Assistance system
Definition
Assistance systems are digital or AI-powered applications that help laboratories perform complex tasks more efficiently, quickly, and with fewer errors. They automate workflows, analyse data, and provide context-relevant information.
Expanded Explanation
Modern assistance systems use technologies such as artificial intelligence, machine learning, natural language processing, or rule-based automation to simplify processes and support decision-making. In laboratory environments, they handle tasks such as searching for measurement data, analysing large datasets, generating visualisations, or identifying material patterns. Assistance systems reduce manual work, minimise error sources, and enable specialists to focus on higher-value activities. They play an increasingly important role in the digitalisation and optimisation of scientific and technical processes.
Relevance to LabV
LabV integrates an AI-powered assistance system that can access all relevant laboratory and material data and support users through simple prompts—similar to ChatGPT, but specialised in material development and quality assurance. The system retrieves measurement data, performs analyses, identifies correlations, and generates automated reports. As a result, LabV eliminates manual steps, improves decision quality, and significantly accelerates processes in R&D and quality control.
FAQ
What are assistance systems used for?
Assistance systems handle repetitive tasks, analyse data, and support decision-making to make processes more efficient and secure.
How do AI-based assistance systems work?
They use algorithms to process data, detect patterns, and automatically provide suggestions or results.
What advantages do assistance systems offer in the laboratory?
They reduce manual data entry, improve documentation, accelerate data analysis, and enable more reliable, data-based decisions.
Synonyms & Related Terms
Digital assistance, AI assistant, automation systems, intelligent assistant software
Internal Links
Material Intelligence, Laboratory Digitalization, AI in Laboratories, Predictive Analytics