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ValueAgent
AI agents read the 10-K so you don't have to
Type a US ticker. 30 minutes later, get a 12,000-word value-investing thesis written by 6 AI agents — trained on Buffett, Klarman, Hohn and Chanos. What's different: - Six specialist agents, not one generalist LLM - Primary sources only — SEC EDGAR direct, no paid feeds - Every claim audited against the source with a trust scoreboard - Four named kill criteria — built to fail loudly, not quietly First thesis free. No advice. Just the work.
Hey Product Hunt 👋
I'm Vadim, founder of ValueAgent.
This started with a frustration. Every great value investor — Buffett, Klarman, Hohn, Pabrai, Greenblatt — treats deep reading as the actual job. Buffett reads 500 pages a day. The reading IS the investing.
For most of us with a day job and a family, that's not realistic. A 10-K is 200+ pages. Add three years of proxies, four earnings calls, the competitor's K — that's a week of focused reading just to form a view on one stock. The gap between the average investor and Buffett isn't intelligence. It's hours.
So I built ValueAgent.
Type a US ticker. 30 minutes later you get a 12,000-word thesis that triangulates intrinsic value three ways, applies Buffett's moat lens, Klarman's margin of safety, Hohn's kill criteria, and Chanos' forensic eye.
The architecture is what I'm proudest of — six specialist agents, each owning one question, instead of one generalist model trying to do everything. The output is one coherent thesis where every claim has a named author and a citation underneath.
A few things I'd genuinely love feedback on:
1. The sample thesis on PLTR (value-agents.com/sample/PLTR) — is the depth right? Where would you cut?
2. Pricing — $79/mo for 20 fresh theses feels right for a self-directed watchlist, but curious how PH folks read it
3. The kill-criteria-as-watchlist concept — useful signal or noise?
First thesis is free, no card. Try any US ticker.
Not investment advice. Just the work.
Happy to answer anything 🙏
About ValueAgent on Product Hunt
“AI agents read the 10-K so you don't have to”
ValueAgent was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #156 on the daily leaderboard. Type a US ticker. 30 minutes later, get a 12,000-word value-investing thesis written by 6 AI agents — trained on Buffett, Klarman, Hohn and Chanos. What's different: - Six specialist agents, not one generalist LLM - Primary sources only — SEC EDGAR direct, no paid feeds - Every claim audited against the source with a trust scoreboard - Four named kill criteria — built to fail loudly, not quietly First thesis free. No advice. Just the work.
On the analytics side, ValueAgent competes within Fintech, Investing and Artificial Intelligence — topics that collectively have 544.7k followers on Product Hunt. The dashboard above tracks how ValueAgent performed against the three products that launched closest to it on the same day.
Who hunted ValueAgent?
ValueAgent was hunted by Vadim Kouznetsov. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of ValueAgent including community comment highlights and product details, visit the product overview.
Vadim Kouznetsov