MoneySense AI

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.

The Problem Nobody Was Solving

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.

What I Actually Built

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.

What I Learned Building 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.

Where It's Going

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.

The Honest Version

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.

New Hey IndieHackers!

I've been building a financial research tool called MoneySense, and I just shipped the feature I’ve wanted for myself for years: Automated SEC Filing Analysis.

The Problem I invest in stocks, but I’m lazy. I know I’m supposed to read the Annual Report (10-K) before buying, but they are 100+ pages of dense legal jargon. I usually just read the press release (which is just marketing) and ignore the filings.

This burned me recently when a stock I owned dropped 20% because of a "hidden" risk factor I would have seen if I had just read page 60 of the report.

The Solution I built a pipeline that pulls live filings from the SEC EDGAR database, cleans the text, and feeds specific sections (Risk Factors, MD&A) into an LLM with a strict system prompt to "act like a forensic accountant."

How it works

1. You type a ticker (e.g., NVDA).

2. It fetches the latest 10-K.

3. It generates an "Executive Summary" focused purely on Risks and Red Flags (ignoring the marketing fluff).

4. It extracts a sentiment score based on the management's tone.

WIP / Challenges The hardest part was token limits and "hallucinations." I had to build a chunking system to feed the document in parts so the AI wouldn't lose context or make up numbers. It’s about 95% accurate now compared to my manual reading.

Try it out It’s live here: https://moneysense.ai/sec-analysis

I’m looking for feedback specifically on the "Red Flag" detection. Does it feel too aggressive? Too passive?

Thanks for checking it out! 🚀

Hello everyone, I'm Mayuresh, developer of MoneSense AI.
The Problem:
As an investor/analyst myself, I was spending 3-4 hours daily reading earnings reports, SEC filings, and financial articles—then manually extracting key insights, sentiment, and risks into spreadsheets. It was tedious, inconsistent, and time-consuming.
The Solution:
MoneSense AI is a Chrome extension that analyzes any financial page with one click. It instantly delivers:
📊 Sentiment scores (bullish/bearish/neutral)
📈 Key tickers and mentioned companies
🎯 Market drivers and catalysts
⚠️ Risks and opportunities
📝 Concise TL;DR summaries
📤 Clean JSON output for dashboards/models

What Makes It Different:
Works on ANY website (Any financial websites like Bloomberg, SEC.gov, Substack, etc.)
Consistent, structured output every time
No passwords - secure magic link authentication
Free to start,

Who It's For:
Individual investors researching stocks
Financial analysts building models
Traders scanning market news
Anyone who reads financial content regularly

What's Next:
Portfolio tracking integration
Email digest of analyzed content
Team collaboration features
API access for power users

I'd love to hear your feedback! What features would make this more useful for your workflow?
Try it free: https://moneysense.ai