Migrate Genesys CX Cloud TEI study to the pattern; retire Streamlit app

studies/202512_GenesysCX -> studies/202512_TEI_Genesys_CX_Cloud,
rebuilt as pattern Variant 4 (TEI composite reproduction):

- teicalc/ self-contained engine: Forrester's tables as the never-edited
  verbatim anchor (incl. the p.14 typo note and the $0 AI-token line),
  generic model/scenarios/staging carried over from the Amazon Connect
  study, ClientDrivers overlay (agents / weekly interactions / revenue,
  flat composite so no growth re-base) with ai_tokens_annual as a direct
  input for the token line the published study models at $0
- one deliverable notebook (business_case.ipynb): widget-pair sidebar
  drivers incl. the AI-token price, published-vs-overlay KPI columns,
  cash-flow/waterfall/scenario charts, verification gate, backstage JSON
  data appendix
- gate + tests reproduce the published totals within $2: NPV $10.8M /
  ROI 266% (engine $10,783,466 / 265.79%; payback 3.3 months, not
  headlined in the PDF); 29 study tests, headless nbconvert green,
  stage simulation leak-free, exports carry the appendix
- old Athena workflow (00_provision..04_export, config.py, seed_data.py,
  PALLADIUM_GENESYSCX_* keys, ATHENA_EXPECTED reconciliation) deleted;
  git history preserves it

With the last legacy study migrated, the retirement lands too:
- app/ (Streamlit UI) and core/notebook_helpers deleted; nothing else
  imported them
- streamlit stripped from pyproject extras, requirements.txt, Makefile;
  .env.example reduced to the Athena keys; 00_setup.ipynb and
  core/bootstrap.py repointed at the pattern studies
- root README reworked: self-contained studies + slim core/ Athena
  toolkit (tei_client, calculations, export, cli)

All suites green: Genesys 29, Amazon Connect 27, CTM 55, template 7,
root 58.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

