Laboratories and technology centers face the challenge of maximizing laboratory equipment utilization. Data needs to be shared between unrelated laboratory equipment from different manufacturers for more effective, efficient and smart use of laboratory equipment.
By using smart sensors, the activity of laboratory devices can be measured and interpreted using Big Data, Machine Learning and Artificial Intelligence. Analyzing the utilization of devices helps us create resource-saving laboratories and gain new insights into materials.
Artificial intelligence is revolutionizing data processing: larger amounts of data than ever before can be processed. This allows data from labs to be read and interpreted in ways that humans or applications themselves cannot. This can create insights that make labs smarter.
Measuring power to feed into MOSAIK is done via smart sensors, which are a small revolution for laboratories. Their data collection makes it possible to read interoperable lab devices simultaneously - and measure activity. This is changing the way labs plan their workloads in a groundbreaking way.
Web and mobile applications allow everyone involved with the laboratories or technology centers to access information on lab equipment usage. This allows users to see the capacities of the devices they want to use. As well as allowing operators to manage utilization and occupancy.
Multi-vendor solution that meets all requirements and is offered ready to use without major implementation risks.
Establish a stronger market position by offering unique technological features compared to your competitors.
Start transforming your repetitive tasks into smart processes using AI, to save valuable time and money.
Connect a few devices or interconnect separate technology parks – MOSAIK is perfectly scalable for your business needs.
Start standardizing processes of planning and utilization to free up time to address future topics.
Offer the information acquired by MOSAIK in user-friendly applications – mobile or web – that adapt to users’ specific needs.
Simulation of natural intelligence in machines programmed to learn with experience and interpret contexts. Able to interact and cooperate with other digital systems.
Machine learning generates knowledge from experience by using algorithms to create artificial knowledge from examples in a learning phase.
Expertise in single repositories for all enterprise data used for reporting, visualization, advanced analysis, and machine learning.
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