Research Framework for Unlisted Companies in AI Berkshire: A Multi-Agent Architecture

AI Berkshire provides a dedicated multi-Agent workflow for researching private (unlisted) companies, orchestrating six specialized agents through a team-lead coordinator to produce validated investment reports from fragmented data.

The xbtlin/ai-berkshire repository implements a rigorous research framework for unlisted companies within the skills/private-company-research.md skill. This framework addresses the fundamental challenge of analyzing private companies where public financial statements are unavailable and information is inherently sparse. It transforms fragmented alternative data into a disciplined, transparent investment research deliverable through structured parallel processing and strict validation protocols.

Core Architecture of the Private Company Research Framework

Team-Lead Coordination Layer

A team-lead Agent orchestrates the entire research process, stitching together findings from specialized agents and producing the final consolidated report. This central coordination layer handles cross-validation, arbitrates conflicts between data sources, and ensures the final output maintains methodological consistency across all research dimensions.

Six Specialized Research Agents

The framework deploys six concurrent agents, each targeting a specific research dimension as defined in skills/private-company-research.md:

• business-decoder: Analyzes business models, product matrices, and user ecosystems
• financial-detective: Handles financial data reconstruction and valuation modeling
• competitive-mapper: Maps industry-wide competitive landscapes and market positioning
• risk-governance-analyst: Profiles management quality, governance structures, and risk factors
• tech-ip-analyst: Evaluates technology stacks, patent portfolios, and R&D capabilities
• signal-miner: Extracts alternative data including recruitment metrics, app analytics, media sentiment, and legal filings

The Seven-Step Execution Workflow

The research framework for unlisted companies executes through a structured pipeline:

1. Team Overview: The lead presents the agent roster and confirms the target company with the user.

2. Team Creation: The system creates a named team (e.g., spaceX-private-research) to isolate the research context.

3. Task Definition: Six concrete tasks are generated, each with a subject, description, and activeForm driving the respective agent (e.g., "拆解SpaceX商业模式、产品矩阵与用户生态").

4. Parallel Agent Launch: All six agents launch concurrently within a single request, each receiving role-specific prompt templates.

5. Progress Reporting: The lead streams updates on agent completion (e.g., "✅ business-decoder completed – 12 key findings") and summarizes interim takeaways.

6. Cross-Validation & Information Stitching: The lead cross-checks data points across agents, arbitrates conflicts, and builds a unified "information puzzle" distinguishing high-confidence (🟢), medium-confidence (🟡), and low-confidence (🔴) data.

7. Final Consolidation: Outputs a structured report containing a one-sentence conclusion, company snapshot, six-dimensional scorecard, validated data table, signal-consistency matrix, valuation ranges, investment thesis (buy/hold/sell), risk matrix, exit-path analysis, and decision-summary page.

Data Source Hierarchy and Validation Standards

Three-Tier Confidence Classification

Every data point in the research framework requires explicit source attribution and confidence labeling:

• 🟢 Primary sources: IPO prospectuses, regulator filings, and parent-company financials
• 🟡 Secondary sources: Reputable outlets (36Kr, Bloomberg, The Information), analyst reports, and industry research
• 🔴 Tertiary signals: Employee reviews, patent databases, app store analytics, and domain footprints

Cross-Validation Protocols

When sources conflict, the framework lists all values and requires the team-lead to justify the chosen figure. The tools/financial_rigor.py module provides decimal-precision utilities for market-cap verification and multi-source cross-validation, automatically flagging tolerance breaches before data enters the final report.

Bias Mitigation and Financial Rigor

The framework enforces strict methodological discipline to prevent narrative bias and ensure accuracy:

• "Leave-Blank" Principle: Explicitly forbids fabricating numbers. If a dimension lacks sufficient data, the report must state "信息缺失" (data missing) without speculation or extrapolation.
• Inverse Thinking: Implements "芒格逆向检验" (Munger's inverse verification) and first-principles questioning to challenge over-optimistic assumptions and avoid confirmation bias.

Financial calculations and validation logic delegate to tools/financial_rigor.py, which guarantees decimal-precision and validates cross-source consistency before final report generation.

Initiating a Private Company Research Session

Execute the research workflow using the slash command:

/private-company-research SpaceX

This command automatically:

• Creates a team named spaceX-private-research
• Fires the six specialized agents in parallel
• Streams progress updates to the console
• Produces a final markdown report saved under reports/SpaceX/SpaceX-private-YYYYMMDD.md

Summary

• AI Berkshire's research framework for unlisted companies employs a multi-Agent architecture with six specialized agents (business-decoder, financial-detective, competitive-mapper, risk-governance-analyst, tech-ip-analyst, and signal-miner) coordinated by a team-lead.
• The seven-step workflow runs agents in parallel, then cross-validates findings to produce a unified report with explicit confidence indicators (🟢🟡🔴).
• Data hierarchy distinguishes primary, secondary, and tertiary sources, with strict validation via tools/financial_rigor.py to ensure decimal-precision and flag tolerance breaches.
• The "Leave-Blank" principle (信息缺失) prevents speculative fabrication when data is missing, while inverse-thinking checks mitigate cognitive bias.
• All functionality is defined in skills/private-company-research.md within the xbtlin/ai-berkshire repository.

Frequently Asked Questions

What makes AI Berkshire's approach to unlisted companies unique?

The framework uses a multi-Agent parallel processing system where six specialized agents simultaneously investigate different dimensions of a private company. Unlike single-query research, this architecture enables real-time cross-validation between agents, with a team-lead arbitrating conflicts and stitching together a unified "information puzzle" with explicit confidence labels for every data point.

How does the framework handle missing financial data?

The framework follows a strict "Leave-Blank" principle (信息缺失). When insufficient data exists for a specific metric, the system explicitly marks it as missing rather than extrapolating or fabricating values. This prevents over-optimistic valuations and ensures investors understand exactly where information gaps exist, maintaining analytical integrity.

What tools ensure accuracy in the final reports?

The tools/financial_rigor.py module provides decimal-precision utilities for market-cap verification and multi-source cross-validation. It automatically flags tolerance breaches and ensures that financial calculations meet rigorous standards before inclusion in the final report, guaranteeing that valuation ranges and financial reconstructions are mathematically sound.

Can the framework research any private company?

Yes, the methodology is designed to research any unlisted company by leveraging alternative data sources through the signal-miner agent. While the analytical framework remains consistent across targets, the depth of the final report depends on available primary and secondary sources, with the confidence scoring system (🟢🟡🔴) transparently indicating data reliability for each finding.

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