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Quant (ex-NYU ML in Finance) built long-form AI equity primers
- firedup 1y agoI’m a quant who taught ML in finance at NYU for 4 years, and have spent ~6 months building an AI workflow that produces long-form equity primer reports. Not selling anything; I’m looking for blunt feedback from analysts who do equity research for a living. What it does (brief) Pulls public sources (filings, transcripts, ownership/insider, patent/R&D mentions, sentiment). Outputs a structured primer (business model, KPIs, financials/ratios, risks, comp set, pipeline/innovation, catalysts). All points are citation-backed to public links; no paywalled sell-side. Where I need your critique Biggest gaps vs. credible sell-side/independent work? Sections you’d cut/condense for real-world usability? Where would you not trust automation without a human pass? Preferred output format: single PDF/HTML vs modular notes? Sample (mods: if links aren’t allowed, I’ll remove): RIG (Transocean) primer (HTML): https://storage.googleapis.com/derek-snow-at-outlook-co-nz-public/sovai-agents-derek-snow-at-outlook-co-nz-positive-1a/tickercompany/RIG_Transocean-Ltd/final_report/RIG_Transocean-Ltd.html https://storage.googleapis.com/derek-snow-at-outlook-co-nz-p... Happy to answer technical/process questions in the thread. No DMs, no waitlists, just trying to make this genuinely useful for practitioners.