A PE-backed services business was catching margin and quality problems weeks too late. We are unifying its operating data and putting predictive models on top, so issues surface before they land, not after.
A PE-backed, multi-region services business · sponsor-owned · heavy weekly job volume
This client asked to remain anonymous.
Problems showed up in the monthly review, not while there was still time to fix them. A margin slip or a billing error surfaced weeks after close, when it was already a customer dispute and a lender question. Staffing was set by last period's volume, not the next one's demand. And the data that would have flagged any of it lived in separate systems that never reconciled, so reported margins were always understated.
We reconciled the operating, billing and accounting systems into one structured foundation, then layered predictive models on top: demand and staffing forecasts, margin and cost-driver models, and risk scoring that decides which jobs to check. The team opens one dashboard that shows what is about to go wrong, while there is still time to act.
The shift is from forensic to preventive. The monthly review still happens, but it stops being the place where bad news is discovered. Margin leaks, billing errors and staffing gaps show up as alerts days or weeks earlier, small enough to fix quietly instead of large enough to explain to a lender.
“We used to find out about a problem in the monthly review. Now it pings us while there is still time to fix it.”
We connect your stack, keep your AI assistant, and have your team asking real questions in under two weeks.