private-company-research

# 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.
- **Survivorship bias** — what's searchable online is mostly company-propagated good news.

**Counter**: prefer leaving blanks ("I don't know") over filling tables with speculation to fake certainty; label every data point with confidence (🟢high/🟡medium/🔴low); separate verifiable fact from inference; when information is extremely scarce, switch to "first-principles mode" and answer only: ① what real problem does this business solve? ② why this team? ③ ceiling if it succeeds / how it dies if it fails? ④ the key validation node at this stage?

> Invert the asymmetry: the market knows little about private companies → pricing is inefficient → that's exactly where alpha may live.

## Execution

Six lenses, best run **in parallel** (via `run_swarm`, one worker per lens; or sequentially via `web_search`):

| Role | Lens |
|------|------|
| business-decoder | Business model + product/user analysis: "what is this business, essentially" |
| financial-detective | Financial patchwork + valuation: "recover the true financial picture under missing data" |
| competitive-mapper | Industry + competition + substitution: "who competes, who could disrupt" |
| risk-governance-analyst | Risk全景 + management/governance/investors: "what could go wrong, who's at the helm" |
| tech-ip-analyst | Tech stack / patents / R&D / moat: "is the tech barrier real and durable" |
| signal-miner | Alternative data (hiring / patents / litigation / app / supply chain): "clues beyond the usual sources" |

You (team-lead) integrate, patch the picture, cross-validate, output the final report.

## Lens 1: Business Model & Users (business-decoder)

- **Core business definition**: one sentence (Duan Yongping style: plain language to a smart layperson). What problem? For whom? If the company didn't exist, what would users do? Is demand rigid (cut in a downturn)?
- **Revenue model**: ads/commission/subscription/take-rate/financial/SaaS/hardware; mix and trend; monetization efficiency (ARPU / take rate / conversion); recurring vs one-off; concentration; predictability.
- **Unit economics**: CAC (paid vs organic, by channel, trend), LTV, LTV/CAC, payback, marginal cost, scale-inflection point.
- **Product matrix & flywheel**: core + extension + incubation; network/data/scale flywheel; iteration speed.
- **Users**: MAU/DAU (from QuestMobile/Sensor Tower/SimilarWeb), S-curve stage, stickiness (DAU/MAU, retention), profile, reputation (App Store trend, social sentiment).
- **Moat (6 dimensions, ★1-5)**: network effects, switching costs, brand mind, data barrier, regulatory license, scale economies — each with evidence + trend (widening/stable/narrowing) + durability. Overall: wide/narrow/none.

## Lens 2: Financial Detective

No standard financials; multi-source patchwork + cross-validation. **Every data point: source, time, confidence, derivation.**

**Source priority**: 🟢 prospectus/regulatory filings, parent-company annual report disclosure, regulatory penalties, bond/ABS offering documents → 🟡 business registry, funding news, third-party reports, deep media (LatePost/The Information/36Kr/Bloomberg) → 🔴 industry extrapolation, ex-employee leaks.

**Key metrics**: revenue (scale/growth/mix/quantity×price), cost (gross margin/R&D/sales/G&A rates, vs peers), profit (EBITDA/net income/profitability timeline), cash flow (operating/burn rate/runway), efficiency (per-capita revenue, capital efficiency).

**Cross-validation**: list every source for the same metric; check convergence across methods; flag single-source ("isolated evidence") data.

**Funding history**: full timeline (round/amount/valuation/lead investor); health of the curve, interval, down-rounds, whether existing investors keep participating; latest-round terms (liquidation preference / anti-dilution / ratchet) and their effect on common-share value.

**Valuation (multi-method)**: ① last-round (adjust for liquidation prefs, 20-40% discount); ② comparable public comps (3-5, PS/PE/EV-EBITDA, liquidity discount 20-30%); ③ DCF scenarios (bear/base/bull, each assumption grounded); ④ terminal-value rollback (5/10y terminal state → implied IRR); ⑤ transaction comps (recent M&A/funding multiples).

**Valuation synthesis**: do the methods converge? If divergent, explain. Distinguish "fair value" and "conservative (margin-of-safety) value".

## Lens 3: Competitive Landscape (competitive-mapper)

- **Market**: TAM/SAM/SOM, penetration, stage (emergence/growth/mature/decline), growth drivers.
- **Value chain** (text map): upstream → company's link (profit pool share) → downstream; bargaining power; structural shifts.
- **Porter's five forces** (★1-5): rivalry, new entrants, substitutes, supplier power, buyer power.
- **Competitor scan**: direct/indirect/substitute/potential entrants (giants) — share, funding, strengths, weaknesses, threat level. Multi-dimensional compare with 2-3 closest competitors.
- **Dynamics**: last-12-month changes; infer competitor strategy from hiring/paten