Discover which technologies leading companies in our network are already using successfully. We will show you real-world solutions and practical examples of how other companies analyze and proactively use their industrial data streams.
Our platform brings together leading technology partners and solution providers who support you in implementing your Predictive AI by providing the necessary expertise and know-how. Rely on proven solutions and experienced specialists from the IIoT community. They know which Predictive AI solutions perform reliably in demanding industrial environments.
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Predictive AI makes industrial data streams analyzable, predictable and actionable. It detects patterns, anomalies and trends long before problems arise.
Predictive AI is a methodological approach in which IoT data is systematically analyzed using machine learning and deep learning to predict future events or conditions. Algorithms learn from historical sensor data, image material or machine parameters and recognize patterns that indicate potential failures, quality deviations or inefficiencies.
Predictive AI is particularly relevant in industrial contexts where real-time data is generated from production systems, energy infrastructure or logistics processes, often in large volumes and at high speed. AI-driven systems make it possible to replace manual analyses, achieve precise predictions and directly automate operational decisions.
Predictive AI is the data-driven use of artificial intelligence for early event detection or process optimization. It combines industrial IoT data with learning models and enables proactive intervention before problems become visible.
IoT data is collected but neither connected nor analyzed
Manual visual inspections are error-prone and costly
Failures are only detected when it is already too late
Systems run at peak load even when more favorable time windows are available
Although many industrial companies have already equipped their machines, systems and logistics processes with sensors and IoT technologies, they often lack concrete, actionable insights. Large amounts of data are collected, but they are rarely analyzed systematically or used for active control.
Manual quality inspections, delayed maintenance actions or inefficient energy usage are typical examples of untapped potential. Lack of transparency, response times in crisis mode, and knowledge loss on the shop floor are among the biggest obstacles to operational excellence.
Predictive AI follows a clearly structured process. First, data is collected from machines, sensors, cameras or energy measurements. This data is then analyzed in real time. The AI models detect deviations, patterns or trends and derive precise predictions and decisions from them.
What makes it special is that these decisions are either executed automatically (for example as an alert, action or control command) or provide experts with a solid basis for the next step. Predictive AI thus becomes a digital assistant at the shop floor, control room or management level.
Early detection of deviations in system behavior, such as temperature, vibration, energy consumption or utilization.
Image recognition and deep learning replace manual visual inspections with objective, AI-supported quality decisions.
Instead of reactive servicing, AI predicts the next optimal maintenance interval based on real operating data.
AI analyzes power flows and controls systems to take advantage of favorable time windows or meet CO₂ targets.
Predictive AI analyzes complex data relationships and supports root cause analysis directly on the shop floor.
Through machine learning, the system suggests optimal settings for production or machine operation, in some cases automatically.
Our partners demonstrate how AI creates real value from IoT data—from quality inspection to energy optimization.
Predictive AI is no longer just a lab project. Companies within our network are already using AI-based applications that make processes safer, more efficient and more transparent. Whether in manufacturing, logistics or energy, these solutions deliver measurable benefits in everyday operations.
Learn how ACP CUBIDO automates quality control using AI and cameras—directly on the shop floor, efficiently and at scale.
Discover how Softserve tailors quality inspection processes to specific industrial requirements. The potential ranges from image processing to AI-driven anomaly detection.
OPC UA simplifies machine communication in food manufacturing, increases efficiency and connects production systems worldwide.
The combination of industrial data sources, machine learning and a scalable architecture enables the use of AI applications on the shop floor. Our partners rely on proven technologies built on a consistent, modular, interoperable and scalable architecture.
Less Waste and Rework
ACP CUBIDO: Defect detection during assembly using camera and AI
Shorter Lead Times
Softserve: Inline inspection without sampling
Better Decisions
Bühler: AI-based process evaluation in the wafer workshop
Fewer Unplanned Downtimes
Digital Services & Partnerships
Many IIoT projects fail not because of the idea, but because of the implementation: a lack of scalability, high operating costs and unclear requirements lead to expensive rework and a failed business case.
