IoT Software Development for Manufacturing
Connect machines, edge gateways and plant systems into one working data flow
IoT software development is the engineering of the full stack that connects physical equipment to digital systems: firmware on devices, the protocols that move data, edge and cloud platforms that process it, and the dashboards and applications that make it useful. Zenkins is an Ahmedabad-based AI software development company for manufacturing. We build industrial IoT software that links machines, sensors and PLCs to your MES, ERP and analytics, and we add AI where it creates value.
- What it is: the software stack that connects machines and sensors to the systems and people that use their data.
- What we build: firmware, protocol and gateway software, edge runtimes, cloud IoT platforms, data pipelines, machine monitoring and OEE dashboards, and integration with MES and ERP.
- How we connect existing machines: through standard industrial interfaces such as OPC UA, MQTT and Modbus, often without modifying the equipment.
- How we reduce risk: a proof of concept on real equipment before platform development, and security defined in phase 1.
- Where AI fits: telemetry feeds predictive maintenance and anomaly models, with results returned to dashboards and maintenance workflows.
Custom Software • AI Engineering • Manufacturing Systems • Cloud & Data
What Is IoT Software Development?
An IoT system is not one piece of software. A firmware engineer working in C or C++ on a microcontroller, a cloud engineer building device management, a data engineer handling telemetry and a front-end developer creating an operator dashboard all contribute. When the layers are built by separate teams, the seams are where projects stall: data in the wrong format, a protocol that does not fit the network, a dashboard nobody trusts.
In manufacturing, there are extra constraints. Machines are decades old and cannot always be modified. Plant networks are segmented for good reason. Connections drop. Data must reach the MES and ERP, not just a dashboard. And the system must stay secure in an environment where an outage costs production.
Zenkins works across the whole stack. We support manufacturers who need an end-to-end system, and plants that already have machines and need the gateway, platform and application layers connected to existing OT equipment.
Machine monitoring and OEE
See what every machine is doing. Collect status, counts and downtime reasons from machines and show availability, performance and quality by line, shift and machine.
Retrofit connectivity
Connect machines you already own. Gateway software reads data from existing PLCs and equipment over OPC UA, Modbus or other interfaces, without changing the machine.
Condition monitoring and predictive maintenance
Act before failure. Edge or cloud models analyse vibration, temperature and current, with alerts and maintenance-order creation.
Connected products
Equipment that reports back. Remote diagnostics and usage data from installed machines for service teams and customers.
The IoT Software Stack: What Each Layer Does
Not every project needs every layer built from scratch. A retrofit may need gateway software, edge processing and a dashboard but no new firmware.
| Layer | What it does | Key technologies | What Zenkins builds |
|---|---|---|---|
| Device and firmware | Collects sensor data and controls actuators on constrained hardware | C and C++, FreeRTOS, Zephyr, ESP-IDF, MicroPython | Firmware, RTOS setup, signed OTA update systems |
| Connectivity | Moves data from device to gateway and cloud | MQTT, OPC UA, Modbus RTU and TCP, CoAP, AMQP, HTTPS, BLE, LoRaWAN, NB-IoT | Protocol selection, broker setup, gateway software |
| Edge | Filters, aggregates and acts locally | AWS IoT Greengrass, Azure IoT Edge, Docker on industrial gateways, TensorFlow Lite, ONNX Runtime | Edge runtimes, local rules, edge inference |
| Cloud platform | Device management and telemetry at scale | AWS IoT Core, Azure IoT Hub, custom MQTT brokers | Device registry, telemetry pipeline, digital twins |
| Data and analytics | Stores and analyses time-series data | InfluxDB, TimescaleDB, Kafka, Spark, Flink | Pipelines, stream processors, alerts. See Data Engineering |
| Application | Where people use the data | React, Next.js, Grafana, Flutter, ASP.NET Core, Node.js | Dashboards, mobile apps, APIs, ERP and MES integration |
IoT Communication Protocols
| OPC UA | MQTT | Modbus | CoAP | AMQP | |
|---|---|---|---|---|---|
| Typical role | Standard machine and PLC interface | Lightweight telemetry to broker and cloud | Legacy PLC and meter registers | Constrained, battery devices | Enterprise messaging |
| Model | Client-server with rich information model | Publish-subscribe | Master-slave register read | Request-response over UDP | Queue and exchange |
| Best for | Reading structured machine data | Streaming data over unreliable networks | Older equipment | Very low-power sensors | Integration into enterprise brokers |
OPC UA or Modbus at the machine, an edge gateway that normalises and filters, MQTT to the platform, and APIs to MES and ERP. We confirm this against your network, equipment age and security rules rather than assuming it.
