I’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-p...Happy to answer technical/process questions in the thread. No DMs, no waitlists, just trying to make this genuinely useful for practitioners.
