COMPUTER VISION AI

Challenge: 
With growing visual sensors, photo and video cameras, the need for fast and accurate processing of these images grows. AI-powered vision systems have a benefit over classical computer vision in many applications, and definitely over manual interpretation and monitoring. Nowadays, anyone can make use of available AI models to process an image, the challenge is in making sure you build a solution that is fast, accurate, robust, trustworthy, and integrated in your application. Enabling new capabilities for the future, like autonomous systems.

Solution:
We have experience with a broad range of vision AI use case and technique and have developed our own set of tools to handle any computer vision challenge. Ranging from object detection, tracking, classification, instance segmentation, detecting anomalies, etc. We leverage available foundational models as starting point, tailoring, finetuning and further developing them to your specific use case.

Techniques and example applications:

SENSOR AI

Challenge: 
As smart systems become more prevalent, the number of sensors embedded in machines, vehicles, and devices is growing exponentially. It opens up new possibilities, but also comes with some challenges:

• More data to process, with increasingly demanding latency and performance requirements; how to handle this overload of data real-time?
• Sensor and real-world data is often noisy, incomplete or inconsistent; how to retrieve valuable and accurate information from this?
• Security and confidentiality; how to handle this data with care?

Solution:
We have and apply a toolbox of techniques, ranging from classical signal processing to advanced deep learning and Bayesian methods; as well as making sure the algorithm and implementation design is ready for real-time, resource-efficient processing and deployment, on the edge, in the cloud or inside your application.

GENERATIVE AI

Challenge: 
Current analysis methods of logs, documents and text files in industrial settings but also other domains like public services, archiving, as well as internal business processes are inefficient, relying heavily on manual analysis and actions, complex dashboards, and resource-intensive ML algorithms. These methods fail to scale to multi-sensor problems and do not utilize textual data and multi-modal system documentation.

Solution:
By integrating Large Language Models with autonomous AI agents, we enable a powerful system capable of interpreting complex text data (documents, files, logging), generating context-aware insights, and making informed decisions with minimal human intervention. LLMs provide the linguistic and contextual intelligence to translate raw data into meaningful narratives, while AI agents leverage this understanding to autonomously analyze logs, identify patterns, and take action. This synergy reduces cognitive load for engineers, enhances operational efficiency, and ensures timely, accurate responses across technical and stakeholder domains.