I spent years in consultative roles inside contact centers, and regardless of their size or industry, the same question kept coming up: how will using this solution benefit what we do, and what is it really going to cost compared to how we are getting things done?

I could answer the first part. The second part came down to estimating and averaging, and how far that landed from what actually happened wouldn’t surface until well after purchase. My job was to help customers solve problems, and I saw this lack of financial certainty as a problem I could solve.

I spotted two major obstacles. The first was methodology — every system measured costs from the top down while performance was measured from the bottom up. This meant the results could never meet at the point of decision, and yet they were being relied on as being equivalent. The second was data — in systems that didn’t communicate, using different file formats and inconsistent units of measure.

In 2015 I began building the methodology using data shared with me by contact center leaders I knew. The output was consistently positive — results built from the ground up were more useful than any averaged cost measure could tell them. That was my validation.

Not long after that I shared what I was doing with my brother Brian, a full-stack developer with deep experience building complex interactive systems. The challenge of building a software solution to handle those complexities was exactly the kind of problem he builds for, and he offered to take it on. The fact that we are brothers has made all the difference in how we work.

It took five more years of development before we earned our first client in 2020.

Since that time the level of operational complexity in contact centers has only grown, but the structural method of determining costs is unchanged — still top down. And increasingly, contact center leaders and their organizations are being held financially accountable for their operations, most relevantly for AI investments.