AI Berkshire Private Company Research Strategy: A Multi-Agent Deep-Dive Workflow

AI Berkshire treats private-company research as a parallel, multi-agent workflow that assembles fragmented data from low-confidence sources, explicitly marks confidence levels with color-coded indicators, and cross-validates every key datum before generating a structured investment report.

The xbtlin/ai-berkshire repository implements a specialized framework for analyzing unlisted companies where public financial disclosures are unavailable. Defined in skills/private-company-research.md, this strategy deploys six specialized agents simultaneously to reconstruct a private company's business model, financial health, competitive position, and risk factors while adhering to strict bias-mitigation protocols.

Core Philosophy and Bias Controls

The framework acknowledges that information scarcity is the default state for private companies. Rather than fabricating data to fill gaps, the system assembles fragmented evidence from multiple low-confidence sources and explicitly marks each piece of data with confidence levels: 🟢 high, 🟡 medium, or 🔴 low.

According to lines 15-30 of skills/private-company-research.md, the AI follows self-aware bias handling principles:

• Leave blanks rather than fabricate data when sources cannot be found
• Separate "verifiable facts" (plain text) from "reasoned inference" (italicized)
• Adopt a "first-principles mode" when data is extremely sparse, answering only four core questions: business problem, team advantage, upside-downside ceiling, and key validation nodes

The Six-Agent Team Structure

The research task is decomposed across six specialized roles defined at lines 45-53 of the skill file. Each agent operates with a specific analytical lens:

• team-lead: Orchestrates the entire workflow, cross-validates data conflicts, and assembles the final investment decision integration
• business-decoder: Analyzes business models and user dynamics through the lens "What is the core business?"
• financial-detective: Reconstructs financial pictures under data gaps using the approach "Reconstruct financial picture under data gaps"
• competitive-mapper: Maps industry landscapes and substitutes, asking "Who competes and how?"
• risk-governance-analyst: Evaluates governance, management, and risk spectrum with "What can go wrong?"
• tech-ip-analyst: Assesses technology stacks, patents, and R&D capability to determine "Are there real tech moats?"
• signal-miner: Harvests alternative data including recruiting trends, app metrics, and legal filings to find "Hidden signals beyond news"

Execution Workflow

The research process follows a rigorous seven-step execution pattern defined in the "第二步" (Step 2) through "第七步" (Step 7) sections of the skill file.

Team Creation and Task Generation

First, the system creates a research team via a TeamCreate call with parameters team_name: {company-name}-private-research and agent_type: team-lead. Then it generates six parallel TaskCreate calls, each corresponding to the specialized roles above.

Each task embeds a subject, description, and an activeForm that drives the agent's specific investigation. The tasks are enumerated from "任务1：商业模式与产品用户深度分析" through "任务6：替代数据信号挖掘" (lines 61-71).

Parallel Agent Execution

All six agents run concurrently (launched via a single message as described in the "第七步" block). Each agent receives a tailored prompt that reiterates the bias-aware research rules and requires:

• Multi-keyword web searches in both Chinese and English
• Full-article retrieval via WebFetch
• Cross-validation of every key datum with at least two independent sources

Cross-Validation and Signal Consistency

After the agents complete their individual research, the team-lead executes the synthesis phase detailed from lines 650-1060:

1. Resolves conflicting data points by listing all sources and rationalizing the chosen value
2. Checks consistency across dimensions (e.g., business growth claims versus hiring trends)
3. Flags "white/gray/black" zones indicating known, partially known, and unknown information

Final Report Assembly

The system writes a structured markdown report to reports/{company}/{company}-private-{YYYYMMDD}.md. This file contains:

• A one-sentence valuation summary
• Six-dimensional scorecards covering business model, finance, industry, risk, tech, and alternative signals
• Consolidated, cross-validated data tables with source attribution
• Investment theses (bull and bear cases) with exit-path analysis

Data Source Hierarchy and Confidence Levels

The framework prioritizes sources according to a strict hierarchy defined in the "数据来源矩阵" section (lines 83-98):

Priority	Source Type	Confidence
1	Prospectus / regulatory filings (招股书 / 监管文件)	🟢 High
2	Parent company / listed affiliate annual reports	🟢 High
3	Regulatory penalties / compliance disclosures	🟢 High
4	Bond / ABS issuance documents	🟢 High
5	Business registration data (天眼查、企查查)	🟡 Medium-high
6	Funding news and valuation reports	🟡 Medium
7	Third-party research reports	🟡 Medium
8	Premium media (36Kr, The Information, Bloomberg)	🟡 Medium
9	Industry data extrapolation	🔴 Low-medium
10	Former employee / insider leaks	🔴 Low

Implementation Examples

You can trigger the private-company research workflow via API calls or CLI commands that map directly to the TeamCreate and TaskCreate actions.

