A deal-sourcing and screening dashboard built around a specific, opinionated thesis: PE doesn't play well at the AI labs/compute layer — it plays in the fragmented, already-profitable SMB service businesses (bookkeeping, IT support, medical billing, home services) where AI is a margin lever on an existing cash-flow base, not the product itself.
All data is fictional. Every company, financial figure, and deal note is solely illustrative — built to demonstrate a sourcing/screening framework end-to-end, not to represent real targets.
Live: https://bakulbadwal.github.io/dealdocket/
Open it: clone the repo and run a local static server (see below) — the app fetches data.json, which browsers block over file://.
- The five-box screen — five weighted criteria (market fragmentation, unit economics, AI-adoption leverage, moat & stickiness, exit path), each with a live slider. Drag any weight and the entire 30-deal pipeline re-ranks in real time — the point is to make the thesis's sensitivity to its own assumptions visible, not just show a static scorecard.
- Weight presets — one-click lens switches (Balanced, AI-Leverage Max, Margin First, Moat & Exit) that jump all five sliders at once; any manual drag hands control back to you.
- A 30-deal illustrative pipeline across 10 service verticals (IT managed services, bookkeeping, marketing agencies, home services, staffing, legal back-office, medical billing/RCM, logistics, customer support/BPO, property management), spanning every stage from sourced to closed to passed — including deals that fail the screen, not just wins. Each row carries an inline thesis note explaining why the deal ranks where it does.
- A deal detail drawer with a radar chart — click any deal for its five-box shape rendered as a live SVG radar (a spiky AI-leverage deal looks visibly different from a balanced platform candidate), plus per-criterion rationale, financials, and the thesis note.
- Filters and live stats — search, vertical/stage/channel filters, an active-pipeline-only toggle, and a stats bar (deals shown, active pipeline, closed, average weighted score).
The visual language is deliberately terminal-inspired — monospace numerals, hairline rules, a single green accent that carries meaning (high scores and healthy stages) rather than decoration.
Same data/view split as the AI Stack: data.json holds every deal, the framework definition, and the thesis copy; app.js renders it and runs the scoring engine; styles.css handles presentation. A sourcing pipeline is exactly the kind of content that should be able to update independently of the render logic.
git clone https://github.com/bakulbadwal/dealdocket.git
cd dealdocket
python3 -m http.server 8000
Then open http://localhost:8000/.
Vanilla HTML/CSS/JS. No framework, no build step, no dependencies.
MIT — see LICENSE.