studies/202512_TEI_Genesys_CX_Cloud/tests/test_model.py

@@ -0,0 +1,122 @@
+"""Engine pins — every number hand-checked before pinning.
+
+RA_benefit = v×(1−rf), RA_cost = v×(1+rf), PV = Σ RA_n/(1.1)^n, initial
+undiscounted. The composite reproduction lands within $2 of the published
+Financial Summary (benefits PV $1.19 low, costs PV $0.40 high) — pinned
+both engine-exact (±$1) and against PUBLISHED (±$5). Forrester does not
+headline a payback for this study; the engine computes 3.3 months.
+"""
+
+import pytest
+
+from teicalc import (
+    BENEFITS_VERBATIM,
+    COSTS_VERBATIM,
+    PUBLISHED,
+    X_LABELS,
+    YEAR_INDEX,
+    YEARS,
+    by_calendar,
+    compute_summary,
+    discount_factor,
+    money,
+    npv,
+    payback_label,
+    payback_months,
+    payback_years,
+    roi_pct,
+)
+
+# Hand-checked risk-adjusted PVs per row (see module docstring).
+ROW_PVS = {
+    "legacy_retirement": 1_981_224.64,
+    "self_service_savings": 4_924_364.84,
+    "agent_efficiency": 6_517_541.70,
+    "agent_assist_sales": 1_417_505.63,
+    "cx_cloud_licenses": 2_193_403.46,
+    "implementation": 1_309_000.00,
+    "ongoing_management": 554_766.94,
+    "genesys_ai_tokens": 0.00,
+}
+
+
+@pytest.fixture(scope="module")
+def composite():
+    return compute_summary(BENEFITS_VERBATIM, COSTS_VERBATIM, 0.10)
+
+
+def test_calendar_mapping():
+    assert YEARS == [2026, 2027, 2028]
+    assert YEAR_INDEX == {2026: 1, 2027: 2, 2028: 3}
+    assert X_LABELS == ["Initial", "2026", "2027", "2028"]
+    assert by_calendar({"1": 10, "2": 20, "3": 30}) == {2026: 10, 2027: 20, 2028: 30}
+
+
+def test_primitives():
+    assert discount_factor(0, 0.10) == 1.0
+    assert discount_factor(1, 0.10) == pytest.approx(1 / 1.1)
+    assert npv([110], 0.10) == pytest.approx(100)
+    assert npv([110], 0.10, initial=-50) == pytest.approx(50)
+    assert roi_pct(14_840_638, 4_057_170) == pytest.approx(265.79, abs=0.1)
+    assert roi_pct(100, 0) == 0.0
+    assert money(10_783_466) == "$10.8M"
+    assert money(-250_000) == "-$250K"
+
+
+def test_payback_edges():
+    assert payback_years(0, [100]) == 0.0
+    assert payback_years(500, []) is None
+    assert payback_years(500, [-100, 200]) is None
+    assert payback_years(300, [-100, 400]) == pytest.approx(2.0)
+    assert payback_months(100, [1_200]) == pytest.approx(1.0)
+    assert payback_label(None) == "beyond 2028"
+    assert payback_label(0.0) == "immediate"
+    assert payback_label(3.3337) == "3.3 months (~Apr 2026)"
+    assert payback_label(14.2) == "14.2 months (~Mar 2027)"
+
+
+def test_per_row_pvs(composite):
+    rows = composite["rows"]["benefits"] + composite["rows"]["costs"]
+    assert len(rows) == 8
+    for row in rows:
+        assert row["pv"] == pytest.approx(ROW_PVS[row["field_key"]], abs=1)
+
+
+def test_composite_totals_engine_exact(composite):
+    assert composite["benefits_pv"] == pytest.approx(14_840_636.81, abs=1)
+    assert composite["costs_pv"] == pytest.approx(4_057_170.40, abs=1)
+    assert composite["npv"] == pytest.approx(10_783_466.42, abs=1)
+    assert composite["roi_pct"] == pytest.approx(265.7879, abs=0.01)
+    assert composite["payback_months"] == pytest.approx(3.3337, abs=0.001)
+    assert composite["initial_costs"] == pytest.approx(1_309_000, abs=0.01)
+
+
+def test_composite_reproduces_published(composite):
+    assert composite["benefits_pv"] == pytest.approx(PUBLISHED["benefits_pv"], abs=5)
+    assert composite["costs_pv"] == pytest.approx(PUBLISHED["costs_pv"], abs=5)
+    assert composite["npv"] == pytest.approx(PUBLISHED["npv"], abs=5)
+    assert round(composite["roi_pct"]) == PUBLISHED["roi_pct"]
+    assert composite["payback_label"] == "3.3 months (~Apr 2026)"
+
+
+def test_yearly_schedules(composite):
+    assert composite["benefits_by_year"][2026] == pytest.approx(5_816_960.00, abs=0.01)
+    assert composite["benefits_by_year"][2027] == pytest.approx(6_054_460.00, abs=0.01)
+    assert composite["benefits_by_year"][2028] == pytest.approx(6_054_460.00, abs=0.01)
+    for y in YEARS:
+        assert composite["costs_by_year"][y] == pytest.approx(1_105_080.00, abs=0.01)
+    assert composite["cumulative_net_by_year"][2026] == pytest.approx(3_402_880.00, abs=0.01)
+    assert composite["cumulative_net_by_year"][2028] == pytest.approx(13_301_640.00, abs=0.01)
+
+
+def test_cross_foots(composite):
+    assert composite["npv"] == pytest.approx(
+        composite["benefits_pv"] - composite["costs_pv"], abs=0.01)
+    for y in YEARS:
+        assert composite["net_by_year"][y] == pytest.approx(
+            composite["benefits_by_year"][y] - composite["costs_by_year"][y], abs=0.01)
+    assert composite["cumulative_net_by_year"][2028] == pytest.approx(
+        sum(composite["net_by_year"].values()) - composite["initial_costs"], abs=0.01)
+    for table, total in (("benefits", "benefits_pv"), ("costs", "costs_pv")):
+        assert sum(r["pv"] for r in composite["rows"][table]) == pytest.approx(
+            composite[total], abs=0.01)