Predict the outcome of a business transformation before you commit to it.
"See 1,000 futures. Choose the one truth."
TimeStone builds a synthetic digital twin of a company, generates up to 1,000 transformation scenarios (Claude-powered, with a rule-based fallback), runs each through a Monte Carlo simulation with realistic risk, and ranks the strategies that maximize risk-adjusted NPV — so leaders decide with a probability distribution, not a single optimistic number.
Transformation decisions — digital programs, M&A integration, market expansion, big tech bets — are made on one deterministic spreadsheet and a gut feel. Then most of them miss their targets. The spreadsheet never showed the downside, the execution risk, or the ramp-up curve.
TimeStone replaces the single number with a distribution of outcomes you can actually reason about.
| Step | Example |
|---|---|
| 01 — Build the twin | Model the company's financials, operations, and market as a digital twin. |
| 02 — Generate scenarios | Produce up to 1,000 transformation hypotheses (Claude or rule-based). |
| 03 — Simulate | Run Monte Carlo on each scenario with shocks, delays, and adoption ramp. |
| 04 — Rank & stress-test | Sort by P(NPV>0), mean NPV, and payback; tornado-analyze the top picks. |
- ✅ You're a transformation lead, strategy team, or operator weighing a major bet
- ✅ You're tired of single-point NPV that hides the downside
- ✅ You want execution risk, market shocks, and adoption ramp modeled explicitly
- ✅ You need a defensible, probability-weighted recommendation for the board
🏢 Digital Twins — Synthetic models of an organization across financials, operations, and market.
🎲 Monte Carlo Engine — 1,000 iterations per scenario with external shocks and ramp-up; deterministic seeding for reproducibility.
🧠 Scenario Generation — Claude generates transformation hypotheses; falls back to a rule-based generator with no API key.
📊 Risk-Adjusted Ranking — Strategies ranked by P(NPV>0), mean NPV, ROI multiple, and median payback.
🌪️ Sensitivity / Tornado — See which assumptions actually move the outcome.
🧪 Invariant Test Suite — pytest locks the financial-model invariants (NPV math, risk variance, no-perfect-success guardrail, seeding).
🖥️ Interactive Dashboard — Streamlit + Plotly UI plus a CLI for batch runs.
🏭 Multi-Industry Templates — Transportation, energy, fintech, SaaS, manufacturing out of the box.
1. DATA INPUT -> Company financials, operations, market data
2. DIGITAL TWIN -> Synthetic model of the business
3. SCENARIO GENERATION -> up to 1,000 hypotheses (Claude or rule-based)
4. MONTE CARLO -> 1,000 iterations/scenario with shocks + ramp-up
5. SYNTHESIS API -> Programmatic access to runs, scenarios, results
6. RANKING -> Top-N by P(NPV>0), NPV, payback
7. SENSITIVITY -> Tornado analysis on the top scenarios
Financial model: capex in year 0, benefits after a (delayed) implementation period, adoption ramp 40% / 70% / 95% / 100% by year, 5-year NPV at configurable WACC (default 12%).
Risk modeled per iteration: execution failure (~5%), market downturn (~8%, −30% revenue), competitive response (~15%, −20% revenue), cost overruns (up to +50%), implementation delays (up to +80%). All configurable via SimulationConfig.
Anonymized example. The model ships with several industry twins; company names are illustrative.
RANK #1 Dynamic Pricing Implementation
P(NPV > 0) : 96%
Mean NPV (5y) : $43M
Mean ROI multiplier : 8.9x
Median payback : 2 years
Recommendation : PROCEED with phased rollout
RANK #3 Predictive Maintenance System
P(NPV > 0) : 64%
Median payback : 5 years
Recommendation : PILOT first — high execution risk
Example industry twins included: a national rail operator (transportation), a consumer-fintech platform, a national power grid (energy), a B2B SaaS company, and a discrete manufacturer.
- Language: Python 3.10+
- AI: Anthropic Claude SDK (optional; rule-based fallback)
- Simulation: NumPy, Monte Carlo
- Modeling: dataclasses + Pydantic
- API: synthesis API + synthetic-data service for programmatic runs
- UI: Streamlit + Plotly
- Testing: pytest (financial-invariant suite)
git clone https://github.com/westfellow25/timestone-ai.git
cd timestone-ai
pip install -r requirements.txt
cp .env.example .env # optional: ANTHROPIC_API_KEY for AI scenarios
python -m timestone list-companies # see available industry twins
python -m timestone assess "<company name>" # run a full assessment
streamlit run src/timestone/interfaces/web/dashboard.pyRun the tests:
pytest tests/ -v- Not a BI dashboard. It simulates futures, it doesn't just chart the past.
- Not a point forecast. Every answer is a distribution with explicit risk.
- Not a black box. The financial model and risks are configurable and test-locked.
- Monte Carlo engine, digital twins, multi-industry templates
- Claude scenario generation + rule-based fallback
- Synthesis API + synthetic-data service
- Invariant test suite
- PDF export of the executive report
- Real-time data integration (financial APIs)
- Multi-objective optimization (NPV vs risk vs time)
- Bayesian updating from pilot results
Built by @westfellow25.