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/patents/products; tech (esp. AI) and regulation effects; winner-take-all vs oligopoly.
• Scenarios: company wins / coexistence / disrupted — conditions and probabilities.
• Global benchmarks: overseas analogs, path, valuation, post-IPO performance, and benchmark limitations.

Lens 4: Risk & Governance (risk-governance-analyst)

• Founder/CEO: background, foresight (3-year prediction accuracy), execution (promise delivery), values, controversies. ★1-5.
• Core team: backgrounds, 2-year talent flow (who left/joined, net), complementarity, culture signals (Glassdoor trends), key-person dependency.
• Equity & governance: founder control (dual-class / concerted action / VIE), dilution trend, employee equity; board, related-party, same-industry competition, majority/minority conflicts.
• Investor roster: lead-investor brand, strategic capital synergy, exit pressure (fund life / secondary sales / ratchet maturity), red flags.
• Risk matrix: regulatory / competition / tech / talent / funding / IPO / geopolitical / monetization / governance / compliance / macro / ESG — each probability × impact × severity × hedgeability × monitor.
• Exit paths: A/HK/US IPO, M&A, secondary, SPAC, stay-private — probability, window, valuation, preconditions, obstacles.
• Worst case (Munger inversion): 3 specific failure paths + probabilities; liquidation value; why smart money doesn't invest (≥5 reasons); failed analogs; the "thesis broken" signal.

Lens 5: Tech & IP (tech-ip-analyst)

• Tech stack: inferred from hiring/blog/open-source/talks; tech-debt signals (refactor hiring, stack switches, outage complaints).
• Patents (Google Patents/CNIPA/USPTO): total/pending/last-2y/field/citation/international; quality (core patents? aligned to business? litigation?); trend; vs competitors.
• R&D: investment (headcount/expense rate vs peers), output (papers/conferences/open-source/blog), efficiency (research-to-product, commercialization).
• Tech talent: core leaders' backgrounds, density (top-institution share), comp competitiveness, attrition signals, hiring direction (→ strategy).
• Tech moat (★1-5): algorithm/model, data, engineering, talent, ecosystem — each with durability (AI-era half-life may be short).
• AI/new-tech impact: is the company a beneficiary or a target of disruption?

Lens 6: Alternative-Data Signals (signal-miner)

Private companies have limited conventional info; alt-data often beats news.

• Hiring (LinkedIn/Boss/Indeed): scale/trend, structure (R&D/product/sales/data/international/compliance/IR — IR hiring = IPO signal; compliance = regulatory or IPO; JD tech stack = strategy).
• App/product (App Store/七麦/SimilarWeb): rank, rating trend, downloads, update frequency, complaint themes, web traffic.
• Social sentiment (Weibo/Zhihu/Xiaohongshu/X/Reddit): official engagement, organic discussion, KOL views, negative events, insider leaks.
• Business/legal (天眼查/企查查): registry/paid-in/equity changes/subsidiaries (new = new biz; deregistered = contraction)/scope changes; litigation/arbitration/penalties/enforcement.
• Supply chain: known suppliers (if listed, check their filings), procurement, partner evaluation.
• Digital footprint: registered domains (new = new biz), subdomains (api/pay → architecture), trademarks (new brands).
• Industry exposure: exec talks, awards, government/association interaction, media frequency/quality.
• Secondary-market signals (if any): SharesPost/EquityZen, implied valuation vs last round, employee selling.

Anomaly list (most important): things inconsistent with the company's narrative; inconsistent with industry norms; sudden changes (hiring freeze / executive departures); unexplained.

Cross-Validation (team-lead, mandatory)

Before synthesis, the team-lead must:

1. Data conflict arbitration: same metric across sources — list all, state which is adopted and why.
2. Signal consistency matrix: business-growth signal vs hiring trend? tech-leadership narrative vs patent/talent data? valuation level vs competitive position? management narrative vs action signals? (contradictions must be explained)
3. Information jigsaw: white zones (known) / gray (clues but uncertain) / black (unknown).
4. Bias check: is positive info detailed while negative is brief? Does every positive judgment have a reverse check?

Final Report Structure

1. One-line conclusion (50-100 words): what's it worth, why.
2. Company snapshot (with confidence column).
3. Six-lens scorecard (★1-5 + core judgment + confidence + completeness), overall score.
4. Key data jigsaw (only cross-validated, with source count + confidence).
5. Signal-consistency matrix.
6. Per-lens summary (3-5 top findings each).
7. Fair value assessment: business essence + 7-dimension moat card + 5-method valuation + fair value range (conservative/reasonable/optimistic + current market valuation + margin of safety %).
8. Investment thesis: bull 5-7 (with sources) vs bear 5-7 (with sources), which side is stronger.
9. Risk matrix (top 3 + mitigation).
10. Exit-path assessment.
11. Investment decision table (one-pager: core logic 3 sentences + value range + key assumptions & validation nodes + fatal risks & "thesis broken" signals + conclusion + expected return/timeline).
12. Information-gap map (dimension / known / missing / missing-impact / how-to-get): does the gap affect the core conclusion? If yes, state "under missing X, conclusion confidence is Y".
13. Tracking checklist (item / frequency / source / metric / alert threshold).
14. Summary paragraph (150-250 words).

Save via write_file to reports/{company}/{company}-private-{YYYYMMDD}.md. Run report_audit on the numbers as a quality gate.

Data Labeling Standard (strict)

• Every key data point: source (specific to media + article), time (year/month), confidence (🟢 prospectus/official / 🟡 credible media / 🔴 estimate/rumor).
• Conflicting data: list all + explain difference and adoption.
• Separate fact from inference: fact in normal text; inference in italics with derivation.
• Missing info: explicitly mark "data missing"; never fabricate.

Key Principles

1. 6 lenses in parallel (run_swarm, or sequential).
2. Transparent derivation — show the math and assumptions; don't hand-wave numbers.
3. Cross-validate — key data ≥2 sources; conflicts all listed.
4. Signal-consistency check — mandatory cross-lens check at synthesis.
5. Clear conclusion — don't dodge invest/watch/avoid; state confidence.
6. Search in both EN and CN — private-company info spans both.
7. Honest blanks — distinguish "sourced analysis" from "speculative fill"; "this dimension lacks data, no meaningful conclusion" is acceptable.
8. Alt-data is not noise — hiring/patents/litigation/app data may be closer to truth than news.
9. True-value focus — the goal is what the business is worth, not a pretty report. If info can't support a reliable valuation, say so.
10. Scarce data ≠ bad company — short AI output ≠ low certainty. Under extreme scarcity, switch to first-principles mode.