Anish Moonka's AI-driven equity research system orchestrates 26 agents across six…

Anish Moonka's AI-driven equity research system orchestrates 26 agents across six phases to automate deep, structured investment analysis. The pipeline begins with four agents collecting SEC filings, transcripts, market and insider data and producing 600–800-word compressed briefings; six agents then perform granular financial scoring (1–5) including SBC-adjusted FCF and GAAP vs non‑GAAP reconciliations. Four agents evaluate moats (0–3 across five dimensions) with an AI-disruption overlay while five forward-looking agents model per-stream revenues, peer rankings, competitor scorecards, and quarterly bull/base/bear scenarios through 2027. Valuation blends a three-scenario DCF and comps into a weighted convergence (DCF 40% / comps 30% / historical 10% / revenue evolution 20%), and three agents synthesize a five-part investable report. First full test was on Amazon; US stocks are live and India is next. The author cautions the current build’s price recommendations need more work but touts the system’s depth for forming informed theses.