About
The belief behind the practice, the operator behind the work, and how an engagement runs.
00Belief
Finance and operations can run meaningfully better with a small, well-placed lift from data strategy and engineering.
At their core, accounting and data are the same discipline; both are about turning raw information into decisions.
01Practice
Sarantium exists to give SMBs and PE-backed companies the financial and operational clarity that was previously only available to large enterprises.
Most companies already have the raw material: the transactions, the systems, and the people who know how the work actually gets done. What they lack is a clear map of how it all flows together, and the infrastructure to surface it.
So we start not with code, but with people: sitting with the team that runs the close, watching how reporting actually happens, and finding where the hours go and where the knowledge lives.
From that map, we build on what you already own. Rather than replacing QuickBooks, NetSuite, Excel, or your CRM, we lay a shared data model beneath them, one spine every report and dashboard can draw from. The judgment stays with people; the engineering makes the picture clear.
The work starts in the office of the CFO, and the results flow outward: a business that makes faster, smoother decisions, built on connected systems, a shorter close, a clear view of what's driving it, and the busywork automated.
Engagements are led by Michael Cohen, with specialists brought in when the scope demands it. We don't staff projects with generalists to fill hours; if a client needs a specific data engineering capability or a domain expert, we find the right person for that outcome. Every engagement stays personal, accountable, and direct.
Michael came to this work through accounting. He studied it because it was the language of business, the clearest way to understand how a company actually works, and always intended to use it operationally, not as a reporting function.
He started in public accounting on the audit side, first at a midsize firm, then at Big 4, with time in PE finance along the way. The credential was valuable, but the pattern underneath it mattered more: the numbers existed and the people understood the business, yet almost none of it reached the decisions that depended on it.
When he left New York and stepped back, he took on work for friends and family: Excel cleanup, financial modeling, basic automation, and a lot of time spent understanding how each business actually ran.
Better analytics led to better questions, and better questions pushed past what spreadsheets could do. To answer them, he began teaching himself the methods and strategies that became data engineering, and how it interacted with finance. That intersection is where the practice was built.
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