Current Status
6
Companies analyzed
15
Scoring dimensions
1024
Avg total score / 1500
5×
Ensemble scoring passes (median + uncertainty)
Companies
| Ticker | Company | Tier | Total Score / 1500 | Dominant Practice | Dominant Objective |
|---|---|---|---|---|---|
| AMZN | Amazon.com Inc. | Tier 1 | Corporate Venture Capital | Develop Business Ecosystems | |
| GOOGL | Alphabet Inc. | Tier 1 | Venture Building | Access to New Technology | |
| MSFT | Microsoft Corporation | Tier 1 | Venture Building | Access to New Technology | |
| CSCO | Cisco Systems Inc. | Tier 1 | Accelerators & Incubators | Access to New Technology | |
| QCOM | Qualcomm Inc. | Tier 1 | Corporate Venture Capital | Market Expansion | |
| MRK | Merck & Co. Inc. | Tier 1 | Corporate Venture Capital | Access to New Technology |
Analysis Pipeline (per company)
1
Document identification & preprocess
Resolve ticker → CIK, download the complete EDGAR SGML 10-K (SQLite-cached), and extract the full narrative — Items 1, 1A, 2, 7, and all of Item 8 incl. financial footnotes. Fetch XBRL structured financials.
2
RAG index & entity discovery
Build a BM25 + sentence-transformer index used only to surface investment-relevant passages; discover named funds / portfolio companies (curated context + retrieval + optional Claude refinement).
3
Windowed extraction (all 15 dimensions)
Tile the entire narrative into overlapping 60K-char windows and scan every window for all 15 dimensions — nothing truncated; evidence merged + deduped. Prompt caching on the shared prefix.
4
Curated-web augmentation (optional)
Scrape curated CVC / accelerator / impact pages and run them through the same all-15-dimension extractor, merging the web evidence (informs every dimension incl. all 7 objectives) before scoring.
5
Ensemble scoring + investment & funds
5 independent scoring passes (temperature=0) → per-dimension median with min/max/std uncertainty, ranks, P×O matrix. A synthesis call adds investment_and_funds (fund sizes + preferences). Financials overridden by XBRL.
6
Output generation
Per-company: P×O heatmap, dashboard (plain + uncertainty/error-bars), ensemble table/figure, .docx. Portfolio: master CSV, heatmaps, self-contained HTML dashboard, portfolio .docx.
Tech Stack
| AI / LLM |
Anthropic Claude API
claude-sonnet-4-5 (pinned 20250929)
temperature=0.0
prompt caching
ensemble scoring
|
| Retrieval |
BM25 (rank-bm25)
sentence-transformers (all-MiniLM-L6-v2)
|
| Web scraping |
requests / httpx
BeautifulSoup
openpyxl (URL workbook)
|
| Data |
pandas
numpy
scipy
SEC EDGAR API
XBRL financials
SQLite cache
|
| Visualisation |
matplotlib
seaborn
wordcloud
HTML/CSS dashboard
|
| Output |
python-docx
CSV
PNG
self-contained HTML
|
Key Files
| cv_toolkit.py | CLI entry point — all subcommands (preprocess, analyze, charts, report, csv, portfolio-report) |
| cv_analyzer.py | Core pipeline: SGML extraction → section parsing → batch extraction → scoring → persist |
| cv_config.py | Single source of truth: dimension lists, colors, financial metrics, paths, tier thresholds |
| cv_sec.py | SEC EDGAR integration: get_filing_text(), fetch_xbrl_financials(), merge_xbrl_into_financials() |
| cv_rag.py | BM25 + sentence-transformer hybrid index; investment entity discovery |
| cv_framework.py | Builds scoring rubrics via Claude from framework/ .txt definitions; caches to .cv_cache/ |
| cv_visualizer.py | Per-company charts: P×O heatmap, dashboard PNG, word clouds |
| cv_portfolio.py | Portfolio-level: heatmap, mean scores, HTML dashboard, portfolio .docx |
| cv_report.py | Per-company .docx report; build_summary_row() for CSV flattening |
| sec_filing_cache.py | EDGAR download + SQLite cache; HTML section extraction with TOC-skip logic |
| cv_url_scraper.py | Curated-web scraper (CVC / accelerator / impact URLs) → scraped/<TICKER>/result.json; powers the optional --with-scraped augmentation |
| ensemble_report.py | Renders the ensemble uncertainty table (CSV/MD) + error-bar figure from ensemble_scores.json |
| company_context.json | CVC fund names, portfolio companies, dimension_hints per ticker — injected into prompts |
| run_batch.sh | Batch analysis script (v1 framework); supports --force and --ticker flags |
| run_batch_v2.sh | Batch script using framework_v2 (shorter, broader rubric definitions); outputs to outputs_v2/ |
| outputs_v2/master_dataframe.csv | Flat file: one row per company, all scores / ranks / descriptions / financials (canonical, curated-web augmented) |
| outputs_v2/portfolio_dashboard.html | Self-contained (zero CDN) interactive portfolio dashboard |
| outputs_v2/<TICKER>/analysis.json | Master per-company record: scores, ranks, P×O matrix, ensemble + uncertainty, investment_and_funds, financials |
| IMPLEMENTATION_GUIDE.html | End-to-end step-by-step guide (programs used + files created at each step) |
| PROJECT_SUMMARY.html | Project summary with current results, architecture, and command reference |
Dimension Framework (15 dimensions)
4 Practices
Corporate Venture Capital
Accelerators & Incubators
Venture Building
Venture Clienting
4 Activities
Mentorship
Business Services
Workspace
Events
7 Objectives
Risk Management
Market Expansion
Access to New Technology
Develop Business Ecosystems
Social & Environmental Impact
Entrepreneurial Culture
Financial Return