Edge or cloud: where each decision should run
Most deployments are hybrid. We document the split during architecture, before platform development.
| Factor | Process at the edge | Process in the cloud |
|---|---|---|
| Latency | Very low-latency control and line-stop responses | Dashboards and analytics tolerate delay |
| Connectivity | Plant or remote site with unreliable links | Reliable connection available |
| Data volume | High raw volume, filter first | Aggregated data after filtering |
| Data residency | Data must stay on site | Cloud storage acceptable |
| Machine learning | Lightweight inference | Training and heavy analysis |
| Example | Anomaly detection on a CNC spindle | Plant-wide OEE trend, fleet analytics |
Our IoT Software Development Services
One team across firmware, edge, cloud, data and applications, connected to your MES and ERP.
Industrial IoT and Industry 4.0 software
Connect the plant to its data. Connect CNC machines, PLCs, SCADA, conveyors, robots and energy meters to monitoring and analytics, with protocol bridging, gateway software for equipment that cannot be modified and integration with MES and ERP.
Examples: Machine connectivity • Protocol bridging • MES and ERP integration
Machine monitoring, OEE and downtime software
Make performance visible. Real-time dashboards for availability, performance, quality and downtime reasons by line, shift and machine.
Examples: OEE dashboards • Downtime capture • Shift reports
Predictive maintenance applications
Turn condition data into action. Condition-monitoring pipelines with edge or cloud models, alerting and maintenance-order creation. Models are built with our AI/ML team.
Examples: Vibration monitoring • Alerting • Work-order creation
Edge computing and gateway software
Keep working when the network does not. Edge runtimes on industrial gateways with local brokers, rules engines, store-and-forward buffering for outages and remote update.
Examples: Local rules • Offline buffering • Remote updates
IoT cloud platform development
Manage devices and telemetry at scale. Device provisioning, registry, shadow or digital twin state, message routing and fleet management on AWS IoT Core, Azure IoT Hub or a custom broker.
Examples: Device registry • Telemetry ingestion • Digital twin state
Firmware and embedded software
Reliable code on constrained hardware. C and C++ and MicroPython firmware for microcontrollers and embedded boards, with RTOS configuration, drivers, secure boot, signed OTA updates and power management.
Examples: RTOS firmware • Secure boot • OTA updates
Real-time dashboards and operator applications
Interfaces built for the shop floor. Control-room and shop-floor interfaces designed for operators, with live data, alert consoles and mobile access.
Examples: Control-room dashboards • Alert consoles • Mobile apps
IoT security
Identity, encryption and segmentation by design. Device identity with X.509 certificates, TLS for all communication, credential rotation and revocation, IT and OT segmentation, and hardening aligned with OWASP IoT guidance. For industrial control environments we align with ISA/IEC 62443.
Examples: Certificate management • Network segmentation • Fleet monitoring
IoT architecture consulting
Decide before you build. Connectivity choice, platform selection, build-versus-buy and a phased roadmap before hardware or vendor decisions are final.
Examples: Architecture review • Platform selection • Phased roadmap
See data flowing from your own equipment first
We start with a proof of concept on real equipment sending data end to end to a basic dashboard, before platform development.
Our IoT Software Development Process
A working proof of concept on real equipment comes before platform development, and security is defined in phase 1, not added at the end.
Architecture
Equipment inventory, connectivity, edge versus cloud decisions and the security model.
Output: IoT architecture document.
Proof of concept
Real equipment sending data end to end to a basic dashboard.
Output: working PoC.
Firmware (if needed)
Embedded code, protocol stacks and OTA update capability.
Output: firmware on target hardware.
Platform and backend
Device management, telemetry pipeline and APIs.
Output: platform with live devices.
Edge layer
Gateway runtime, local rules and store-and-forward.
Output: edge processing working offline.
Applications
Web dashboards, mobile apps, and MES and ERP integration.
Output: user-facing software.
Security hardening
Certificate management, review and testing.
Output: security report.
Deployment and monitoring
Rollout, fleet monitoring and runbooks.
Output: production system.
Technology & Engineering
An IoT stack chosen for your equipment and network. We choose by protocols, scale, security needs and the cloud you already use.
Embedded
C, C++, FreeRTOS, Zephyr, ESP-IDF, MicroPython
Protocols
OPC UA, MQTT (Mosquitto, EMQX, HiveMQ), Modbus, CoAP, AMQP, BLE, LoRaWAN, NB-IoT
Cloud and edge
AWS IoT Core and Greengrass, Azure IoT Hub and IoT Edge, Docker, Kubernetes
Data
InfluxDB, TimescaleDB, Kafka, Flink, Spark
Backend
ASP.NET Core, Node.js, Python FastAPI, Java Spring Boot
Front end
React, Next.js, Grafana, Flutter
Edge ML
TensorFlow Lite, ONNX Runtime, Edge Impulse
DevOps
GitHub Actions, Terraform, Docker, Kubernetes
Industrial IoT across manufacturing industries
Equipment, protocols, environments and compliance differ by sector.
