Companies Are Great at Explaining What Happened. That's the Problem.

Every business is a capital-allocation system, deciding where to put the next dollar, hire, unit of inventory, capacity, and attention. Those choices compound into growth, margin, and cash, or into waste.

Yet the information behind those choices is fragmented. Definitions conflict. Business logic lives in someone’s head. Leaders see that a number moved, but not why or what to do about it. By the time the answer arrives, the moment to act has passed. One better decision about pricing, inventory, hiring, or working capital can outweigh thousands of hours saved.

The problem I kept seeing

Early in my career, I worked with distressed and capital-intensive businesses including Hertz, American Airlines, and Carnival Cruise Line. They moved billions of dollars through fleets, aircraft, ships, inventory, and infrastructure, often on margins so thin that a single point of improvement could transform cash generation. Yet the data needed to act was locked in legacy systems and a few people’s heads. Leaders diagnosed the same problems meeting after meeting, able to see what was wrong but rarely equipped to change it.

At Plaid, I saw the same problem from the opposite position. We raised more than $1 billion, pursued acquisitions, reorganized, and repeatedly reset our plan through the fintech downturn. I worked on the decisions behind those moves: where to invest, where to pull back, and how to improve efficiency without sacrificing the future.

We had no shortage of data. Questions about pipeline, headcount, or product investment required several teams and systems. While the analysis was assembled, the decision waited. We were explaining what had happened when we needed to decide what to do next.

Why Sapien

Living this problem from both sides pushed me to look for the answer. I founded the Strategic Finance Network in New York for finance leaders and operators, and through it met many VC-backed companies building for the office of the CFO. Most offered a better interface on a financial model.

Then Ron Nachum and Sapien’s co-founders offered to host a dinner for the group. Ron was the first founder I had met whose worldview matched what I had seen from the inside: AI would transform the office of the CFO not through a prettier interface, but by solving the data problem beneath it. What looks like a finance problem is really a company-understanding problem. Finance sits where data, operating decisions, and capital converge. Sapien was building the system I had wished existed throughout my career. I joined to help build it.

From reporting to operating

Sapien connects to a company’s ERP and operational data and learns how that business works: its definitions, cost structure, product and customer hierarchies, and the logic that lives in a controller’s head. That context turns a report into an answer. A report shows that margin declined. A company-aware system explains what products, customers, or SKUs drove the change and where to look next.

Trust is the foundation. When a decision touches the P&L, leaders need to inspect the sources, assumptions, definitions, and logic behind every answer. The goal is not to replace human judgment, but to give people a stronger basis for exercising it and move teams from reporting on the business to operating it.

What better decisions are worth

The impact is already visible. Carlex corrected a $12 million EBITDA attribution error, then identified a roughly $1.5 million annualized profit opportunity in a 20-minute analysis. Odeko measured $5 - $6 million in annualized bottom-line gains from a sequence of pricing changes directed by Sapien. Cooper Standard analyzed all relevant program-cost data, uncovering more than $2 million in initial inventory savings. Blink Charging turned a group of seasonal, loss-making chargers profitable within weeks, with roughly 40% margin uplift.

The use cases differ: pricing, inventory, asset profitability, and performance attribution. But the pattern is the same. The value is not faster analysis for its own sake. It is connecting terabytes of data across systems to surface better decisions and act while the opportunity is still on the table.

The company we are building

At Sapien, we are building toward the self-optimizing company: not a business that hands its judgment to software, but one that continuously understands what is changing, surfaces the decisions that matter, learns from every action its people take, and gets measurably better at running itself.

The defining question for enterprise AI is no longer how much content it can generate or how many hours it can save. It is how much better it can help a business run. That is the future we are building at Sapien.

If you run a business where a point of margin matters, we would like to show you what Sapien finds in your data.