On our platform, you will find tried-and-tested technologies, best practices from real industry projects and the collective knowledge of our community. We show you how to avoid typical mistakes with the right technology stack – from data acquisition to AI evaluation – and how to set up your IIoT project economically and future-proof.
Discover how leading companies from our network successfully structure their projects – modular, interoperable and data-secure.
Data acquisition forms the solid foundation of your IoT application. Whether machine, operating or sensor data - accurate and reliable data acquisition enables precise analyses and data-based decisions. Modern solutions capture data directly at the machine, standardized and in real time.
Reliable data transmission is essential for every IoT process. Choose between wired (e.g. Ethernet) and wireless technologies (e.g. 5G, LoRaWAN) based on your requirements in order to optimally combine stability and flexibility.
The efficient preparation and pre-processing of your raw data ensures that it can be used immediately. Whether edge computing or local pre-processing - reduce the amount of data and significantly improve the performance of your IoT systems.
Data standardization creates the basis for efficient, cross-manufacturer communication and consistent use of data throughout the entire life cycle. Whether through protocols such as OPC UA over MQTT for secure transmission or standardized product data and digital twins - your IIoT projects remain flexible, scalable and economical.
The convergence of production technology (OT) and information technology (IT) enables you to achieve a transparent data flow without media disruptions. This allows you to eliminate data silos, speed up decision-making and optimize your operational processes in the long term.
IoT platforms form the central nervous system of your digital infrastructure. As PaaS or SaaS solutions - for example in the form of customer portals for manufacturers - they store, visualize and manage IoT data. As a result, you always have a comprehensive overview of your processes and can make well-founded, data-based decisions.
Protecting sensitive industrial and process data is a top priority. Modern security concepts ensure that your data is transmitted and stored in encrypted form and that your systems always comply with current regulatory requirements.
Efficient management of networked IoT devices is a key component of successful digitalization strategies. From commissioning and updates to decommissioning - structured device management reduces operating costs and significantly increases the security of your IoT infrastructure.
Visualized data enables faster and more precise decisions in your company. Modern dashboards and graphical presentations transform complex data streams into clear, real-time displays and create transparency at all levels of the company.
The systematic analysis of your IoT data uncovers hidden correlations and identifies optimization potential in your processes. From descriptive statistics to complex analysis procedures - gain valuable insights from your operational data for well-founded business decisions.
Data analysis is the basis for data-driven IIoT applications - from process optimization to AI-supported predictions. While traditional evaluations work with fixed rules, AI algorithms independently recognize patterns and anomalies - for predictive maintenance or quality forecasts, for example. Both approaches complement each other and optimize the potential of your IoT data.
Industry-specific IoT applications address concrete challenges with preconfigured functionalities. From production optimization to asset tracking — these specialized solutions offer rapid time-to-value and can be flexibly adapted to individual requirements.
Predictive AI does not emerge in isolation. For AI to deliver reliable predictions on the shop floor—such as for faults, wear or energy consumption—data from sensors, control systems and machines must be standardized, analyzed and contextualized. Our partners rely on end-to-end architectures, from data acquisition devices and model development to AI-driven decision-making. Each technology serves as a functional building block: interoperable, industry-grade and scalable.
Our community brings together industry experts who have already implemented successful IIoT projects – openly, practically and on an equal footing. Gain insights into how other companies have solved challenges, share your use cases and discover new ideas and concrete solutions for your business.
Every innovation starts with an idea. Discover proven use cases that support your digital transformation — from predictive maintenance to worker safety.
Real-time monitoring of machine and sensor data to reduce downtime.
Data-driven maintenance to detect failures early and cut costs.
Seamless tracking of assets and material flows in production and logistics.
Automated collection and management of production and operational data.
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Whether you need initial insights, specific questions or concrete use cases – we can help you! Contact us for individual consultation and solutions.
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IoT is the common abbreviation for the Internet of Things. The definition to the meaning is wide, so we translate the terms from industry to practice here.
Whatever you call it, you’ll find it here without “buzzword bullshit bingo.”
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