Oops! Something went wrong while submitting the form.
Digital Twin Webinar: Understanding the Retail Analytics Glow-Up
Register Here →
Implementing artificial intelligence or machine learning to speed up or augment decision making is an arduous task. Often, organizations struggle with data quality (too little data, inconsistent data, or unusable data) and can’t make progress on their own.
Whether your team is thinking about AI/ML but hasn’t gained traction yet, or if you just need support keeping current momentum going, RevUnit can help you with agile methodologies that lead to successful solutions, fast, and prioritize practical over theoretical.
Understanding data features, or the collection of input data to a machine learning model, is central to developing effective automation with machine learning. It’s impossible to attain quality results without clean, sensible features, so we:
Machine learning is complex and requires lots of experimentation to build the most successful model based on well-defined metrics. Model preparation is critical to successful automation.
A model that is not deployed is not providing value. Like software deployment, there is an entire art associated with AI deployment, and that can’t be overlooked when achieving successful automation.
Create adaptable assortment planning that leverages AI to help your planners create profitable, unique assortments.
Better connect shippers to carriers, cutting out manual processes and optimizing truck capacity.
Businesses across all industries can use these systems to create a better experience for customers while also helping employees make more-informed decisions.
Whether your data project involves building a data model, a data visualization, applying machine learning, or data integration, taking an agile approach will get you better results, faster.
Data intelligence is simply about driving the right insights, to the right people, at the right time — and companies have an advantage if they can move fast on AI and ML.
