Robert Helewka
e88449d15a
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>
96 lines
3.9 KiB
Python
96 lines
3.9 KiB
Python
"""Client-overlay pins — identity at the composite, linear per-driver
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scaling, the direct AI-token input, and copy semantics."""
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import dataclasses
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import pytest
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from teicalc import (
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BENEFIT_DRIVERS,
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BENEFITS_VERBATIM,
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COMPOSITE,
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COST_DRIVERS,
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COSTS_VERBATIM,
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ClientDrivers,
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compute_summary,
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overlay_rows,
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scale_factor,
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)
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def _row(rows, key):
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return next(r for r in rows if r["field_key"] == key)
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def test_identity_at_composite():
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"""overlay_rows(COMPOSITE) reproduces the verbatim study to the cent."""
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ob, oc = overlay_rows(COMPOSITE)
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got = compute_summary(ob, oc, 0.10)
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want = compute_summary(BENEFITS_VERBATIM, COSTS_VERBATIM, 0.10)
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assert got["benefits_pv"] == pytest.approx(want["benefits_pv"], abs=0.01)
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assert got["costs_pv"] == pytest.approx(want["costs_pv"], abs=0.01)
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assert got["npv"] == pytest.approx(want["npv"], abs=0.01)
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def test_driver_map_covers_every_row():
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assert set(BENEFIT_DRIVERS) == {r["field_key"] for r in BENEFITS_VERBATIM}
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assert set(COST_DRIVERS) == {r["field_key"] for r in COSTS_VERBATIM}
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def test_scale_factor():
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d = ClientDrivers(agents_fte=300, weekly_interactions=160_000,
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annual_revenue=5_000_000_000)
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assert scale_factor("agents", d) == pytest.approx(0.5)
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assert scale_factor("interactions", d) == pytest.approx(2.0)
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assert scale_factor("revenue", d) == pytest.approx(2.0)
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assert scale_factor("fixed", d) == 1.0
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with pytest.raises(KeyError):
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scale_factor("contacts", d)
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def test_half_agents_halves_agent_rows_only():
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ob, oc = overlay_rows(ClientDrivers(agents_fte=300))
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assert _row(ob, "legacy_retirement")["year_values"]["1"] == pytest.approx(340_000)
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assert _row(oc, "cx_cloud_licenses")["year_values"]["1"] == pytest.approx(420_000)
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# Interaction-, revenue-driven, and fixed rows unmoved.
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assert _row(ob, "self_service_savings")["year_values"]["1"] == pytest.approx(2_329_600)
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assert _row(ob, "agent_assist_sales")["year_values"]["1"] == pytest.approx(600_000)
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assert _row(oc, "implementation")["initial"] == 1_190_000
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def test_double_interactions_doubles_volume_rows_only():
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ob, oc = overlay_rows(ClientDrivers(weekly_interactions=160_000))
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assert _row(ob, "self_service_savings")["year_values"]["1"] == pytest.approx(4_659_200)
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assert _row(ob, "agent_efficiency")["year_values"]["1"] == pytest.approx(5_824_000)
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assert _row(ob, "legacy_retirement")["year_values"]["1"] == pytest.approx(680_000)
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assert _row(oc, "cx_cloud_licenses")["year_values"]["1"] == pytest.approx(840_000)
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def test_double_revenue_doubles_agent_assist_only():
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ob, _ = overlay_rows(ClientDrivers(annual_revenue=5_000_000_000))
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assert _row(ob, "agent_assist_sales")["year_values"]["1"] == pytest.approx(1_200_000)
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assert _row(ob, "self_service_savings")["year_values"]["1"] == pytest.approx(2_329_600)
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def test_ai_tokens_direct_input():
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"""The token line takes the negotiated annual figure directly (rf 0.0),
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adding annual × Σ1/1.1ⁿ = 250,000 × 2.48685… ≈ $621,713 to costs PV."""
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_, oc = overlay_rows(ClientDrivers(ai_tokens_annual=250_000))
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tokens = _row(oc, "genesys_ai_tokens")
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assert tokens["year_values"] == {"1": 250_000.0, "2": 250_000.0, "3": 250_000.0}
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base = compute_summary(BENEFITS_VERBATIM, COSTS_VERBATIM, 0.10)
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ob, oc = overlay_rows(ClientDrivers(ai_tokens_annual=250_000))
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got = compute_summary(ob, oc, 0.10)
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assert got["costs_pv"] - base["costs_pv"] == pytest.approx(621_713.00, abs=1)
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assert got["benefits_pv"] == pytest.approx(base["benefits_pv"], abs=0.01)
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def test_drivers_frozen_and_rows_are_copies():
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with pytest.raises(dataclasses.FrozenInstanceError):
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COMPOSITE.agents_fte = 1 # type: ignore[misc]
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ob, oc = overlay_rows(COMPOSITE)
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ob[0]["year_values"]["1"] = -1
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oc[0]["year_values"]["1"] = -1
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assert BENEFITS_VERBATIM[0]["year_values"]["1"] == 680_000
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assert COSTS_VERBATIM[0]["year_values"]["1"] == 840_000
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