palladium/studies/202602_TEI_Amazon_Connect

202602 — Amazon Connect TEI

Self-contained reproduction of the Forrester Total Economic Impact™ Of Amazon Connect study (February 2026, commissioned by AWS), built on the Mercury Notebook Deliverable Pattern as Variant 4 — TEI composite reproduction: Forrester's composite organization is the never-edited verbatim anchor, the in-notebook gate proves the engine reproduces the published totals, and 🟡 client drivers rescale the composite live.

Source

The full Forrester study is at docs/202602_TEI Report Amazon Connect.pdf.

Published composite totals (3-yr risk-adjusted PV @ 10%), reproduced by teicalc to within the PDF's own table rounding (benefits PV lands $223 low; costs PV $0.22 low):

Metric	Published	Engine
Benefits PV	$101,696,791	$101,696,568
Costs PV	$22,983,076	$22,983,076
NPV	$78,713,715	$78,713,492
ROI	342%	342.48%
Payback	<6 months	0.7 months

Composite organization (the verbatim anchor 🟢)

• Global B2C, ~$10B revenue (Y1), 30% YoY growth
• 2,000 contact-center agents, 200 supervisors
• 20M annual contacts (75% calls, 25% chat)
• 10-min average handle time

Client overlay (🟡)

A first-order linear rescale — "the composite at your size", not "your TEI". Each published row scales with the driver that dominates its PDF derivation:

Row	Driver	Confidence
AI contact resolution · content/sentiment	contacts	🟡
Forecasting/supervision · legacy savings	agents	🟡
Data-driven profit lift	contacts	🔴 proxy (revenue-driven in the PDF)
Amazon Connect usage	contacts	🟡
Implementation · ongoing management	fixed	🟡 project-based

The client growth rate re-bases the composite's Y1→Y3 trajectory (which embeds 30% YoY). At composite scale the overlay is the identity — the gate asserts it.

Layout

202602_TEI_Amazon_Connect/
├── teicalc/               ← ALL math (stdlib-only) — notebooks hold none
│   ├── anchor.py          ← Forrester's tables, VERBATIM, never edited
│   ├── model.py           ← NPV/ROI/payback, risk adjustment, compute_summary
│   ├── overlay.py         ← ClientDrivers + driver map + overlay_rows
│   ├── scenarios.py       ← conservative / moderate / aggressive
│   └── staging.py         ← on_stage()/backstage() (Mercury vs nbconvert)
├── notebooks/business_case.ipynb   ← THE deliverable
├── scripts/export_report.py
├── tests/                 ← hand-checked pinned acceptance numbers
├── config.toml            ← Mercury theme (NTT DATA brand)
├── pyproject.toml         ← full toolchain as core deps — no requirements.txt
├── docs/                  ← the Forrester PDF
└── exports/               ← generated .html/.md; gitignored

Run

python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

Task	Command
Tests	pytest
Serve (the stage)	mercury --working-dir notebooks/ (run from this project root so config.toml loads)
Analyst view (backstage)	jupyter lab
Headless check	jupyter nbconvert --to notebook --execute --inplace notebooks/business_case.ipynb
Export for LLMs	python scripts/export_report.py

The data appendix (markdown tables + JSON model state) rides inside exports/business_case.md — it replaces the retired exports/export.json pipeline and is the payload for the Athena study-repository roadmap.

History

This study previously ran on the shared core/ package with an Athena-workflow notebook chain (provision → push → calculate → export). That workflow was retired when the study migrated to the pattern (git history preserves it); the engine reproduces the same published totals locally, pinned in tests/.