PE/VC Industry Investment Research Report Generator

Problem

PE/VC industry research often mixes sector analysis, financing comparisons, team resumes, academic signals, and risk notes across web pages, financial APIs, business registries, and scholar profiles. Manual assembly can leave out sources, mix valuation conventions, bury comparisons in prose, and apply inconsistent standards to academic-driven startups. This skill organizes that material into a structured PDF report: cover, table of contents, chapters, tables, and source annotations, suitable for deal review, sector screening, government fund materials, and LP-facing industry analysis.

How It Works

The workflow has three phases. It first confirms the target sector, focus areas, and selected companies, then collects data in parallel from A-share market data, historical financial data, financing events, policy updates, and core team information. Financing records prioritize Tianyancha and the National Enterprise Credit Information Publicity System, with Qichacha, ITJuzi, and verified news as secondary sources; each item should retain a query link. For hard-tech, biopharma, and AI projects with academic founders, it also collects Google Scholar citations, h-index, top-conference/top-journal papers, academic awards, and timelines, then evaluates signal, conversion, resource, and management value. The generation phase maps the material into chapters such as sector overview, market size, competitive landscape, company profile, technology path, team analysis, investment logic, and risks, and renders a Chinese A4 PDF using scripts/report_template.py.

Boundaries

This is a research-report drafting workflow, not formal due diligence, audit, or a final investment decision. Data claims need sources; financing and valuation figures should keep qualifiers such as estimated, reported, or varies by source. Comparisons should be table-first, and team resumes, funding history, and academic output should not rely on a single page or a single media source. The PDF pipeline is Python-based, so font paths, A-share ticker suffixes, and string encoding need normal local environment checks.