Managing Business Travel Risk with Machine Learning
Mosaic is developing a machine learning based tool that assists corporate travel manages and business travelers in making the safest travel decisions possible.
Our expert team specializes in developing tailor-made machine learning solutions that drive innovation and growth. With a proven track record of successful implementations across diverse industries, Mosaic Data Science has helped customers elevate their operations, stay ahead of the competition, and make data-driven decisions with our comprehensive machine learning development services.
Mosaic is developing a machine learning based tool that assists corporate travel manages and business travelers in making the safest travel decisions possible.
Decision processes in support of jobs that either cannot be or are very difficult to automate are frequently overlooked by out of the box software providers. One such process is the creation of optimal staffing plans for outbound teams loading cartons onto trucks.
Weather has a high impact on operations in many industries, and therefore is of great value to integrate into strategic decision making. Mosaic has roots in aviation research & development, giving us deep expertise in combining weather data streams with planning applications to facilitate efficient resource allocation.
A leading clothing manufacturer distributor and retailer of clothing realized they needed to fortify their pricing decisions with machine learning insights.
For many pharmaceutical firms, trial recruitment forecasting plays a role in trial recruitment planning. However, these forecasts may be generated with relatively simplistic approaches based on only a small subset of available internal & external data.
Gameplay data are a trove of information about how athletes are acting and reacting in real situations, and there are real benefits to be gained by mining this information at every level, from the athlete to the entire team. In the modern age, the team that can measure and understand itself through its own data will have the competitive edge.
Meeting customer expectations is more difficult than ever, more and more of market share goes to companies who are able to perceive needs rather than react. Whether e-tailing or selling in brick-n-mortar stores, inventory planning is a promising area for predictive analytics,
Optimizing seasonal staffing and resourcing is a key challenge for many industries, especially when the exact timing of high-volume activity can change based on complex factors.
Mosaic built a model that predicted signal disruption, up to 16 hours in advance, based on North American precipitation forecasts and historical signal quality measurements.
We examine how to apply machine learning to segment based
on transaction data and transform those clusters into customer segments.