AI- and ML-Powered Data Solutions

Automate the ordinary. Adapt to change. Discover actionable insights.

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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.

Our clients include:
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Solving a $60M inventory loss problem

A closer look at how the emerging tech and analytics teams applied machine learning to operational technology, increasing product identification accuracy to over 90%.

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Our Approach

Feature Preparation

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:

  • Gain stakeholder alignment on what is important
  • Define vision for what value the output drives
  • Ensure the data is clean and complete
Model Preparation

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.

  • Determine the right type of model(s)
  • Develop metrics to assess performance from both technical and stakeholder perspectives
  • Iteratively train model(s) over the hyperparameter space
Model Deployment

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.

  • Choose frameworks that work with existing production environments
  • Create a robust, testable pipeline for deployment
  • Automate the deployment pipeline
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Understanding & implementing recommendation engines

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Businesses across all industries can use these systems to create a better experience for customers while also helping employees make more-informed decisions.

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Applying agile to speed up your data projects

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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.

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Are you overcomplicating data intelligence?

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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.

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Are you looking to create change, faster, with your data?

We can help you make that happen. Let's set up a time to discuss how.

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