I used to spend Sunday evenings doing something most people would find deeply boring: reading SEC filings.
10-Ks. 10-Qs. Form 4s. Proxy statements. Hundreds of pages of dense, legal-adjacent language that most investors never open. I did it because I believed — and still believe — that the edge in public market investing isn't information asymmetry anymore. Everyone has access to the same news. The edge is comprehension asymmetry. Who actually reads the primary source?
The problem was that reading a single 10-K properly takes three to four hours. I'm a founder. I don't have three to four hours for one filing on one company. So I started to think about whether AI could do the reading for me — and keep the insight.
That question became MoneySense AI.
When I looked at what existed in the retail investing tool space, I found two categories of products.
The first category is data aggregators — platforms that pull financial metrics (P/E, revenue growth, margins) from filings and surface them in dashboards. These are useful, but they're telling you what the numbers are, not what the numbers mean or what changed since the last filing.
The second category is AI-powered "stock analysis" tools that are essentially wrappers around a language model with a prompt that says something like "analyze this stock." The output tends to be generic, confident-sounding, and not grounded in actual primary source documents.
Neither category was doing what I actually wanted: reading the real filing, identifying what changed, and surfacing the things that matter and that most people will miss.
That gap felt like a real problem. SEC filings are the most information-dense, legally accountable documents public companies produce. They're free. They're updated every quarter. And almost nobody reads them — not because they're unimportant, but because they're hard and time-consuming to parse.
The opportunity was to make the comprehension accessible without stripping out the nuance.
MoneySense AI lets you analyze any SEC filing — 10-K, 10-Q, Form 4, and more — and get plain-language insights in under a minute.
But the design decisions mattered more than the core concept.
The first decision: ground everything in the actual document. I'd seen too many AI tools hallucinate financial data or blend a company's actual filings with training data in ways that were hard to detect. Every insight MoneySense AI surfaces is traced back to the specific filing and section it came from. If you want to verify it, you can go straight to the source.
The second decision: focus on change, not just state. The most valuable signal in a 10-Q isn't the revenue number — it's whether the risk factors section added anything new, whether the language around guidance became more hedged, whether an accounting policy quietly shifted. MoneySense AI is built around comparison and delta, not just point-in-time summaries.
The third decision: don't dumb it down. The target user is an investor who wants to understand a company properly — not someone who wants a buy/sell recommendation. The output is written to inform judgment, not replace it.
The hardest part wasn't the AI. Getting the language model to read and summarize filings accurately was actually the most tractable part of the problem, especially with modern models. The hardest part was document parsing — SEC filings come in HTML, XBRL, and PDF formats with wildly inconsistent structure across companies and time periods. Building a pipeline that could handle the full range of real-world EDGAR documents without breaking was where most of the early engineering time went.
The ICP took time to find. My initial assumption was that the core user would be active retail traders. It turned out the highest-engagement users were a different profile: people who hold 5–15 positions, do their own research, and genuinely want to understand what they own. They're not trading on every filing — they're using the tool as part of a due diligence workflow. That insight changed how I wrote the product copy and how I thought about distribution.
Content is the distribution strategy. SEC filing analysis is a naturally shareable output. When you find something interesting in a filing, you want to tell someone. I started sharing filing insights on Twitter and LinkedIn — not as ads for the product, but as genuine analysis — and that became the primary growth channel. The people who found the analysis useful went looking for the tool that produced it. That flywheel is still the most efficient thing we've found.
The long-term vision is a financial research layer that makes institutional-quality document analysis available to any investor, regardless of whether they have a Bloomberg terminal or a research team.
Right now that means SEC filings. But the same comprehension problem exists across earnings call transcripts, investor presentations, bond indentures, and regulatory filings in markets outside the US. The surface area is large.
The near-term focus is on making the product faster, expanding the comparison features (filing-over-filing change tracking, side-by-side company comparisons), and building out the insider trading analysis — Form 4 data is one of the most underused public datasets in retail investing.
I built MoneySense AI because I was the target user and the tool I wanted didn't exist. That's the most Indie Hackers sentence I can write, and it also happens to be true.
The bet is that comprehension asymmetry is durable — that the investors who understand what they own at the primary source level will consistently outperform those who don't, and that most people will always prefer a shortcut to three hours of dense legal reading.
If that bet is right, there's a large and largely unserved market sitting in EDGAR's public database, updated every quarter, for free.
We're just learning to read it.
MoneySense AI is available at moneysense.ai. If you're an investor who wants to go deeper on any public company's filings, try it free.