Free, open tools that apply the same rigour as our research, accessible to any investor. AI-powered analysis, quantitative scoring, systematic screening.
"AI-powered annual report analyser. Enter a US ticker or upload a PDF. Lens retrieves the 10-K from SEC EDGAR, enriches it with structured financial data from FMP, and returns red flags, green flags, key metrics, and an analyst verdict in under 60 seconds. Powered by Qwen3 32B running on Groq's LPU inference hardware."
"Quantitative company profiling across five dimensions: value, quality, growth, momentum, and safety. Scores are computed from live financial data and displayed as an animated radar chart with an AI-generated summary. Scoring models draw on Piotroski, Altman, and classic value investing frameworks. Same backend as Lens, zero external dependencies beyond the data APIs."
"Open-source Python screener for US small-cap growth stocks with quality guardrails. Filters on revenue momentum, margin quality, operating cash flow, and leverage, then ranks surviving names on a composite score weighted toward revenue growth and balance-sheet strength. Universe: US-listed, $300M to $5B market cap, excluding Financials and Utilities."
Over the years, I've spent a lot of time on investment platforms, forums, newsletters and communities. Great ideas everywhere: tickers, theses, one-liners that make you think. But I always had the same problem: when I find something interesting, I still have to do all the research myself. Every time.
I looked for research that was actually complete, a real deep-dive where you can follow the reasoning, check the numbers and disagree with the conclusion if you want. Rarely found it. Especially on small-caps, where institutional coverage is thin and mispricing opportunities are real. So I built it myself.
Read more about the process and the checklist →