private-company-research

name	private-company-research
category	analysis
description	Deep research framework for pre-IPO / private companies (Ant Group, SpaceX, Stripe, ByteDance...). Six analyst lenses — business model, financial forensics, competitive landscape, risk & governance, tech & IP, alternative-data signals — run in parallel via run_swarm, then cross-validated for signal consistency before any verdict. Built around the core challenge of private-company work: information is scarce, so every data point carries a confidence label (high / medium / low), inference is shown separately from fact, and 'I don't know' is a valid output. Outputs a fair-value range, exit-path analysis, and an information-gap map. Use for any unlisted company where you need to judge what the business is actually worth.

Private-Company Research: Multi-Lens Deep Framework

Deep research on an unlisted company (e.g. Ant Group, ByteDance, SpaceX, Stripe).

Ultimate goal: under information scarcity, recover the company's true value — not the market valuation, but what the business is actually worth.

Framework Characteristics

Private vs public research: no standardized financials (multi-source patchwork + cross-validation); few valuation anchors (funding rounds, comparables, scenarios); large information asymmetry ("jigsaw" research); uncertain exit path (IPO / M&A / secondary).

AI Research Bias Self-Check (core premise)

Private companies are where AI bias is worst. Watch for:

• False conservatism — with little data, AI gives conservative/vague conclusions, but scarce data ≠ bad company.
• False precision — to fill the template, AI disguises "reasonable guess" as "sourced analysis".
• Comparables trap — forcing a public-comp overlay inherits public-market logic and misses private-specific value.