TurfDynamics gives enterprises a defensible way to make their hardest calls. It turns scattered data, competing priorities, and tacit expert judgment into structured, auditable decision models — so a team can see exactly how a choice was reached, weigh the trade-offs by role, and revisit it when the facts change. Every decision carries its evidence, its dissent, and its versioned history, which means the organisation can both stand behind the call today and learn from it later.
Open problems worked underneath this project.
Measuring extraction and value capture in two-sided markets with open data and reproducible methods, instead of vibes and quarterly decks.
How far can open, self-hosted LLMs go on real financial and planning tasks before cost or accuracy forces a frontier model? Benchmarks welcome.
Structured capture of options, evidence, and trade-offs as a reviewable decision graph.
AI-assisted compilation that weights each contributor's input by their role, with realtime notifications as a decision moves through its lifecycle.
Versioned, reviewable decision records with one-click PDF export for sign-off.
Branch a decision into scenarios and compare projected outcomes before committing.