Industrial AI Solutions
Practical AI applications purpose-built for industrial environments — delivering measurable improvements in uptime, quality, and efficiency.
Predictive Maintenance
ML models trained on vibration, temperature, current, and pressure data from motors, pumps, compressors, and conveyors to predict failures 2-8 weeks in advance, reducing unplanned downtime by 30-50%.
Computer Vision Inspection
Deep learning vision systems for real-time defect detection — surface flaws, dimensional accuracy, assembly verification, and label/packaging quality — replacing manual inspection with 99%+ accuracy.
Production Optimisation
AI models that analyse production data to recommend optimal machine settings, changeover sequences, and production schedules — maximising throughput and minimising energy consumption.
Energy Management AI
Intelligent energy monitoring and optimisation — predicting demand peaks, optimising chiller/pump schedules, and identifying energy waste patterns across production equipment and HVAC systems.
Quality Prediction & Process Control
Models that predict product quality attributes from process parameters in real-time, enabling proactive adjustments before out-of-spec product is produced — reducing scrap and rework.
Anomaly Detection for OT Security
AI-based network behaviour analysis that detects anomalous traffic patterns in OT networks — identifying potential cyber threats, rogue devices, or protocol anomalies before they cause operational impact.
AI Engineering Services
End-to-end Industrial AI services — from feasibility assessment and data readiness through model development, edge deployment, and ongoing model monitoring.
AI Feasibility & Data Assessment
Review of existing automation data (PLC tags, SCADA history, sensor logs) to identify high-value AI use cases and assess data quality, volume, and infrastructure readiness.
ML Model Development & Training
End-to-end model development — data collection, cleaning, feature engineering, model training, validation, and deployment — using industrial data sets and domain-specific feature knowledge.
Edge AI Deployment
Deployment of trained ML models on edge industrial hardware (Siemens Industrial Edge, Advantech, NVIDIA Jetson, or custom IPC) for low-latency, offline-capable inference at the machine or line level.
Cloud AI & Analytics Platform
Cloud-based AI analytics platforms (AWS, Azure, or on-prem) for aggregate model training, dashboarding, and continuous model retraining from edge-deployed systems across multiple sites.
SCADA-AI Integration
Bidirectional integration between AI models and existing SCADA/PLC systems — AI recommendations displayed on HMIs, automated setpoint adjustments, and alarm-triggered model inference.
AI Strategy & Roadmap
Strategic consulting to build an Industrial AI roadmap — identifying quick-win use cases, data infrastructure requirements, skills development, and phased implementation planning.
Industrial AI Use Cases
Real-world applications of AI across South African industry sectors.
Conveyor Bearing Predictive Maintenance
Monitor vibration and temperature on conveyor idlers and drive motors. AI model predicts bearing failures 3-6 weeks in advance, enabling planned replacement during scheduled maintenance.
Bottling Line Visual Inspection
High-speed camera system with deep learning detects fill levels, cap placement, label alignment, and product defects at 600+ bottles per minute — replacing human visual inspection.
HVAC & Chiller Energy Optimisation
AI model learns building thermal dynamics and production heat loads to optimise chiller setpoints, pump speeds, and damper positions — achieving 15-25% HVAC energy reduction.
Predictive Quality in Injection Moulding
Model predicts part dimensional quality from mould temperature, injection pressure, hold time, and material moisture — enabling real-time parameter adjustments before defective parts are produced.
Pump Cavitation Detection
Acoustic and vibration sensor data analysed by ML model detects early cavitation in centrifugal pumps — preventing impeller damage and unplanned pump outages in water and process plants.
OT Network Threat Detection
AI monitors industrial network traffic for protocol anomalies, unauthorised device connections, and unusual data transfers — alerting security teams to potential OT cyber threats in real-time.
Why Cretek for Industrial AI in South Africa?
Cretek bridges the gap between industrial automation and artificial intelligence in South Africa. We are not a pure software AI company — we are automation engineers who apply machine learning to real industrial problems, working with your existing PLCs, SCADA, sensors, and control infrastructure.
Our Industrial AI solutions start with data — we understand how to extract, clean, and time-align industrial data from diverse sources (PLCs via OPC UA, SCADA historians, IoT sensors, production databases). We then apply proven ML techniques — regression, classification, time-series forecasting, computer vision — to deliver practical outcomes: predictive maintenance that prevents downtime, vision inspection that catches defects, energy optimisation that reduces cost, and production optimisation that increases throughput.
We serve clients across Gauteng, Johannesburg, Pretoria, Boksburg, and nationwide — from food and beverage vision inspection to mining pump predictive maintenance and chemical process optimisation. All AI systems are deployed with proper MLOps practices: model versioning, data drift monitoring, retraining pipelines, and fail-safe fallback to conventional control.
Industrial AI — Frequently Asked Questions
How much data do I need for Industrial AI?▼
It depends on the use case. Predictive maintenance typically needs 6-12 months of historical data with corresponding failure records. Computer vision needs 500-5000 labelled images of good and defective products. Production optimisation can start with 3-6 months of process data. We assess data availability in our feasibility phase and can augment with targeted data collection if needed.
How long does it take to deploy an AI solution?▼
A typical first use case — such as predictive maintenance on a critical pump — takes 8-12 weeks from data collection to live deployment. Computer vision systems take 6-10 weeks with labelled data. Larger multi-use-case deployments take 4-8 months. We recommend starting with one high-value, low-complexity use case as a proof of concept.
What is the ROI of Industrial AI?▼
Typical ROI results from our projects: predictive maintenance reduces unplanned downtime by 30-50%, computer vision reduces defect escape rate by 90%+, AI energy optimisation reduces consumption by 15-25%, and quality prediction reduces scrap by 20-40%. Most projects achieve payback within 6-18 months.
Do I need data scientists on my team?▼
No. Cretek provides the full AI engineering capability — from data engineering and model development through deployment and monitoring. We can also train your automation team on basic model monitoring and interpretation. For ongoing AI programs, we recommend developing in-house data literacy over time.
How do you ensure AI models stay accurate over time?▼
We implement model monitoring — tracking prediction accuracy, data drift, and feature distributions. When performance degrades below a threshold, models are retrained on new data. Retraining can be automated (continuous learning) or triggered. We also design models with explainability — so operators and engineers understand why a prediction was made.
Can AI integrate with my existing SCADA system?▼
Yes. AI predictions and recommendations are displayed directly on SCADA HMIs, sent as alarms, or written back to PLC tags for automated setpoint adjustment. We integrate with Siemens WinCC, Ignition, FactoryTalk, and custom SCADA platforms via OPC UA, REST APIs, or database bridges.
Explore AI for Your Operations
Tell us about your operational challenges. Our Industrial AI specialists will assess your data readiness and identify high-impact AI opportunities with a clear ROI projection.