Case in Point #1 – Plugging a $930,000 Leak

Introducing Case in Point

Proximo has been doing this work for nearly 30 years, building data systems, analytics, and now AI solutions for organizations in pharma and life sciences, associations, financial services, consumer goods, and more. Most of that work has never been discussed publicly. Case in Point is a new recurring series that changes that. We'll share projects from across our history, some from many years ago and some from this year, with client names kept confidential. We're including older work on purpose. The technology changes quickly, but the business problems underneath it don't, and some of the most useful lessons come from projects that predate today's AI conversation.

First up: plugging a $930,000 leak.

A global beauty company employed more than 8,000 beauty consultants across more than 2,000 retail cosmetics counters. The company and each retailer split the consultants' wages under complex terms, and retailers billed the company for its share. The company's record-keeping system couldn't reflect those terms, so it had no reliable way to check whether a bill was correct. In the year before the new system went in, retailers overbilled the company by $930,000.

We started with the agreement itself. Our team mapped exactly how the wage split worked, how a new system would need to integrate with existing databases, and where it had to stay flexible as the arrangements changed. We then designed and built a system that let the company verify retailer billing instantly and look for discrepancies by store, region, manager, or nationwide. After it went live, overpayment dropped to nearly zero.

The project involved no AI, and had AI been an option, it would not have solved the problem. The value came from "old fashioned" data governance, and aligning definitions and data so that expenses could be match the originating contract, allowing the company to finally answer a question it should always have been able to answer. In our experience, this is still where organizations most often leave money on the table, and it's the first place we'd tell any leader to look before investing in something more advanced.

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