Mosaic Data Science clients have successfully used Amazon AWS to solve data warehouse, analytics, and AI/ML problems. Discover how our partnership and AI services with AWS have helped customers optimize efficiency, improve decision-making, and accelerate innovation with new, valuable insights.
Mosaic developed Explainable AI in Lunar Operations to support lunar rovers using imagery data from hardware to assist NASA rovers in assessing the traversability of a given region of terrain, supporting increasingly autonomous operations.
Mosaic applied our robust data analytics expertise across disciplines to develop novel, data-driven models that identify anomalous behavior for a leading aerospace government agency.
Mosaic brought its deep expertise in aviation to design and test effective machine learning models that would predict the arrival and departure assignments of flights, helping facilitate automated testing and deployment of advanced predictive decision-support tools.
Mosaic helped a nationwide retailer model the price elasticity of demand across their product catalog, empowering them to optimize prices and maximize SKU revenue.
Mosaic worked with a vet tech company to develop an encompassing pet wellness score and early disease-identification models using an AI-driven collar device that collects pet behavioral information and reports health insights to the owner’s app using IoT and sensor data techniques.
Mosaic helped a trucking and logistics operator optimize their machine learning deployment on Amazon Web Services (AWS). As an AWS Select Partner, Mosaic was well-positioned to deploy machine learning engineering and serverless architecture services that sped up model inference while performing a minor overhaul of the AWS architecture and code base organization.
Mosaic built a computer vision solution to improve the process of a vehicle moving through an automated car wash.
Mosaic was contracted by multi-national manufacturer of construction and mining equipment to develop a proof-of-concept predictive maintenance model to predict equipment failure before it happens to minimize downtime and optimize maintenance schedules.
Summary Mosaic helped an energy company optimize its hydrocarbon inventory management process by working with them to develop a centralized system to collect and control inventory data quality, improve accuracy, and improve the bottom line. Why Intelligent Inventory Management Matters in the Oil & Gas Sector The oil and gas Read more…
Mosaic designed and deployed a custom machine learning model to help this retail energy company predict customer churn and inform a geographic growth strategy.