Mosaic helps businesses design and deploy IoT solutions powered by custom AI & ML models that reduce operational costs and achieve business growth in real time.
A hospitality technology disruptor was developing a new sensor suite to detect when a guest was smoking inside a property and distinguish it from approved activities such as cooking. They engaged Mosaic to build time series classification models to identify and alert on smoking events from the streaming sensor data.
Using AI to analyze sensor data, Mosaic helped a renewable energy producer optimize furnace temperature to improve wood pellet production efficiency by reducing slag production and pressure buildup and providing control room operators with easy-to-use recommendations for setting furnace controls.
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 helped a commercial airline integrate custom machine learning to predict departure runway and taxi-out time, cut operational costs, and improve the customer experience, aiding in reducing fuel emissions with AI.
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.
Given a set of nodes & connections, which can abstract anything from transportation networks, connections between customers, knowledge graphs, or molecular structures to computer data, graph analytics provide a helpful tool to quantify & simplify the many moving parts of dynamic systems.
Both descriptive analytics and machine learning models can benefit greatly from using geographic data analysis to solve segmentation use cases.
Anytime you wish to predict the transient state(s) of something or someone constantly monitored by sensors, time series classifications are the right tool. This article will explain some basic concepts of using deep learning models for TSC and finish with a brief discussion of ways to improve the performance to save on cost and speed.
Mosaic developed a data-driven alerting solution powered by unsupervised learning to assist a leading energy utility in detecting voltage anomalies & informing optimal grid health decisions.
Mosaic developed an innovative optimization app for the green power sales function at a leading utility, helping them recommend suites of renewable energy products to meet corporate carbon footprint reduction goals within budgetary constraints.