Quantum Computing

Case Study

Quantum inspired algorithms for Enel

Data Reply has developed a quantum algorithm that allows you to quickly plan the maintenance work performed by units operating throughout Italy. The solution also makes the use of resources more efficient, thus reducing costs. This algorithm brings added value to Enel's business by supporting the multinational in its decision-making process, with a high impact on planned daily activities

DATA REPLY & AWS

Data Reply provides solution on AWS Data & Analytics stack and ML stack. We build valuable data products starting from raw data sources and we provide a strong expertise in building machine learning and AI models on the AWS platform. Thanks to the multiple projects up and running in the AWS cloud environment and to a team with 50+ AWS certifications, we have built a deep experience in designing, implementing, and managing Data and ML applications on the AWS platform.

Validation Framework

Best Practice

We heroes portal

The service designed to boost your brand’s impact. Now more than ever, we are committed to help essential workers who are making a difference giving them a fast and reliable access to daily benefits.

AWS Cloud

Case Study

Getting Cloud and ML into the DNA of Nexi

Data Reply enabled Data Analysis and Machine Learning on AWS Cloud for Nexi, the largest Italian PayTech Company, bringing quantity and quality of data and leveraging Artificial intelligence-based technologies, resulting in major impacts in customer’s capabilities in areas like Fraud, Risk Management, Marketing and Operations, in a safe and compliant way.

Human machine interfaces

Best Practice

Assessing car damage with the use of Image Recognition

Data Reply has developed a framework based on Deep Learning techniques, Data Mining and Natural Language Processing capable of classifying input data, such as the photos taken by appraisers and the repair data recorded by car repair shops.

Data science

Best Practice

Thousands of new products: when to order and how many?

Defining the optimum quantity of the product to be ordered becomes a more complex issue whenever we are faced with a situation involving a lack of information, as is the case with a first order. It may be effective here to have a tool that can recommend the proper “sell-out” quantity, configured before the product is launched on the market.

University Events

Academy

Data Reply in Universities

Data Reply participated in Data Bites at City University of London and Data Science Challenge at Queen Mary University of London.