feat: add GenesysCX study and fix Streamlit chart key collisions

- Add 202512_GenesysCX TEI study (config, seed data, notebooks, README)
  with NPV $10.8M / ROI 266% including AI-token cost line
- Add explicit `key` parameter to all chart wrappers in app/components
  to prevent StreamlitDuplicateElementId errors when the same figure
  type renders across Summary/Benefits/Costs tabs
- Render benefits bar and cost pie charts on their respective tabs
- Add benefits_vs_costs_by_year chart wrapper

studies/202512_GenesysCX/ctm-token-calculator/README.md

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+# CTM Token Calculator
+
+**Genesys AI Token Cost & Business Case Calculator** — interactive,
+defensible modeling of Genesys Cloud **CX 3** platform + AI feature costs
+against realistic benefit scenarios, replacing single-point vendor ROI
+outputs with sensitivity-aware **Floor / Realistic / Stretch** analysis.
+
+> ⚠️ **Planning tool.** Uses published Genesys list rates unless overridden —
+> explicitly not a replacement for contractual pricing. No Genesys API
+> integration; this is a forward-looking model, not a production-consumption
+> dashboard.
+
+## CTM context
+
+- 9 sites (NAM, EMEA, AUZ, 6× APAC), **2,088 contracted named users**
+- NAM volumes from CTM discovery; **all other site data is estimated —
+  confirm with CTM** (flagged throughout the UI)
+- Cost takeouts include the NICE IEX (NAM) retirement placeholder ($1.3M/yr,
+  estimated)
+- Every meter carries a confidence flag: 🟢 confirmed (published rate) ·
+  🟡 estimated · 🔴 unknown (working default, rate not yet sourced)
+
+## Install & run
+
+```bash
+cd ctm-token-calculator
+python -m venv .venv && source .venv/bin/activate
+pip install -r requirements.txt
+
+# Streamlit app (7 pages: Inputs → Export)
+streamlit run app/streamlit_app.py
+
+# JupyterLab notebook variant (same numbers, same library)
+jupyter lab notebooks/ctm_token_calculator.ipynb
+
+# Tests
+pytest
+```
+
+## Architecture
+
+All math lives in the pure-Python `tokencalc/` library; the notebook and
+Streamlit app are thin presentation layers calling the same functions —
+Run-All in the notebook produces identical headline numbers to the app on
+default inputs.
+
+| Module | Purpose |
+|---|---|
+| `meters.py` | Token meter + pricing dataclasses, confidence enum |
+| `defaults.py` | Genesys meter catalogue, CTM sites/takeouts/phasing, CX 3 rate ($111.28/user/mo) |
+| `inputs.py` | Validated input dataclasses (sites, feature scopes, takeouts) |
+| `scenarios.py` | Floor/Realistic/Stretch + benefit params (Genesys claim vs pressure-tested) |
+| `cost_model.py` | Platform, per-user AI, consumption AI cost engines |
+| `benefit_model.py` | AHT/ACW/email/deflection/STA benefit engines |
+| `business_case.py` | 3-year P&L, NPV @ 8%, payback, ROI |
+| `exports.py` | Multi-sheet Excel, CSV, JSON scenario save/load |
+
+### Correctness rules encoded in the model
+
+1. **Agent Copilot covers Supervisor AI Summary** — AI Summary & Insights is
+   never billed at sites where Copilot is enabled (Copilot's 40 tokens/user/mo
+   includes summarization). Implemented and tested.
+2. **Billing-style rounding** — monthly consumption token totals are rounded
+   up (`ceil`) per site before pricing; per-user totals are exact.
+3. **Regional pricing** — every site resolves its token rate through its
+   pricing region (US/EU/AU/APAC); nothing is hardcoded to US.
+4. **Adoption ramp** — consumption features ramp (default Y1 = 70%); per-user
+   licences are paid in full from their phase year. Phasing is per-site,
+   per-feature, per-phase (1/2/3/off).
+
+### Verified reference numbers
+
+- STA: 2,088 users × 30 tokens × 12 × $1 = **$751,680** ✓ (test)
+- Agent Copilot: 2,088 × 40 × 12 × $1 = **$1,002,240** ✓ (test)
+- NPV hand-check: 100/yr × 3 @ 8% = 257.710 ✓ (test)
+
+## Auditability
+
+Every number traces to an input and a meter: cost rows carry the feature,
+scope (sites), and confidence; benefit rows carry the driver line and scope;
+the Excel export includes input, meter, cost-detail, benefit-detail, business
+case, and three-scenario comparison sheets.