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private-company-research

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.

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# 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:
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