Automotive & Mobility
Line and station visibility. Line and station monitoring, tool and torque data capture and EV test-bench data.
Examples: Torque data capture • Line monitoring • EV test benches
Semiconductor & Electronics
Equipment and environment data. Equipment data collection, cleanroom environmental monitoring and SMT line traceability.
Examples: Equipment data • Cleanroom monitoring • SMT traceability
Industrial Equipment & Machinery
Connected equipment. Connected-equipment platforms, remote diagnostics and usage-based service.
Examples: Remote diagnostics • Usage data • Service platforms
Electrical & Energy Equipment
Test benches and field assets. Test-bench data capture and monitoring of installed assets.
Examples: Test-bench data • Asset monitoring
Chemicals & Process Manufacturing
Process variables and alarms. Process variable monitoring, tank and flow data and alarm analytics.
Examples: Process monitoring • Tank and flow data • Alarm analytics
Pharmaceuticals & Life Sciences
Monitoring with audit trails. Cold-chain and cleanroom monitoring with audit trails and equipment qualification data.
Examples: Cold-chain monitoring • Cleanroom monitoring • Qualification data
Consumer & Industrial Products
Packaging lines and energy. Packaging line OEE, energy use per production cell and warehouse integration.
Examples: Line OEE • Energy monitoring • Warehouse integration
New Energy & Advanced Manufacturing
Cell and line data. Cell formation and test data collection and flexible line analytics.
Examples: Cell test data • Line analytics • Flexible data collection
Why Choose Zenkins for IoT Software Development?
One team from firmware to the operator screen. IoT projects fail at the seams between layers. We own the seams.
Full stack, one team
Constrained devices and enterprise integration under one roof.
Why it matters: Fewer handoffs and finger-pointing.
Proof of concept before platform
Real equipment and real data first.
Why it matters: Risk is retired early.
Protocols chosen from your environment
No preferred default pushed on you.
Why it matters: The design fits your plant.
Security from day one
Identity, encryption and segmentation are in scope for every platform.
Why it matters: Production and IP stay protected.
Connected to the business
MES, ERP, data and AI come from the same team.
Why it matters: Machine data becomes business value.
Works with legacy equipment
Gateway approaches avoid modifying proven machines.
Why it matters: No rip and replace.
Ready to connect your machines to your systems?
Whether you need machine monitoring, retrofit connectivity, an edge gateway or a full industrial IoT platform, Zenkins can take you from a proof of concept on your equipment to a secure production system.
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IoT software development for manufacturing: frequently asked questions
Find answers to common questions about IoT software development services, including use cases, architecture, device integration, security, cost, and timelines.
What is IoT software development?
It is the engineering of the software layers that let physical devices connect, send data, receive commands and integrate with business systems, from device firmware through cloud platforms to applications.
What is the difference between IoT and IIoT?
IIoT (industrial IoT) applies IoT to factories and plants, with extra needs: industrial protocols such as OPC UA and Modbus, OT network security, high uptime and integration with MES and ERP.
Can you connect our existing machines without replacing them?
Usually yes. We use gateway software and standard industrial interfaces to read data from existing PLCs and machines without modifying them, subject to your OT team’s approval.
Why MQTT, and when would OPC UA be better?
MQTT is lightweight and ideal for streaming telemetry to a broker or cloud. OPC UA is a rich standard for reading structured data from machines and PLCs. Many plants use OPC UA at the machine and MQTT upstream.
What is edge computing and why does it matter?
Edge computing processes data near the machine. It cuts latency, reduces bandwidth, keeps working through network outages and keeps sensitive data on site.
AWS IoT Core or Azure IoT Hub?
Both are capable. We recommend based on your existing cloud estate, security policies and integration needs.
How do you secure industrial IoT?
Unique device identities, mutual TLS, certificate rotation, IT and OT segmentation, fleet monitoring and testing against OWASP IoT guidance, with alignment to ISA/IEC 62443 for industrial control environments.
Can IoT data feed AI and predictive maintenance?
Yes. Telemetry feeds data pipelines and models from our AI/ML team, with results returned to dashboards and maintenance workflows.
Do you integrate with MES and ERP?
Yes. Machine data is made available to MES, ERP and quality systems through APIs and messaging. See API Development & Integration.
How much does an IoT project cost?
Cost depends on the layers needed, equipment variety, scale and security scope. A focused machine-monitoring project is much smaller than a full platform with firmware and edge. We scope after a discovery call.