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/tests/test_benefit_model.py

@@ -0,0 +1,107 @@
+"""Benefit engine."""
+
+from __future__ import annotations
+
+import pytest
+
+from tokencalc.benefit_model import (
+    calculate_acw_summarization_benefit,
+    calculate_email_ai_benefit,
+    calculate_total_benefit,
+)
+from tokencalc.defaults import CTM_DEFAULT_FEATURE_SCOPES, CTM_DEFAULT_SITES
+from tokencalc.inputs import WORKING_SECONDS_PER_YEAR, FeatureScope, SiteInput
+
+ALL_SITES = [s.site_name for s in CTM_DEFAULT_SITES]
+
+
+def _small_site() -> SiteInput:
+    return SiteInput(
+        "Small", "US", agents=10, supervisors=1,
+        voice_volume_monthly=10_000, email_volume_monthly=1_000,
+        chat_volume_monthly=0, sms_volume_monthly=0,
+        voice_aht_seconds=300, email_aht_seconds=600,
+        chat_aht_seconds=480, voice_acw_seconds=60,
+        fully_loaded_agent_cost_annual=74_880,  # → $0.01/second exactly
+        fully_loaded_supervisor_cost_annual=95_000,
+    )
+
+
+def test_acw_benefit_hand_check():
+    """10,000 calls × 12 × 70% eligible × 60s ACW × 40% reduction ×
+    50% Y1 realization × $0.01/s = $10,080."""
+    site = _small_site()
+    assert site.agent_cost_per_second == pytest.approx(0.01)
+    df = calculate_acw_summarization_benefit(
+        [site], FeatureScope("Agent Copilot", ["Small"]), "realistic", year=1,
+    )
+    expected = 10_000 * 12 * 0.70 * 60 * 0.40 * 0.50 * 0.01
+    assert df["annual_value"].sum() == pytest.approx(expected)
+
+
+def test_email_benefit_split():
+    site = _small_site()
+    df = calculate_email_ai_benefit(
+        [site], FeatureScope("Email AI (Auto-Respond)", ["Small"]),
+        "realistic", year=1,
+    )
+    lines = set(df["benefit_line"])
+    assert lines == {
+        "Email Auto-Respond (displaced handling)",
+        "Email Auto-Suggest (drafting time)",
+    }
+    # auto-respond: 1,000×12 × 20% × 600s × 50% × $0.01 = $7,200
+    respond = df[df["benefit_line"].str.contains("Respond")]["annual_value"].sum()
+    assert respond == pytest.approx(7_200)
+
+
+def test_scenarios_produce_distinct_benefits():
+    totals = {
+        name: calculate_total_benefit(
+            CTM_DEFAULT_SITES, CTM_DEFAULT_FEATURE_SCOPES, name, year=2
+        )["annual_value"].sum()
+        for name in ("floor", "realistic", "stretch")
+    }
+    assert totals["floor"] < totals["realistic"] < totals["stretch"]
+
+
+def test_claim_exceeds_realistic():
+    realistic = calculate_total_benefit(
+        CTM_DEFAULT_SITES, CTM_DEFAULT_FEATURE_SCOPES, "realistic", year=1,
+        params="realistic",
+    )["annual_value"].sum()
+    claim = calculate_total_benefit(
+        CTM_DEFAULT_SITES, CTM_DEFAULT_FEATURE_SCOPES, "realistic", year=1,
+        params="claim",
+    )["annual_value"].sum()
+    assert claim > realistic
+
+
+def test_benefits_ramp_by_year():
+    by_year = [
+        calculate_total_benefit(
+            CTM_DEFAULT_SITES, CTM_DEFAULT_FEATURE_SCOPES, "realistic", year=y
+        )["annual_value"].sum()
+        for y in (1, 2, 3)
+    ]
+    assert by_year[0] < by_year[1] < by_year[2]
+
+
+def test_zero_volume_site_is_safe():
+    site = SiteInput(
+        "Empty", "US", agents=0, supervisors=0,
+        voice_volume_monthly=0, email_volume_monthly=0,
+        chat_volume_monthly=0, sms_volume_monthly=0,
+        voice_aht_seconds=300, email_aht_seconds=600,
+        chat_aht_seconds=480, voice_acw_seconds=0,
+        fully_loaded_agent_cost_annual=0,
+        fully_loaded_supervisor_cost_annual=0,
+    )
+    df = calculate_total_benefit(
+        [site], [FeatureScope("Agent Copilot", ["Empty"])], "realistic", year=1,
+    )
+    assert df["annual_value"].sum() == 0
+
+
+def test_working_seconds_constant():
+    assert WORKING_SECONDS_PER_YEAR == 2_080 * 3_600