The Money No One Was Watching

Early in my career, I came upon the discovery that a product I helped build was giving away free money. No one else seemed to be paying attention.

A subtle edge case let a customer pay far less than the product was worth.

It took me a while to realize no one owned catching it. No alert fired. No business review surfaced it. Had I not raised it, it would have gone completely unnoticed. What’s more: I was an engineer, not someone in finance or product.

To catch anyone's attention, I had to build a Looker dashboard with "Free Money" in big, bold letters in the title. That finally got attention to reconcile this issue with the customer. However, building that dashboard required crafting a lot of SQL queries, asking other teams a lot of questions, setting up dbt pipelines, securing a lot of approvals for data replication, and spending countless hours fighting Looker quirks.

When Every Question Becomes a Research Project

At the time, I assumed this blindness to short-term financial performance was unique to companies with complex funds flows. Years later, after months of talking to hundreds of finance teams, I learned that most companies actually have the same problem: investigating the profitability of a single new product line is a multi-week research project. Even simple questions such as:

Why did more cash go out the door last month than usual?

Which customers are costing us the most?

….took days of digging. Many of the companies are hemorrhaging money because of it, with no engineer around to write the queries that would have caught it in time.

To me, finance teams are still living in a pre-Google search era. They answer questions the way we did before Google: go to the library, walk the aisles to find the right books, read to find the necessary facts, and synthesize the facts to answer a question.

This may sound like an exaggeration, but it’s a reality that I’ve lived, and what I’ve heard firsthand from hundreds of finance teams. Building a single “report” requires multiple teams — Finance, IT, Data Platform, Business Systems, and Engineering. Their work and coordination add up to weeks of manual labor.

The most important questions become the most expensive to ask. Companies ask fewer, move slower, and miss opportunities worth millions, sometimes hundreds of millions, in revenue.

A Search Bar is Only The Beginning

Quanta is building a Google equivalent for finance: a single search bar where those questions get answered in seconds instead of weeks.

The idea isn’t new. What’s been missing is the foundation to make it work.

In fact: there's a graveyard of companies that tried this before, many started by second-time founders or seasoned finance professionals who had seen firsthand how much the problem matters. They went after solving FP&A (financial planning & analysis) with better interfaces and dashboards that make answers easier to visualize. In the current AI wave, companies are slapping an LLM on top of existing tools and data warehouses.

Here lies the problem: financial systems don’t talk to each other. It’s scattered across systems, spreadsheets, and documents, each capturing only part of the business. The real challenge is connecting those pieces into a coherent picture. Once you understand how the data fits together, answering questions about the business becomes dramatically simpler.

Start Where the Data is Created

So we do the hard work of producing our customers' most foundational finance data ourselves: their accounting, their general ledger, their financials. With accounting logins, we see context that no API connector can reach. With LLMs, we can read documents and make sense of messy, unstructured data. With generative AI, we can generate queries, logic, and proofs on top of data we already understand.

For the first time, this trillion-dollar problem is solvable. We don’t just sit on top of financial data. We help create it, reconcile it, and understand it.

From Weeks of Research to Everyday Answers

Companies live and die by the answers to the following questions:

• Should we double down on this product?

• Is this customer segment actually profitable?

• Is our pricing working?

Today, each one is a bespoke research project. Tomorrow, it should feel as natural as typing into Google search.

The companies that get there first won't just save analyst hours. They'll run more experiments, make better decisions, and compound those advantages over time. In the short term, that gives companies an enormous advantage because they make better decisions much faster. In the long term, we think it fundamentally changes how companies operate, by eliminating huge amounts of wasted time, effort, and bad decisions that come from making decisions in the dark.