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/tokencalc/scenarios.py

@@ -0,0 +1,112 @@
+"""
+Scenario definitions — Floor / Realistic / Stretch.
+
+Every scenario parameter the cost and benefit engines read lives here;
+no magic numbers in the calculation modules. Ships with the spec
+defaults; callers may construct custom :class:`Scenario` objects.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+
+
+@dataclass
+class Scenario:
+    name: str
+
+    # ── Cost-side drivers ───────────────────────────────────────────
+    voice_bot_deflection: float          # share of voice volume deflected to bot
+    voice_bot_avg_minutes: float         # bot minutes per deflected call
+    agentic_va_deflection: float         # share of voice volume to agentic VA
+    voice_summarization_eligibility: float
+    voice_knowledge_eligibility: float
+    email_auto_respond_rate: float       # share of email auto-responded
+    email_auto_suggest_acceptance: float
+
+    # year -> fraction of full benefit realized
+    benefit_realization: dict[int, float] = field(default_factory=dict)
+
+    # year -> fraction of steady-state consumption cost incurred.
+    # Per-user licenses are paid in full from day 1; consumption meters
+    # ramp with usage (default Y1 = 70%).
+    consumption_cost_realization: dict[int, float] = field(
+        default_factory=lambda: {1: 0.70, 2: 1.0, 3: 1.0}
+    )
+
+    def realization(self, year: int) -> float:
+        if year in self.benefit_realization:
+            return self.benefit_realization[year]
+        last = max(self.benefit_realization, default=0)
+        return self.benefit_realization.get(last, 1.0) if year > last else 0.0
+
+    def cost_realization(self, year: int) -> float:
+        if year in self.consumption_cost_realization:
+            return self.consumption_cost_realization[year]
+        last = max(self.consumption_cost_realization, default=0)
+        return (
+            self.consumption_cost_realization.get(last, 1.0) if year > last else 0.0
+        )
+
+
+#: Benefit reduction parameters. ``claim`` = Genesys ROI-doc figure;
+#: ``realistic`` = pressure-tested midpoint of the spec's Y1 range.
+#: The benefit engine uses ``realistic`` by default; ``claim`` powers
+#: the side-by-side comparison view.
+BENEFIT_PARAMS: dict[str, dict[str, float]] = {
+    "voice_aht_knowledge_reduction": {"claim": 0.094, "realistic": 0.055},  # 4-7% Y1
+    "voice_acw_reduction": {"claim": 1.00, "realistic": 0.40},              # 30-50% Y1
+    "digital_aht_reduction": {"claim": 0.18, "realistic": 0.085},           # 5-12% Y1
+    "digital_acw_reduction": {"claim": 1.00, "realistic": 0.40},            # 30-50% Y1
+    "sta_aht_reduction": {"claim": 0.04, "realistic": 0.015},               # 1-2% Y1
+    "email_auto_suggest_time_saving": {"claim": 0.30, "realistic": 0.30},   # × acceptance
+    # ESTIMATED lines (no Genesys claim published):
+    "supervisor_copilot_time_saving": {"claim": 0.10, "realistic": 0.05},
+    "predictive_routing_aht_reduction": {"claim": 0.04, "realistic": 0.02},
+}
+
+
+SCENARIOS: dict[str, Scenario] = {
+    "floor": Scenario(
+        name="floor",
+        voice_bot_deflection=0.20,
+        voice_bot_avg_minutes=1.0,
+        agentic_va_deflection=0.05,
+        voice_summarization_eligibility=0.50,
+        voice_knowledge_eligibility=0.40,
+        email_auto_respond_rate=0.10,
+        email_auto_suggest_acceptance=0.25,
+        benefit_realization={1: 0.30, 2: 0.60, 3: 0.80},
+    ),
+    "realistic": Scenario(
+        name="realistic",
+        voice_bot_deflection=0.35,
+        voice_bot_avg_minutes=1.5,
+        agentic_va_deflection=0.15,
+        voice_summarization_eligibility=0.70,
+        voice_knowledge_eligibility=0.60,
+        email_auto_respond_rate=0.20,
+        email_auto_suggest_acceptance=0.40,
+        benefit_realization={1: 0.50, 2: 0.80, 3: 0.95},
+    ),
+    "stretch": Scenario(
+        name="stretch",
+        voice_bot_deflection=0.50,
+        voice_bot_avg_minutes=2.0,
+        agentic_va_deflection=0.25,
+        voice_summarization_eligibility=0.90,
+        voice_knowledge_eligibility=0.80,
+        email_auto_respond_rate=0.50,
+        email_auto_suggest_acceptance=0.60,
+        benefit_realization={1: 0.75, 2: 0.95, 3: 1.00},
+    ),
+}
+
+
+def get_scenario(name: str) -> Scenario:
+    try:
+        return SCENARIOS[name.lower()]
+    except KeyError as e:
+        raise KeyError(
+            f"Unknown scenario {name!r}. Valid: {sorted(SCENARIOS)}"
+        ) from e