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The following case studies illustrate how our method, expertise and technologies can be applied to turn challenges into tangible successes.
At Aiternalab, we turn challenges into innovative opportunities
Manufacture
Manufacturing Process Optimisation with AI
Challenge: A manufacturing company is facing inefficiencies in its production line, resulting in delays and high costs.
Potential solution: Implementation of a predictive maintenance system based on artificial intelligence algorithms to identify problems before they occur, thereby optimising production times and reducing operating costs.
Integration of blockchain technology for traceability in the supply chain
Sfida: Un’azienda di logistica necessita di un sistema più trasparente e sicuro per la gestione della sua supply chain.
Potenziale soluzione: Integrazione di un sistema tecnologico basato su tecnologia blockchain per garantire la tracciabilità completa dei prodotti lungo l’intera catena di fornitura, aumentando la sicurezza e la fiducia dei clienti.
Logistics
Healthcare
Development of a health app aimed at optimising data collection and analysis
Challenge: A healthcare facility is confronted with the complexity and inefficiency of managing patient and caregiver data. The difficulty in consolidating, accessing and analysing this data effectively limits the ability to provide an optimal and timely service to its patients.
Potential solution: Development of an integrated healthcare data management solution, leveraging advanced technologies such as cloud computing and data analysis algorithms. The system would enable efficient collection, storage and analysis of data, ensuring security, compliance and real-time accessibility for healthcare professionals.
Innovative Video Surveillance System with Machine Learning
Challenge: To proactively identify and treat risks.
Potential Solution: Integrated video surveillance system with advanced machine learning algorithms specifically designed to identify and treat risks, respecting risk mitigation rules.
Security
Industrial Maintenance
Optimising Maintenance with AR Viewer
Challenge: To improve the efficiency and safety of maintenance procedures.
Potential Solution: Using an augmented reality (AR) viewer integrated with advanced pattern recognition algorithms, designed to optimise industrial maintenance procedures and increase safety.
Optical and Physical Sensors for Quality Control in Automotive Production
Challenge: To identify defective parts during production.
Potential Solution: Implementation of a system based on optical and physical sensors, supported by machine learning algorithms for pattern recognition, to improve quality control in the automotive production line.
Automotive
Marketing
Sentiment Analysis for Advanced Marketing Strategies
Challenge: To effectively analyse consumer feedback.
Potential Solution: Development of NLP-based Sentiment Analysis techniques to analyse consumer feedback on various platforms, identifying prevailing sentiments and trends to refine marketing strategies.
AI Algorithm Optimisation Framework
Challenge: To improve the performance of AI algorithms.
Potential Solution: Creation of a framework focused on the optimisation of hyperparameters, crucial for maximising the efficiency and effectiveness of artificial intelligence models.
Artificial Intelligence