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_meters.py

@@ -0,0 +1,66 @@
+"""Meter catalogue integrity."""
+
+from __future__ import annotations
+
+import pytest
+
+from tokencalc.defaults import DEFAULT_METERS, DEFAULT_PRICING
+from tokencalc.meters import Confidence, MeterType, TokenMeter, TokenPricing
+
+
+def test_all_spec_meters_present():
+    expected = {
+        "Voice Bot", "Virtual Agent (legacy)", "Agentic Virtual Agent",
+        "AI Summary & Insights", "Direct Messaging", "Social Listening",
+        "Social Responses", "Speech & Text Analytics", "Agent Copilot",
+        "Email AI (Auto-Suggest)", "Email AI (Auto-Respond)", "AI Translate",
+    }
+    assert expected == set(DEFAULT_METERS)
+
+
+def test_confirmed_rates():
+    m = DEFAULT_METERS
+    assert m["Voice Bot"].units_per_token == 17
+    assert m["Voice Bot"].tokens_per_unit == pytest.approx(0.0588, abs=1e-3)
+    assert m["Agentic Virtual Agent"].tokens_per_unit == 1.2
+    assert m["AI Summary & Insights"].tokens_per_unit == 0.02
+    assert m["Direct Messaging"].units_per_token == 400
+    assert m["Speech & Text Analytics"].tokens_per_unit == 30
+    assert m["Agent Copilot"].tokens_per_unit == 40
+
+
+def test_unknown_meters_flagged():
+    unknown = {f for f, m in DEFAULT_METERS.items() if m.confidence is Confidence.UNKNOWN}
+    assert unknown == {
+        "Email AI (Auto-Suggest)", "Email AI (Auto-Respond)", "AI Translate"
+    }
+    assert Confidence.UNKNOWN.icon == "🔴"
+    assert Confidence.CONFIRMED.icon == "🟢"
+
+
+def test_inverse_consistency_validated():
+    with pytest.raises(ValueError, match="not inverses"):
+        TokenMeter(
+            feature="Bad", meter_type=MeterType.PER_MINUTE,
+            units_per_token=10, tokens_per_unit=0.5,
+            confidence=Confidence.ESTIMATED, notes="",
+        )
+
+
+def test_every_confirmed_meter_has_source_url():
+    for m in DEFAULT_METERS.values():
+        if m.confidence is Confidence.CONFIRMED:
+            assert m.source_url, f"{m.feature} missing source URL"
+
+
+def test_pricing_effective_rate():
+    p = TokenPricing(region="US", list_rate_per_token=1.0,
+                     contracted_rate_per_token=0.85)
+    assert p.effective_rate(use_contracted=False) == 1.0
+    assert p.effective_rate(use_contracted=True) == 0.85
+    # no contracted rate → falls back to list
+    assert DEFAULT_PRICING["US"].effective_rate(use_contracted=True) == 1.0
+
+
+def test_all_regions_priced():
+    assert set(DEFAULT_PRICING) == {"US", "EU", "AU", "APAC"}