Across all industries, Data Analytics and AI are becoming essential building blocks for making business processes more robust and efficient. The reason is simple: data- and AI-driven applications deliver far more than transparency alone. Used effectively, they enable targeted interventions, drive continuous improvement, and empower decisions that look further ahead.
The potential use cases are broad — from reducing waste and optimizing energy consumption to improving product quality. Thanks to advanced data science methods such as AI-based image processing and the emerging field of Agentic AI, this potential continues to grow.
What truly matters are tailored approaches that turn production, process, and enterprise data into measurable results. Device Insight guides companies along their Data & AI journey, combining deep domain expertise with advanced data science know-how.
What sets us apart: our consultants, software engineers, and data scientists understand both the world of machines and the world of data. Together, we design analytics and AI solutions that focus precisely where operational improvements have the most immediate impact.
For two chocolate producers, Device Insight developed predictive quality models that forecast the optimal machine settings for each production batch. This allows production to start with minimal waste and top-tier quality — right up to the perfect snap.
For the Swiss manufacturer of elevator and wire ropes, Device Insight implemented an anomaly detection system for production. The solution is based on a process dataset that integrates sensor data, quality parameters, and machine signals to model normal process behavior. A deep learning algorithm identifies deviations in real time — for example, in tension or stranding parameters — enabling early intervention before quality issues or downtime occur.
For an international dairy and whey producer, Device Insight developed an anomaly detection model that analyzes product, recipe, raw material, and machine data, as well as key quality parameters. The system detects deviations by identifying relevant patterns and relationships between input variables — ensuring consistent product quality and stable production.
The most important question at the start of any data analytics project is: What’s the goal? Do you want to streamline production, reduce waste, stabilize product quality, or lower energy consumption? For each of these objectives — and many more — Data Analytics and AI provide powerful tools.
Here’s an overview of the four most common application areas for data- and AI-driven solutions:
One thing is clear: data alone doesn’t solve problems. Only when you can see in detail what’s happening within a system – how influencing factors interact, which parameters matter – can intelligent control become possible. That’s why, at Device Insight, we don’t just analyze datasets – we think in systems.
We capture a domain by diving deep into the physical realities around machines and products, then examine the complete data flow: from input (e.g., recipes) to output (e.g., product quality). On this foundation, we design robust architectures for your analytics and AI applications.
Our methodology provides the blueprint — the right technologies bring it to life. With proven techniques, we develop custom industrial AI applications for predictive maintenance, visual quality inspection, process optimization, and intelligent assistance systems — always with the goal of making your data directly actionable.
Machine and deep learning methods such as regression, classification, time-series forecasting, and anomaly detection form the core of our solutions. We also apply deep learning for visual inspection — including object detection and semantic segmentation — and generative AI with LLM integration. Each technology is carefully aligned to the use case.
Using platforms such as Databricks and Microsoft Fabric, we ensure the necessary computing power and secure data availability. These leading data intelligence frameworks form our central environment for analysis, model training, and integration.
With DataOps, MLOps, DevOps, and EdgeOps, we ensure that infrastructure and ML models aren’t just built, but also efficiently integrated, monitored, and continuously improved.
Depending on requirements, our solutions run centrally in the cloud (Azure, AWS) or locally on site.
The path to an effective Data Analytics & AI solution begins with an initial discovery: Which processes and assets are in focus? What data is available – and what’s needed? To answer these questions, we dive deep into your domain – analyzing historical data to uncover patterns, trends, and potential. Based on this understanding, we design a tailored solution architecture, select the right machine learning methods, and estimate the expected “Return on Data.” A Proof of Concept marks the first tangible result – a functional model that’s refined in a pilot phase, integrated into your systems, and continuously monitored. After rollout, we maintain and optimize the ML models to ensure lasting impact in day-to-day operations.
We showcase how companies can overcome data silos, cost traps, and complexity barriers when implementing Data Analytics.
We’ll help you keep the overview – and harness Data & AI precisely where it creates real business value.
