Stop losing shifts to downtime you could have predicted.
CoralUnity engineers OPC-UA SCADA integrations, MQTT-based IIoT sensor networks, and ML-driven predictive maintenance systems that surface real-time production intelligence, from the plant floor to the executive dashboard, before the next failure costs you a shift.
Talk to an ExpertChallenges we know how to solve
Every unplanned breakdown is a shift you will never get back.
Unplanned downtime costs discrete manufacturers an average of $260,000 per hour in automotive and $22,000 per hour in general manufacturing (Aberdeen Research). Your maintenance team cannot inspect their way out of this. They are reactive by design. CoralUnity deploys ML models trained on your actual sensor data that detect failure signatures days before the breakdown, giving your team time to act on the next scheduled window instead of scrambling at 2 AM.
Your operators are drowning in paper while your competitors run lean.
Manual production records, paper travelers, and clipboard-based quality checks consume 15-20% of operational labor capacity per shift and introduce transcription error rates as high as 23% (MESA International). CoralUnity replaces these workflows with role-based digital forms, automated approval routing, and real-time exception alerts, deployed in weeks, not a multi-year ERP rollout.
You are managing your plant with data that is already 8 hours old.
End-of-shift data collection means quality escapes, yield losses, and OEE degradation compound for an entire production run before anyone can intervene. CoralUnity connects your PLCs and SCADA historians to live dashboards with sub-second latency from sensor read to management view. Your floor supervisors see deviation alerts the moment a process drifts out of spec.
What we build for your industry
OPC-UA & SCADA Integration
We bridge legacy PLCs, DCS, and SCADA platforms to modern web dashboards via OPC-UA and OPC-DA, eliminating proprietary data silos. Plants gain historian-grade data access without ripping out existing control infrastructure.
IIoT Sensor Network Architecture
We design and deploy edge-to-cloud IIoT architectures using MQTT over TLS, purpose-built for high-frequency machine telemetry in harsh industrial environments. Includes edge gateway configuration, protocol translation, and cloud ingestion pipelines to AWS IoT or Azure IoT Hub.
ML-Based Predictive Maintenance
Our data engineers train anomaly-detection and remaining-useful-life models on your time-series vibration, temperature, and current-draw data using TensorFlow and scikit-learn. Models deploy at the edge via ONNX, catching failure signatures 72-200 hours before breakdown.
Custom MES Development
We build Manufacturing Execution Systems aligned to ISA-95 standards, covering work order management, WIP tracking, genealogy, and electronic batch records, integrated directly with your ERP (SAP, Oracle, or custom) and SCADA layer.
Shop Floor Process Digitization
We replace paper travelers, manual sign-off sheets, and clipboard-based quality checks with digital workflows, electronic signatures, and real-time production reporting, enabling full 21 CFR Part 11 or ISO 9001 audit trails where required.
OEE & Quality Intelligence Dashboards
We build shift-level OEE dashboards that decompose Availability, Performance, and Quality losses in real time, giving floor supervisors, quality leads, and plant managers a single view to drive daily continuous improvement decisions without waiting for end-of-shift reports.
Results from the field
“After CoralUnity integrated our Siemens S7 PLCs with an OPC-UA historian and deployed ML anomaly detection on our press line vibration data, we cut unplanned downtime by 44% in six months and avoided two catastrophic bearing failures we would never have caught with manual inspections.”
— VP of Manufacturing Operations, Tier-2 Automotive Stamping Supplier (4 plants, 1,200 employees)
What's your next technical challenge?
We work embedded in your team from day one. No generic consulting, no overhead. Just real delivery with measurable results.