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_cost_model.py

@@ -0,0 +1,140 @@
+"""Cost engine — including the spec's acceptance numbers."""
+
+from __future__ import annotations
+
+import pytest
+
+from tokencalc.cost_model import (
+    calculate_consumption_ai_cost,
+    calculate_per_user_ai_cost,
+    calculate_platform_license_cost,
+    calculate_total_cost,
+)
+from tokencalc.defaults import (
+    CONTRACTED_NAMED_USERS,
+    CTM_DEFAULT_FEATURE_SCOPES,
+    CTM_DEFAULT_SITES,
+    DEFAULT_METERS,
+    DEFAULT_PRICING,
+)
+from tokencalc.inputs import FeatureScope, SiteInput
+from tokencalc.scenarios import get_scenario
+
+ALL_SITES = [s.site_name for s in CTM_DEFAULT_SITES]
+
+
+def _scope(feature, sites=None, **kw):
+    return FeatureScope(feature, sites or ALL_SITES, **kw)
+
+
+def test_default_sites_match_contracted_users():
+    assert sum(s.named_users for s in CTM_DEFAULT_SITES) == CONTRACTED_NAMED_USERS
+
+
+def test_sta_acceptance_number():
+    """2,088 users × 30 tokens × 12 months × $1 = $751,680."""
+    df = calculate_per_user_ai_cost(
+        CTM_DEFAULT_SITES, _scope("Speech & Text Analytics"),
+        DEFAULT_METERS["Speech & Text Analytics"], DEFAULT_PRICING,
+    )
+    assert df["annual_cost"].sum() == pytest.approx(751_680)
+
+
+def test_agent_copilot_acceptance_number():
+    """2,088 users × 40 tokens × 12 months × $1 = $1,002,240."""
+    df = calculate_per_user_ai_cost(
+        CTM_DEFAULT_SITES, _scope("Agent Copilot"),
+        DEFAULT_METERS["Agent Copilot"], DEFAULT_PRICING,
+    )
+    assert df["annual_cost"].sum() == pytest.approx(1_002_240)
+
+
+def test_per_user_not_active_before_phase():
+    df = calculate_per_user_ai_cost(
+        CTM_DEFAULT_SITES, _scope("AI Translate", phase=3),
+        DEFAULT_METERS["AI Translate"], DEFAULT_PRICING, year=2,
+    )
+    assert df["annual_cost"].sum() == 0
+
+
+def test_copilot_covers_supervisor_summary():
+    """Rule 1: AI Summary cost is zero at Copilot sites."""
+    scenario = get_scenario("realistic")
+    total = calculate_total_cost(
+        CTM_DEFAULT_SITES,
+        [
+            _scope("Agent Copilot"),
+            _scope("AI Summary & Insights"),
+        ],
+        DEFAULT_METERS, DEFAULT_PRICING, scenario, year=1,
+        include_platform=False,
+    )
+    summary_row = total[total["cost_line"] == "AI Summary & Insights"].iloc[0]
+    assert summary_row["annual_cost"] == 0
+    # Without Copilot the same line costs real money.
+    total2 = calculate_total_cost(
+        CTM_DEFAULT_SITES,
+        [_scope("AI Summary & Insights")],
+        DEFAULT_METERS, DEFAULT_PRICING, scenario, year=1,
+        include_platform=False,
+    )
+    assert total2[total2["cost_line"] == "AI Summary & Insights"].iloc[0][
+        "annual_cost"
+    ] > 0
+
+
+def test_consumption_tokens_rounded_up_monthly():
+    """Rule 2: ceil on monthly site token totals."""
+    site = SiteInput(
+        "Tiny", "US", agents=5, supervisors=0,
+        voice_volume_monthly=100, 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=60,
+        fully_loaded_agent_cost_annual=65_000,
+        fully_loaded_supervisor_cost_annual=95_000,
+    )
+    # realistic: 100 calls × 35% × 1.5 min = 52.5 min × (1/17) = 3.088
+    # tokens × 70% Y1 ramp applied to units → 36.75 min → 2.16 tokens → ceil 3
+    df = calculate_consumption_ai_cost(
+        [site], FeatureScope("Voice Bot", ["Tiny"]),
+        DEFAULT_METERS["Voice Bot"], "realistic", DEFAULT_PRICING, year=1,
+    )
+    assert df.iloc[0]["tokens_monthly"] == 3
+    assert df.iloc[0]["annual_cost"] == pytest.approx(3 * 12 * 1.0)
+
+
+def test_regional_pricing_not_hardcoded():
+    pricing = dict(DEFAULT_PRICING)
+    from tokencalc.meters import TokenPricing
+
+    pricing["APAC"] = TokenPricing(region="APAC", list_rate_per_token=2.0)
+    apac_site = next(s for s in CTM_DEFAULT_SITES if s.region_pricing == "APAC")
+    df = calculate_per_user_ai_cost(
+        [apac_site], _scope("Speech & Text Analytics", [apac_site.site_name]),
+        DEFAULT_METERS["Speech & Text Analytics"], pricing,
+    )
+    expected = apac_site.named_users * 30 * 12 * 2.0
+    assert df["annual_cost"].sum() == pytest.approx(expected)
+
+
+def test_year1_consumption_ramp_default_70pct():
+    sc = get_scenario("realistic")
+    assert sc.cost_realization(1) == pytest.approx(0.70)
+    assert sc.cost_realization(2) == 1.0
+
+
+def test_platform_license_cost():
+    df = calculate_platform_license_cost(CTM_DEFAULT_SITES)
+    expected = CONTRACTED_NAMED_USERS * 111.28 * 12
+    assert df["annual_cost"].sum() == pytest.approx(expected)
+
+
+def test_total_cost_default_scopes_runs_all_years():
+    for year in (1, 2, 3):
+        df = calculate_total_cost(
+            CTM_DEFAULT_SITES, CTM_DEFAULT_FEATURE_SCOPES,
+            DEFAULT_METERS, DEFAULT_PRICING, "realistic", year,
+        )
+        assert (df["annual_cost"] >= 0).all()
+        assert {"cost_line", "scope", "annual_cost", "confidence"} <= set(df.columns)