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>
This commit is contained in:
2026-07-09 16:38:51 -04:00
parent a420af230b
commit e88449d15a
54 changed files with 8462 additions and 6427 deletions

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"""Export the deliverable notebooks as LLM-readable report sources.
Executes each notebook fresh (widget defaults — or whatever defaults you edit in),
then writes both formats to exports/:
exports/<notebook>.html — human-reviewable, tables render
exports/<notebook>.md — leanest LLM input
Plotly figures export as JavaScript an LLM cannot read; each notebook's
machine-readable appendix section carries every number behind them.
Run from the project root: python scripts/export_report.py [name-filter]
An optional argument exports only notebooks whose filename contains it.
"""
from __future__ import annotations
import subprocess
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parent.parent
NOTEBOOKS = [
ROOT / "notebooks" / "business_case.ipynb",
]
EXPORTS = ROOT / "exports"
def main() -> None:
picked = [nb for nb in NOTEBOOKS
if len(sys.argv) < 2 or sys.argv[1] in nb.name]
if not picked:
sys.exit(f"no notebook matches {sys.argv[1]!r}")
EXPORTS.mkdir(exist_ok=True)
for nb in picked:
for fmt in ("html", "markdown"):
subprocess.run(
[sys.executable, "-m", "nbconvert", "--execute",
"--to", fmt, "--output-dir", str(EXPORTS), str(nb)],
check=True, cwd=ROOT,
)
for p in sorted(EXPORTS.iterdir()):
if p.suffix in (".html", ".md"):
print(f"wrote {p.relative_to(ROOT)} ({p.stat().st_size / 1024:,.0f} KB)")
if __name__ == "__main__":
main()