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.