AI in manufacturing is a key focus of the innovation strategy at SCIIL AG and SCIIL BALTIC UAB. We develop advanced industrial software solutions for production, quality assurance and AI-driven data analysis. Our research and development activities include both EU-funded projects and self-financed initiatives, with a strong focus on real-world applications in manufacturing.
On this page, we provide an overview of our ongoing and completed R&D projects.
SCIIL BALTIC UAB implements the “REPORTIO II” project:
AI-based solution for poultry farms
SCIIL BALTIC is implementing the “REPORTIO II” project to enhance its smart farm monitoring system with new, user-relevant solutions. The system is based on IoT sensors, machine learning and advanced data analysis.
Upon successful completion, the existing Reportio product will be extended with additional smart functionalities.
- Project title: “REPORTIO II”
- Project implementer: UAB SCIIL BALTIC
- Project objective: to further develop the existing Reportio system by adding new, user-relevant smart monitoring and analysis functionalities based on IoT data, machine learning and advanced analytics.
Total project volume: EUR 1,638,052.06
Support from the European Regional Development Fund: EUR 737,123.44
VISION – AI-supported visual quality inspection in industrial production
- Project sponsor: SCIIL AG, Germany
- Funding program: Tax research allowance under the German Research Allowance Act (FZulG)
Project objective
Development of an AI-based, OEM-agnostic platform for automated visual quality inspection in industrial production environments. The system is designed to detect dynamic and static defects in real time and provide digital support for quality processes directly on the store floor.
Project description
As part of its innovation and research activities, SCIIL is developing a modular AI-based visual inspection solution that integrates industrial camera systems, edge processing, server infrastructure and a central data and application platform.
A key focus of the project is the development of advanced deep learning models for detecting complex and variable defect patterns, such as wrinkles, material deformations, contamination and assembly deviations. The system is capable of analyzing both dynamic defect patterns (e.g. position-dependent defects) and static characteristics.
The main technological challenges include:
- developing robust training methods for highly variable production conditions
- real-time processing of large volumes of image data
- ensuring scalability across different camera systems and OEM environments
- seamless integration into existing production systems, MES and quality management systems
The project includes the development of prototypes, pilot installations in real production environments and continuous optimization of AI models and system performance. The objective is to create a market-ready, industrial-grade solution that can be deployed both on-premise and in hybrid architectures.