Industrial Automation Software Development
Industrial automation software built on the data your plant already generates
Modern manufacturing equipment generates more data than most plants know what to do with. PLCs log cycle counts and fault codes, sensors measure temperature and vibration, SCADA systems record process variables by the second. Almost none of that data reaches the people who could act on it. IoT machine monitoring software doesn't add sensors to your machines. It connects what already exists to a system that makes the data usable: live OEE, automated downtime tracking, and the condition data that feeds predictive maintenance.
OPC-UA, MQTT, and Modbus TCP connectivity to PLCs, SCADA systems, and industrial sensors across your plant
Real-time OEE dashboards showing availability, performance, and quality per machine, line, and shift
Automated downtime detection with operator reason code capture and MTBF/MTTR reporting
Condition trend monitoring feeding your predictive maintenance system and CMMS work order creation
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
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The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Plant managers walking the floor to find out which machines are running because there is no live visibility system?
OEE calculated manually from shift reports a day after the shift ends, when it's too late to act?
Plain answer
Industrial automation software connects a manufacturer's existing PLCs, SCADA systems, and sensors via OPC-UA, MQTT, and Modbus TCP to real-time OEE dashboards, downtime tracking, and predictive-maintenance data. RaftLabs builds these platforms so plant managers get live visibility into machine availability and performance without manual data collection. Every engagement is fixed cost, scoped in writing before development starts.
What to remember
- OEE is calculated continuously from live machine signals and operator inputs, not manual shift reports, as availability x performance x quality.
- Downtime clocks start automatically from machine signals, with operators prompted only for the reason code, producing accurate MTBF/MTTR per machine and failure mode.
- Older machines without digital interfaces can be monitored via low-cost I/O modules on existing relays, avoiding a full instrumentation retrofit.
- A first monitored line or cell covers connectivity plus a live OEE dashboard; a full single-plant rollout adds downtime tracking, alerting, and predictive-maintenance data. Multi-site expansion builds on the same platform.
Proof
- shipping production software across healthcare, fintech, hospitality, and logistics
- Since 2015
- RaftLabs delivery record
- average client rating on Clutch
- 4.9/5
- Clutch, verified reviews
- scope and cost agreed in writing before any development starts
- Fixed price
- Every RaftLabs engagement
IoT machine monitoring built on the signals your plant already generates
Industrial IoT software development turns the signals your plant already generates into live OEE, downtime, and condition data. RaftLabs connects your existing PLCs, SCADA systems, and sensors, then works with your engineering team to understand your equipment, protocols, and network topology. We design the integration layer to fit your environment: edge computing where bandwidth or latency makes cloud-first impractical, direct connectivity where the infrastructure allows it.
The payoff is not just visibility. The same condition data feeds predictive maintenance, which reduces equipment breakdowns by up to 70% and maintenance costs by roughly 25% (Deloitte). Capturing it cleanly from day one is what turns a monitoring dashboard into a system that prevents failures instead of just reporting them.
Capabilities
What we build
PLC and SCADA connectivity
Data normalisation across heterogeneous machine protocols (OPC-UA, MQTT, Modbus) into a single model, with edge computing nodes where bandwidth or latency requires local processing.
Real-time OEE dashboards
Availability, performance, and quality calculated continuously per machine, line, and shift, accessible on any device with configurable targets per asset.
Downtime tracking and alerting
Automatic downtime detection from machine signals with operator reason-code capture, and MTBF/MTTR reporting per machine and failure mode.
Predictive maintenance integration
Trend monitoring on vibration, temperature, and cycle-time drift feeding automated CMMS work order creation (SAP PM, Maximo).
Energy and utilities monitoring
Consumption tracked per machine and production area with anomaly detection for equipment drawing above-normal power.
Multi-site monitoring and reporting
Consolidated OEE, downtime, and energy reporting across plants with normalised data regardless of PLC vendor or protocol mix.
How we work
From scope to live monitoring platform
- Week 101
Equipment and protocol scoping
We map your equipment types, existing protocols, and the visibility gaps you're missing. You leave week 1 with a written scope document and a fixed-price quote.
- Weeks 2-502
Connectivity and data model design
Protocol mapping, edge computing needs, and OEE calculation logic designed against your actual plant. Your engineering team reviews the integration approach before any build starts.
- Weeks 6-1403
Develop and integrate
Connectivity, dashboards, and downtime tracking built in parallel, validated against real machine data.
- Final 2-3 weeks04
Launch and team rollout
Plant managers and maintenance teams trained on the dashboards before full rollout.
Why us
Why manufacturers choose RaftLabs
- 01
The team that scopes it builds it
The engineers who assess your equipment also build the solution. No bait-and-switch, no offshore handoff after the contract is signed.
- 02
Fixed price before development starts
We scope the work, calculate the cost, and lock it in writing before any development starts.
- 03
Shipping production software since 2015
Real engagements across SaaS, fintech, healthcare, and logistics, not every one published as a named case study. We build data-heavy platforms where reliability and clean integration matter.
- 04
No integration surprises during build
Connectivity approach for every machine type is documented during discovery, not discovered mid-project.
- 05
Built as the foundation for predictive maintenance
The condition data captured is designed to feed failure-prediction models from day one.
Work with us
Have an IoT monitoring project?
Tell us your equipment types, existing protocols, and what visibility you're missing. We'll design the integration and give you a fixed cost.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.
Common questions
We work with OPC-UA, MQTT, Modbus TCP, and REST APIs as the primary connectivity methods. OPC-UA is preferred for modern equipment as it carries rich metadata alongside process values. MQTT works well for high-frequency sensor data. Modbus TCP covers the large installed base of older PLCs. For machines that pre-date digital interfaces, we assess whether signals such as motor run status or fault relay outputs are accessible and design an I/O bridge accordingly.
Overall Equipment Effectiveness is the product of availability, performance, and quality. The software calculates each component from live machine signals and operator inputs rather than manual shift reports, so the number is current and accurate rather than a day-late calculation.
IoT monitoring provides the data layer that predictive maintenance models run on. The monitoring platform captures and stores time-series condition data per machine. Predictive models are trained on that historical data to identify patterns preceding specific failure modes, then run against the live data stream to flag when a machine's condition is trending toward failure.
Yes. The monitoring layer is designed to feed and read from the systems you already run. We push production counts and downtime events up to MES and ERP, and open work orders in a CMMS such as SAP PM or IBM Maximo when a condition threshold is crossed. The normalised data model also serves as the foundation for a digital twin if you plan to simulate line changes later.
Can the solution read native PLC data for the models in your plant? Which protocols are supported: OPC UA, MQTT, Modbus? Match the platform to the existing stack; fighting it adds cost never on the quote. OT data exposure increases risk. Mitigate with network segmentation, role-based access, and encrypted tunnels. Decide what must happen at the edge: safety interlocks, quality rejects, anything measured in milliseconds runs locally.
Most projects start by reading existing PLC signals via gateways or adapters. Replacement is only necessary when legacy hardware cannot expose the required telemetry. Machines that speak no standard protocol drive the integration budget more than any software license. List every controller brand, protocol, and rough age before vendor conversations. It controls the integration budget.
Tie the pilot to a single business KPI, like OEE. Pilot small, but design for the second plant. Count the full cost: licensing, gateway hardware, integration work, training. Ask vendors for a three-year number, not a monthly one.
We scope in phases. Cost depends on the number of machines, the protocol mix, and whether edge computing is needed. Request a 30-min call and we will scope your plant and give you the number.
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