Launching today
CirclFi
13 AI valuation models on every US stock, updated daily
4 followers
13 AI valuation models on every US stock, updated daily
4 followers
Stop guessing. CirclFi runs every US stock through 13 institutional valuation models daily — Bayesian DCF, Monte Carlo, Markov Chains and more. Find undervalued stocks with our Quality of Company score.
Hey Product Hunt! 👋
I spent years inside BlackRock managing valuations on Aladdin — the 21 trillion dollar platform that Wall Street runs on. Every morning, I would open a terminal that costs 25,000 dollars a year and see exactly what institutional investors see: multi-model fair value estimates on every stock, updated daily.
Then I would go home and watch my friends make investment decisions based on Reddit posts and gut feelings.
That gap haunted me.
Here is the truth most people don't know: Wall Street doesn't use ONE valuation model. They don't just run a DCF and call it a day. They run 8, 10, 13 models simultaneously — Bayesian DCF, Monte Carlo simulations, Earnings Power Value, regime-switching models — and look at where the models agree and disagree. That consensus is the edge.
Retail investors? They get a single "analyst target price" and a green or red arrow. That's it.
So I built CirclFi.
13 independent valuation models running on every US stock, every day. The same multi-model approach that BlackRock, Bridgewater, and Two Sigma use — but for 0.90 cents a day instead of 25,000 dollars a year.
No predictions. No "buy" or "sell." Just math. 13 models tell you what a stock is worth — you decide what to do with it.
What is free: Every stock page shows 2 to 4 models unlocked, plus quality scores, value trap detection, and full investment analysis articles — all free, no signup.
What is in the terminal: All 13 models, 5,892 stocks, confidence scores, and the full consensus view.
I built this because I believe the tools should not be the moat. The thinking should be.
Would love your feedback — what would make this more useful for your investment research? 👇