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_overlay.py

@@ -0,0 +1,95 @@
+"""Client-overlay pins — identity at the composite, linear per-driver
+scaling, the direct AI-token input, and copy semantics."""
+
+import dataclasses
+
+import pytest
+
+from teicalc import (
+    BENEFIT_DRIVERS,
+    BENEFITS_VERBATIM,
+    COMPOSITE,
+    COST_DRIVERS,
+    COSTS_VERBATIM,
+    ClientDrivers,
+    compute_summary,
+    overlay_rows,
+    scale_factor,
+)
+
+
+def _row(rows, key):
+    return next(r for r in rows if r["field_key"] == key)
+
+
+def test_identity_at_composite():
+    """overlay_rows(COMPOSITE) reproduces the verbatim study to the cent."""
+    ob, oc = overlay_rows(COMPOSITE)
+    got = compute_summary(ob, oc, 0.10)
+    want = compute_summary(BENEFITS_VERBATIM, COSTS_VERBATIM, 0.10)
+    assert got["benefits_pv"] == pytest.approx(want["benefits_pv"], abs=0.01)
+    assert got["costs_pv"] == pytest.approx(want["costs_pv"], abs=0.01)
+    assert got["npv"] == pytest.approx(want["npv"], abs=0.01)
+
+
+def test_driver_map_covers_every_row():
+    assert set(BENEFIT_DRIVERS) == {r["field_key"] for r in BENEFITS_VERBATIM}
+    assert set(COST_DRIVERS) == {r["field_key"] for r in COSTS_VERBATIM}
+
+
+def test_scale_factor():
+    d = ClientDrivers(agents_fte=300, weekly_interactions=160_000,
+                      annual_revenue=5_000_000_000)
+    assert scale_factor("agents", d) == pytest.approx(0.5)
+    assert scale_factor("interactions", d) == pytest.approx(2.0)
+    assert scale_factor("revenue", d) == pytest.approx(2.0)
+    assert scale_factor("fixed", d) == 1.0
+    with pytest.raises(KeyError):
+        scale_factor("contacts", d)
+
+
+def test_half_agents_halves_agent_rows_only():
+    ob, oc = overlay_rows(ClientDrivers(agents_fte=300))
+    assert _row(ob, "legacy_retirement")["year_values"]["1"] == pytest.approx(340_000)
+    assert _row(oc, "cx_cloud_licenses")["year_values"]["1"] == pytest.approx(420_000)
+    # Interaction-, revenue-driven, and fixed rows unmoved.
+    assert _row(ob, "self_service_savings")["year_values"]["1"] == pytest.approx(2_329_600)
+    assert _row(ob, "agent_assist_sales")["year_values"]["1"] == pytest.approx(600_000)
+    assert _row(oc, "implementation")["initial"] == 1_190_000
+
+
+def test_double_interactions_doubles_volume_rows_only():
+    ob, oc = overlay_rows(ClientDrivers(weekly_interactions=160_000))
+    assert _row(ob, "self_service_savings")["year_values"]["1"] == pytest.approx(4_659_200)
+    assert _row(ob, "agent_efficiency")["year_values"]["1"] == pytest.approx(5_824_000)
+    assert _row(ob, "legacy_retirement")["year_values"]["1"] == pytest.approx(680_000)
+    assert _row(oc, "cx_cloud_licenses")["year_values"]["1"] == pytest.approx(840_000)
+
+
+def test_double_revenue_doubles_agent_assist_only():
+    ob, _ = overlay_rows(ClientDrivers(annual_revenue=5_000_000_000))
+    assert _row(ob, "agent_assist_sales")["year_values"]["1"] == pytest.approx(1_200_000)
+    assert _row(ob, "self_service_savings")["year_values"]["1"] == pytest.approx(2_329_600)
+
+
+def test_ai_tokens_direct_input():
+    """The token line takes the negotiated annual figure directly (rf 0.0),
+    adding annual × Σ1/1.1ⁿ = 250,000 × 2.48685… ≈ $621,713 to costs PV."""
+    _, oc = overlay_rows(ClientDrivers(ai_tokens_annual=250_000))
+    tokens = _row(oc, "genesys_ai_tokens")
+    assert tokens["year_values"] == {"1": 250_000.0, "2": 250_000.0, "3": 250_000.0}
+    base = compute_summary(BENEFITS_VERBATIM, COSTS_VERBATIM, 0.10)
+    ob, oc = overlay_rows(ClientDrivers(ai_tokens_annual=250_000))
+    got = compute_summary(ob, oc, 0.10)
+    assert got["costs_pv"] - base["costs_pv"] == pytest.approx(621_713.00, abs=1)
+    assert got["benefits_pv"] == pytest.approx(base["benefits_pv"], abs=0.01)
+
+
+def test_drivers_frozen_and_rows_are_copies():
+    with pytest.raises(dataclasses.FrozenInstanceError):
+        COMPOSITE.agents_fte = 1  # type: ignore[misc]
+    ob, oc = overlay_rows(COMPOSITE)
+    ob[0]["year_values"]["1"] = -1
+    oc[0]["year_values"]["1"] = -1
+    assert BENEFITS_VERBATIM[0]["year_values"]["1"] == 680_000
+    assert COSTS_VERBATIM[0]["year_values"]["1"] == 840_000