I started investing in 2020, in the middle of the pandemic, like a lot of people did. I bought companies that felt right. Some worked out. Some didn't.
What bothered me wasn't the losses. It was that I couldn't really explain either outcome. The wins felt as accidental as the mistakes.
Two questions I couldn't answer
What is this business actually worth? And what should I pay for it?
Simple questions. Getting to an answer was not.
The financial data I needed was either locked behind an expensive subscription or dumped on me in bulk — hundreds of metrics, no guidance about which ones mattered or what any of it meant for the decision in front of me. Every platform I tried handed me more information and left me with less clarity.
So I ended up where a lot of people end up: in a spreadsheet at midnight, building a valuation model I wasn't confident in, about a company I hadn't finished understanding. Then doing it again for the next company. And the next.
More data didn't make me a better investor. It just made me busier.
Having access to information is critical.
The frustrating part was that the thinking had already been figured out. Buffett, Munger, and Graham laid out the principles decades ago: buy businesses you understand, look for a durable competitive advantage, back honest management, and never pay more than the business is worth — with room to be wrong.
What existed was either built for institutions and priced that way, stuck in a desktop era that assumed you had an hour and two monitors, or focused entirely on the numbers while ignoring the first question any good investor asks: do I actually understand this business?
Does this business fit what you actually understand and care about?
Can competitors erode this advantage, or does it hold?
Are the people running it honest and good with capital?
Is the price far enough below value to protect you?
So I built it
I've spent close to two decades building enterprise platforms, a good stretch of it in financial systems — payment and settlement modernization at an investment bank, a global financial data pipeline at a large tech company, platform work across insurance and healthcare. I'm an engineer by training: Cornell, Mechanical and Aerospace.
Which is to say: I've spent my career making messy financial data reliable at scale.
Moatly is the tool I wanted for myself. All four questions answered in one place, on a phone. The financial data pulled and analyzed already, so the work you do is judgment instead of data entry. A buy price calculated with a margin of safety built in, so you know your number before emotion gets a vote. And an AI mentor that will answer a question at 11pm without charging five figures for the privilege.
Who it's for
Value investors, first. People who already think in terms of moats and intrinsic value and would rather spend their time on judgment than arithmetic.
But just as much for who I was in 2020 — curious, motivated, and drowning in data with no framework to organize it. You shouldn't need a finance degree or an expensive coaching program to invest thoughtfully in businesses you understand.
That's the whole idea. Less time gathering numbers. More time on the thinking that actually decides the outcome.