Use Cases

Real-time, adaptive Machine Learning holds a great promise in many domains, predicting best offer in retail, detecting and reducing financial fraud, fighting churn in telecom, forecasting crops in agriculture and more. Datomize helps teams to embrace Machine Learning and add real-time predictions to their core business.

Retail and Travel

Retail and Travel

With a great amount of data points and regular shifts in the operating conditions, retailes, airlines, hotels can boost their selling capabilities, improving consumer offers while maintaining margins, even when conditions change fast. Datomize platform’s constant learning adapts machine learning algorithms automatically to respond and follow the changes.

Banking

Banking

Adaptive real-time machine learning offers banks and other financial institutes ways to reduce risk and cost, enhance personal customer experience, detect fraud while responding to the quick changes in the markets.

Insurance

Insurance

With Machine Learning, insurance companies can improve predictions, becoming efficient and more profitable while bettering customer experience. With an constantly learning algorithm, Insurers allow for better personalized offers, reducing risk and loss.

Agriculture

Agriculture

A growing part of modern agriculture and Agritech specifically, is the ability to digest a multitude of inputs from soil samples, satellite data, weather and historic and give actionable predictions on irrigation and pest control as well as on future yields. In an ecosystem where nothing is stable, adaptive real-time Machine Learning holds a major advantage.

Healthcare and Pharma

Healthcare and Pharma

Healthcare institutes and Pharma companies collect and analyze huge quantities of data points and need a quick response with measurable accuracy. Real-time algorithms can help them deliver better outcomes while adjusting to new inputs. Unknown circumstances such as new virus strain can benefit from a quick turnaround of a real-time machine learning algorithm with an ability to adjust to new inputs.

Industrial Automation

Industrial Automation

As industrial manufacturing is moving into the 4th industrial revolution with digitization and automation, the ability to predict outcomes becomes more crucial. Machine Learning in predictive maintenance can help to identify malfunctions before they occur. Large amounts of sensor data digested in real-time by a Machine Learning algorithm can perform manufacturing operation optimization and streamline supply chain management.

Sports

Sports

Whether on or off the game field, real-time accurate predictions can give that little edge which competing is all about. With a constant flow of information and the ability to adjust and adapt, Machine Learning can help teams and individuals stay ahead and go for the win.

Utilities

Utilities

Smart utilities and grids are being deployed all over, with more data flowing back to control centers for electricity, gas and water. All this data needs to be leveraged with the power of machine learning to create a better, cheaper and more reliable service for customers. Better demand projections, planning, preventive service fault detection and others will utilize real-time accurate modeling which can react to quick changes like storms and unpredictable events to maintain the service people expect.

Gaming

Gaming

Gaming is all about competing for attention and maintaining a high interest while remaining entertaining. It relationship, adapting to quick changes in consumer behaviour while predicting churn and shifts in a vibrant and fast moving market. Gaming companies can benefit from AI using the huge amount of collected information they have on their players, increasing involvement, stickiness and preventing churn.

Telecommunication Service Providers

Telecommunication Service Providers

Telecommunication companies collect a huge amount of customer usage data as well as network performance information. In the fierce competition and the highly complex environments these companies operate, all this data needs to be leveraged to give the end customer a better personalized experience, preventing customer churn while improving network operation and experience, adapting to changes, faults with better real-time, adaptive models.