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Robert Helewka a2420ed692 refactor: restructure repo into core/app modules with per-study folders

Reorganize Palladium codebase into a modular architecture with `core/`
shared logic and `app/` Streamlit UI, separating per-study assets into
`studies/YYYYMM_<Vendor>/` folders containing notebooks, seed data, and
configuration. Update README to reflect new structure, add `.gitignore`
entries for `.env` and study exports, and refresh component documentation.

2026-05-20 22:28:12 -04:00

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202602 — Amazon Connect TEI

Self-contained TEI study folder. All data, notebooks, and exports for the Forrester Total Economic Impact™ Of Amazon Connect (February 2026, commissioned by AWS) live here.

Source

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

Key composite numbers reproduced in seed_data.py:

Metric	Value
ROI	342%
NPV	$78.7M
Benefits PV	$101.7M
Costs PV	$23.0M
Payback	<6 months
Discount rate	10%
Analysis period	3 years

Composite organization

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

Layout

202602_AmazonConnect/
├── README.md          ← this file
├── config.py          ← TOOL_PUBLIC_ID, REPORT_PUBLIC_ID, study slug
├── seed_data.py       ← BENEFITS, COSTS, ASSUMPTIONS as Python dicts
├── notebooks/
│   ├── 01_benefits.ipynb       ← quantify the 5 benefits, push to Athena
│   ├── 02_costs.ipynb          ← quantify the 3 costs
│   ├── 03_business_case.ipynb  ← /calculate, charts, scenarios
│   └── 04_export.ipynb         ← /export → exports/export.json
├── exports/           ← generated; .gitignored
└── docs/
    └── 202602_TEI Report Amazon Connect.pdf

Workflow

Set up credentials in the project root .env (see .env.example).
Create / link the TEI tool in Athena, then put its public_id in config.py.
Open notebooks/01_benefits.ipynb and run all — pushes the 5 benefit rows from seed_data.py into Athena.
02_costs.ipynb — pushes the 3 cost rows.
03_business_case.ipynb — calls /calculate, renders the cash flow chart, runs scenario analysis. Should reproduce the PDF's $78.7M NPV / 342% ROI.
04_export.ipynb — writes exports/export.json for the report pipeline.

Adding a new study

Copy this folder, rename to YYYYMM_<Vendor><Solution>, and:

Replace seed_data.py with your benefits/costs.
Update config.py with the new tool/report public IDs.
Tweak the notebooks' narrative; the helper imports are the same.

The only thing that changes between studies is the data and the narrative prose in the notebooks. All math, charts, and API calls come from core/.

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