How AI Berkshire Performs Industry Research from an Investment Theme: A 10-Stage Systematic Pipeline

AI Berkshire performs industry research from an investment theme by executing a reproducible 10-stage pipeline that blends the investment philosophies of Warren Buffett, Charlie Munger, Duan Yongping, and Li Lu with automated data validation through financial_rigor.py and report_audit.py.

AI Berkshire, an open-source investment research framework hosted at xbtlin/ai-berkshire, encodes systematic value investing principles into an auditable workflow. The methodology to perform industry research from an investment theme is fully documented in skills/investment-research.md and executed through specialized validation tools in the tools/ directory.

The 10-Stage Investment Research Pipeline

The research process follows a rigorously-defined sequence that begins with bias acknowledgment and ends with automated quality assurance.

Stage 1: Bias Self-Awareness (Pre-Step)

Before data collection begins, the system rates the target company's information richness (A/B/C classification) and records a confidence checklist. This forces the model to identify data gaps and recognize whether conclusions stem from verified facts or inference chains.

Stage 2: Data Collection via Task Agent

The internal Task agent issues structured requests to gather revenue breakdowns, financial metrics, competitive positioning, management details, industry outlook, and risk factors. Data sources include Macrotrends, StockAnalysis, AASTocks, and 东方财富, as specified in skills/financial-data.md.

Stage 3: Cross-Verification with financial_rigor.py

Every key numeric field (share count, market cap, income, cash) is processed by tools/financial_rigor.py. This script performs multi-source consistency checks, precise decimal arithmetic (avoiding floating-point rounding errors), and generates a verification log appended to the final report.

Stage 4: Business-Model Analysis

Guided by Duan Yongping's "right business" framework, the model produces a concise analysis of the company's core value proposition, revenue streams, profitability trends, and ecosystem lock-in mechanisms.

Stage 5: Moat Evaluation

Buffett's five-moat framework assesses branding, switching costs, network effects, scale, and tech patents. Each moat claim is validated against the data collected in Stage 2.

Stage 6: Reverse-Thinking & Risk Listing

Applying Munger's "invert-the-problem" approach, the system generates a failure-path matrix, identifies historical analogues, and flags cognitive biases including anchoring, survivorship, and narrative traps.

Stage 7: Management Assessment

Leadership evaluation covers capital allocation decisions, ownership alignment, and cultural factors, combining Duan Yongping's "right people" criteria with Buffett's integrity lens.

Stage 8: Industry & Civilization Trend Analysis

Li Lu's "civilization-evolution" view prompts analysis of paradigm shifts (e.g., AI, renewable energy) and forecasts Total Addressable Market (TAM) growth trajectories.

Stage 9: Valuation & Safety Margin

Using financial_rigor.py, the system runs three-scenario DCF/PE calculations (optimistic, base-case, bearish) and compares outcomes against historical and peer valuations to derive a margin of safety.

Stage 10: Decision Memo & Audit

A final decision matrix (buy/hold/sell) is compiled, followed by execution of tools/report_audit.py, which randomly samples 15% of data points, re-validates them, and blocks publication unless all deviations are ≤ 1%.

Automated Validation Tools in Practice

The tools/ directory contains executable scripts that enforce numerical rigor throughout the pipeline.

Verifying Market Capitalization

Use financial_rigor.py to verify that price × shares equals the reported market cap:

python3 tools/financial_rigor.py verify-market-cap \
    --price 42.37 \
    --shares 1500000000 \
    --reported 63400000000 \
    --currency USD

Cross-Validating Financial Metrics

Cross-check net profit figures from multiple independent sources:

python3 tools/financial_rigor.py cross-validate \
    --field net_profit \
    --values '{"macrotrends": 5.23e9, "stockanalysis": 5.20e9}' \
    --unit USD

Running Three-Scenario Valuations

Calculate valuation ranges across optimistic, base, and bearish cases:

python3 tools/financial_rigor.py three-scenario \
    --price 42.37 \
    --eps 3.12 \
    --shares 1.5e9 \
    --growth 0.12 0.08 0.03 \
    --pe 15 20 30 \
    --years 3 \
    --currency USD

Auditing Final Reports

Before publication, extract audit samples and verify them:

# Extract 15% random sample from the report

python3 tools/report_audit.py extract \
    --report reports/Alibaba/Alibaba-Research-20260707.md > audit_samples.json

# After filling in fetched values, run the verdict

python3 tools/report_audit.py verdict \
    --results audit_samples_filled.json \
    --report reports/Alibaba/Alibaba-Research-20260707.md

Key Source Files and Architecture

The pipeline is orchestrated through the skills/ framework, ensuring every stage is reproducible and traceable to its data sources.

Component	Path	Purpose
Research workflow definition	skills/investment-research.md	Step-by-step guide for the AI agent
Financial validation	tools/financial_rigor.py	Multi-source cross-check and precise arithmetic
Report auditing	tools/report_audit.py	Random-sample verification and quality gate
Data source conventions	skills/financial-data.md	Lists primary and secondary data providers per market
Sample output	reports/Alibaba/Alibaba-Research-20260707.md	Reference implementation of the final report format

Summary

• AI Berkshire performs industry research from an investment theme through a 10-stage pipeline combining legendary investor frameworks with automated validation.
• tools/financial_rigor.py enforces numerical precision via multi-source cross-verification and decimal arithmetic.
• tools/report_audit.py provides a 15% random-sample quality gate that blocks publication if deviations exceed 1%.
• The methodology is defined in skills/investment-research.md and supports reproducible, auditable research workflows.

Frequently Asked Questions

How does AI Berkshire validate financial data accuracy?

AI Berkshire validates financial data through tools/financial_rigor.py, which performs multi-source consistency checks using precise decimal arithmetic to avoid floating-point errors. Every key numeric field (market cap, income, cash) generates a verification log that is appended to the final report's appendix.

What investor frameworks guide the AI Berkshire research process?

The system integrates four legendary investor frameworks: Warren Buffett's moat and integrity criteria, Charlie Munger's reverse-thinking and bias detection, Duan Yongping's "right business, right people" principles, and Li Lu's civilization-evolution trend analysis. These are encoded in skills/investment-research.md.

How does the automated audit process work in AI Berkshire?

The tools/report_audit.py script randomly samples 15% of data points from the completed report, re-validates them against primary sources, and calculates deviation rates. The report is only published if all deviations are ≤ 1%, ensuring statistical reliability of the final investment thesis.

Where can I find the complete methodology definition for AI Berkshire's research pipeline?

The complete 10-stage methodology is documented in skills/investment-research.md in the xbtlin/ai-berkshire repository. This file defines each analytical stage, the required data inputs, and the validation criteria used to perform industry research from an investment theme.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}