Nonstop wanted to build a new ML algorithm for its cost estimation process but had limited ML experience on the cloud.
ClearScale created an effective ML solution for Nonstop and set the team up to manage the algorithm going forward.
Nonstop’s cost estimation process is now much faster and more accurate, which will lead to better overall customer outcomes and a quicker sales cycle.
Amazon Forecast, Amazon Sagemaker
Nonstop Administration and Insurance Services is a healthcare technology company that streamlines all aspects of the benefits administration process, including claims management and financial reporting. Nonstop offers an all-in-one portal that keeps everything employers and employees need in one place. As a result, companies spend less time, money, and energy on benefits administration, and employees get the healthcare coverage they need at a lower cost.
Nonstop recently decided it wanted to start taking advantage of machine learning (ML) technology to improve key parts of its business model. The company eventually connected with ClearScale, a Premier Tier Services partner with significant ML experience on the cloud and hired the team to help create a new ML algorithm with major implications.
One critical part of Nonstop’s work is estimating employer healthcare costs for the upcoming year. Of course, this is easier for existing clients, as Nonstop can look back on historical data and extrapolate forward from there. But estimating costs for new clients is harder, especially when there is little data available.
To overcome this challenge, Nonstop co-founder, David Slows, a mathematician by training, would run his own calculations to come up with an estimate. The problem was that this process was time-consuming and error prone. It introduced a bottleneck into the company’s business model. Consequently, Nonstop decided to look into automating the cost prediction process with a sophisticated ML algorithm.
The problem was that the internal team didn’t have a lot of experience building and training ML algorithms on the cloud. Nonstop wanted to hire a consultant that had proven ML expertise on Amazon Web Services (AWS), the company’s cloud platform. Fortunately, ClearScale had everything needed to help bring Nonstop’s vision to life, including the AWS Machine Learning Competency.
The ClearScale Solution
ClearScale’s knowledge of AWS’ ML capabilities and services was instrumental in the project’s success. The ClearScale team was able to hit the ground running and knew exactly how to best tackle Nonstop’s project by leveraging AWS services Amazon Forecast and Amazon Sagemaker.
Along the way, Nonstop’s CTO Darryl Young helped address any technical questions about the company’s technology, clearing the way for ClearScale to focus on training and testing the new algorithm. In addition, ClearScale trained Nonstop’s team on how to manage the algorithm. The client can now change parameters or adjust how the algorithm works without ClearScale’s guidance.
Throughout the entire project, ClearScale was available for questions and collaboration. The Nonstop and ClearScale project teams shared a Slack channel to keep an open line of communication. ClearScale’s project manager was also immensely valuable for keeping everyone on the same page.
While Nonstop hasn’t started using the new ML algorithm publicly, the company is pleased with how everything turned out. By automating the cost estimation process, Nonstop can deliver better projections to clients regarding how much they are likely to spend and save in a given year. This will improve customer experiences dramatically and allow employers to plan more effectively for employee healthcare needs.
On top of that, Nonstop’s sales cycle is now much faster. The company can deliver a crucial data point to prospective customers more quickly and accelerate revenue production. This is essential for any high-growth business.
Looking ahead, Nonstop is excited about the possibilities of its new ML capability. The company also knows it has a reliable partner in ClearScale that can plug in seamlessly on even the most technically ambitious cloud projects.