Industry Chain Research

The problem

PE and VC analysts spend days manually searching fragmented data sources to build industry maps and due diligence reports. Manually cross-referencing industry policies, patent filings, and bidding data is slow, prone to oversight, and often results in incomplete competitive landscapes.

What it does

• Scans policy, financing, and secondary market signals to identify high-potential industry chains.
• Decomposes industry chains into specific segments and evaluates them based on barriers, scarcity, and pricing power.
• Discovers non-public companies by cross-referencing registration, patent, and bidding data across 300+ sources.
• Performs deep due diligence on targets, including financial estimation via tax/environmental data and IPO path simulation.
• Provides verifiable evidence for every conclusion with clickable source links.

Frameworks & tools

Python 3.10+, Cue Data API. Accesses diverse datasets including administrative records, judicial rulings, and brokerage research.

Why this beats prompting it yourself

General LLMs hallucinate financial data and lack access to real-time Chinese industrial databases. This skill enforces a 4-stage analytical funnel with built-in anti-hallucination mechanisms, ensuring every claim is backed by industrial-grade source data and specific data points like patent counts or bidding history.

Use cases

• Sector scanning to identify emerging investment themes in high-tech manufacturing.
• Sourcing pre-IPO targets within a specific niche of the supply chain.
• Conducting preliminary due diligence on non-public firms using public data proxies.
• Analyzing industry value migration and competitive moats for portfolio management.

Known limitations

Only covers public data sources. Private financial records for non-public firms are estimated via proxies and require offline verification.