Python API Invocation

import requests
import json

# Step 1: Create the research team

team_payload = {
    "team_name": "antgroup-private-research",
    "agent_type": "team-lead"
}
team_resp = requests.post(
    "https://api.ai-berkshire.com/v1/team/create",
    json=team_payload,
    headers={"Authorization": "Bearer <YOUR_TOKEN>"}
)
team_id = team_resp.json()["team_id"]

# Step 2: Generate tasks for each agent

tasks = [
    {
        "team_id": team_id,
        "subject": "拆解Ant Group商业模式、产品矩阵与用户生态",
        "description": "Deep-dive analysis of business model and user ecosystem...",
        "activeForm": "分析Ant Group商业模式与用户生态"
    },
    # Additional tasks for financial-detective, competitive-mapper, etc.

]

for t in tasks:
    requests.post(
        "https://api.ai-berkshire.com/v1/task/create",
        json=t,
        headers={"Authorization": "Bearer <YOUR_TOKEN>"}
    )

CLI-Style Orchestration

# Create team

ai-berkshire team create \
  --name antgroup-private-research \
  --role team-lead

# Create tasks for each role

ai-berkshire task create \
  --team antgroup-private-research \
  --role business-decoder \
  --subject "拆解Ant Group商业模式、产品矩阵与用户生态" \
  --form "分析Ant Group商业模式与用户生态"

# Repeat for financial-detective, competitive-mapper, risk-governance-analyst,

# tech-ip-analyst, and signal-miner

Summary

• AI Berkshire implements a six-agent parallel research framework defined in skills/private-company-research.md for analyzing unlisted companies.
• The team-lead agent orchestrates business-decoder, financial-detective, competitive-mapper, risk-governance-analyst, tech-ip-analyst, and signal-miner roles.
• Every data point receives a confidence marker (🟢🟡🔴) and requires cross-validation from at least two sources before inclusion in the final report.
• The system outputs structured markdown reports to reports/{company}/{company}-private-{YYYYMMDD}.md containing six-dimensional scorecards and investment theses.
• Bias mitigation is enforced through explicit rules: mark sources, separate facts from inference, and leave blanks rather than fabricate data.

Frequently Asked Questions

How does AI Berkshire handle missing data when researching private companies?

The framework explicitly instructs agents to leave blanks rather than fabricate data when sources cannot be found. According to lines 15-30 of skills/private-company-research.md, the system adopts a "first-principles mode" for extremely sparse data, answering only four core questions about the business problem, team advantage, upside-downside ceiling, and key validation nodes.

What are the six specialized agents in the AI Berkshire research team?

The six agents are: team-lead (orchestration), business-decoder (business model analysis), financial-detective (financial reconstruction), competitive-mapper (industry landscape), risk-governance-analyst (risk assessment), and tech-ip-analyst (technology moats). A seventh role, signal-miner, harvests alternative data such as recruiting trends and app metrics.

Where does AI Berkshire store the final research reports on private companies?

Final reports are written to reports/{company}/{company}-private-{YYYYMMDD}.md within the repository structure. Each report includes a one-sentence valuation summary, six-dimensional scorecards, cross-validated data tables with source attribution, and bull/bear investment theses.

What confidence levels does AI Berkshire assign to different data sources?

The system uses a three-tier hierarchy: 🟢 High confidence for prospectuses, regulatory filings, and listed affiliate reports; 🟡 Medium confidence for business registrations, funding news, and third-party research; and 🔴 Low confidence for industry extrapolations and unverified insider leaks. This hierarchy is defined in the "数据来源矩阵" section at lines 83-98 of the skill file.

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