Mosaic Data Science’s optimization services leverage the latest advancements in artificial intelligence with traditional optimization techniques, such as mathematical optimization and scheduling optimization, to drive unparalleled efficiency and performance. Our expert team helps customers fine-tune processes, resources, and strategies through data-driven insights and predictive modeling. From supply chain streamlining to resource allocation, we harness the power of math to enhance decision-making and achieve optimal outcomes.
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 helped one of the US’s largest regional homebuilders deploy an artificial intelligence engine that recommends the optimal community features based on different geographic regions. The recommender delivers data-driven insights the company can trust when planning, building, and positioning new communities.
This blog discusses the decision process for adjusting flight schedules in response to weather events using advanced analytics.
We scheduled some time for this Q&A with Principal Data Scientist Evan Lynch, where we explored the many cross-industry challenges that scheduling optimization aims to solve, and the robust scheduling techniques that lie at the junction between mathematical optimization and machine learning.
We explore how ML & optimization can aid the risk assessment and management process with an eye towards robustness and resiliency.
This white paper explores how we can integrate third-party weather data and utility-specific asset prioritization requirements to build more relevant optimization tools for scheduling the inspection and potential repair of grid anomalies.
Mosaic helped a leading hospital optimize the scheduling of elective surgery and created a better daily rhythm for surgeons.
For the past several years, ML has exploded in popularity, while the excitement for MO has mostly plateaued. Why this has occurred is very much up for debate. One might surmise that ML is simply a better tool than MO, and therefore it replaced it in terms of popularity. This, however, is wrong-headed. ML and MO are typically used to solve very different problems. One might also think that problems MO has historically solved no longer exist.
Mosaic designed and deployed custom computer vision models to automate asset recognition & inform inspection decisions.