reduction in energy costs clients achieve through our smart energy management solutions — measured, not projected.
Projects delivered across IoT, mobile, enterprise, and AI — including connected-systems work for Abbott, Vodafone, and Adidas.
Countries with active clients. IoT deployments span smart cities in the Middle East, factories in Europe, and healthcare networks in North America.
Connected across
every vertical.
IoT without domain knowledge is an expensive experiment. Our practice spans six industries — each with its own protocol preferences, compliance constraints, and success metrics. Pair with our industry expertise for vertically deep delivery.
- 01
Automotive IoT
Connected vehicle platforms, fleet telemetry, predictive maintenance triggers, and over-the-air update pipelines — wired from sensor to dashboard.
- 02
Retail & commerce
Smart shelving, foot-traffic analytics, automated reorder systems, and loss-prevention sensors that feed directly into inventory and ERP.
- 03
Smart home automation
Voice-controlled and app-driven home systems: lighting, HVAC, security, and appliance orchestration across iOS, Android, and voice assistants.
- 04
Industrial IoT
Factory floor instrumentation, condition monitoring, digital twin integration, and OT/IT convergence for manufacturing and utilities.
- 05
Smart healthcare
Remote patient monitoring, wearable data ingestion, clinical asset tracking, and real-time alert pipelines that comply with HL7 and HIPAA.
- 06
Smart energy management
Demand-response systems, sub-metering, renewable-source orchestration, and reporting layers — helping organisations cut energy costs by up to 30%.
From sensor to
production dashboard.
A connected system is only as strong as its weakest layer. Our IoT engagements cover device integration, cloud data pipelines, and end-user interfaces — one team owns the full vertical.
Hardware meets cloud.
Firmware interfacing, protocol bridging, and device provisioning across MQTT, CoAP, BLE, Zigbee, LoRaWAN, and cellular. We handle the edge runtime so your team works with clean, validated streams.
- Multi-protocol gateway integration
- OTA firmware update pipelines
- Edge computing and on-device inference
- Device fleet management and provisioning
Insight delivered where decisions happen.
Time-series ingestion, stream processing, and analytics pipelines feed mobile apps, web dashboards, and ERP integrations. Real-time alerting and AI inference run on the same data model.
- Time-series data pipelines and storage
- Real-time alerting and notification systems
- Mobile and web monitoring dashboards
- ERP, CRM, and analytics platform integration
Intelligence that runs
at the edge.
Generic AI integrations underperform when latency, bandwidth, and reliability constraints are real. We build models that operate on-device as well as in the cloud. Explore the full scope on our AI & ML engineering page.
Predictive maintenance
ML models trained on vibration, temperature, and current signatures flag failure risk before the component fails — turning reactive repairs into planned events.
Anomaly detection at the edge
On-device inference that flags operational deviations in milliseconds without a round-trip to the cloud, even in intermittent-connectivity environments.
Computer vision on hardware
Quality inspection, occupancy detection, and safety compliance verified by vision models running on embedded hardware — not just cloud endpoints.
Natural-language device control
LLM-powered interfaces that translate plain-English commands into device actions across heterogeneous hardware ecosystems.
Digital twin intelligence
Live sensor streams feed simulation models so you can test process changes, run load scenarios, and predict outcomes before touching physical infrastructure.
Automated energy optimisation
Reinforcement-learning agents that continuously tune HVAC, lighting, and production schedules against real-time energy prices and comfort constraints.
Connected-systems depth
since 1998.
Our IoT practice draws on patterns shipped with Life Technologies, Jackson Coker, McDonald's, Vodafone, Adidas, Oracle, Abbott, AstraZeneca, and BCG — faster to production, fewer wrong turns. See our delivery record in our portfolio.
27 years of connected-systems depth
We've shipped IoT work for McDonald's, Vodafone, Adidas, Abbott, AstraZeneca, BCG, and 3,000+ clients across 90+ countries — your device ecosystem is a variant on patterns we've already hardened.
Full-stack IoT ownership
Firmware interfacing, cloud data pipelines, and end-user mobile or web dashboards — one team owns the entire vertical, eliminating hand-off gaps between embedded and application layers.
AI built into the sensor layer
Our ML engineers co-design the data model from first principles — so models are trainable on live device data, not retrofitted onto a schema that wasn't built for AI.
Security and compliance by default
Device identity, end-to-end encryption, HIPAA/GDPR data residency, and OTA update signing architected in from day one, not bolted on before audit.
Protocol and platform fluency
MQTT, CoAP, AMQP, Zigbee, Z-Wave, BLE, LoRaWAN, AWS IoT Core, Azure IoT Hub, Google Cloud IoT — we pick the right protocol for your device constraints, not for our convenience.
Measured energy and cost outcomes
Every IoT engagement is wired to a measurable outcome — energy savings, downtime reduction, throughput gain. We baseline at kickoff and report against it at delivery.
Answers for
hardware teams.
01What types of IoT devices and protocols do you work with?
02Can you integrate IoT data with our existing ERP or analytics stack?
03How do you handle security for IoT deployments?
04What does a typical IoT project timeline look like?
05Can you help us add AI capabilities to an existing IoT system?
06Do you provide ongoing support after deployment?
Connect your hardware,
ship the intelligence.
Start with a device-fleet audit — protocol inventory, data-quality assessment, and a clear AI-readiness score. Most teams have a working proof of concept within four to six weeks.