@@ -0,0 +1,604 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "2b0c5d04",
"metadata": {},
"source": [
"# 00 · Provision — Amazon Connect TEI in Athena\n",
"\n",
"Creates everything this study needs in the Athena sandbox, end to end:\n",
"\n",
"1. **Report template** *Amazon Connect 2026* (3 years, 10% discount rate) + **field definitions**\n",
"2. **Client selection** — browse the CRM, pick the client, and pull their profile (industry, agent counts, revenue) so nothing is re-entered\n",
"3. **Attachment** — pick (or create) the **Proposal or Engagement** the tool binds to\n",
"4. **Tool instance** + **seed values** from `seed_data.py` (the published Forrester figures)\n",
"5. **Server-side calculation** and **verification** against the published totals: **NPV \\$78.7M · ROI 342% · payback <6 months**\n",
"6. Persists all IDs to `.env` so the other notebooks, the CLI, and the Streamlit app pick them up automatically.\n",
"\n",
"Safe to re-run — every step finds existing objects before creating new ones."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5bcc7740",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Athena connected — https://athena.ouranos.helu.ca (1 report templates visible)\n",
"📁 Study: 202602_AmazonConnect\n"
]
}
],
"source": [
"import sys, pathlib # path shim: works on a fresh kernel\n",
"for _p in [pathlib.Path.cwd(), *pathlib.Path.cwd().parents]:\n",
" if (_p / \"pyproject.toml\").exists():\n",
" sys.path.insert(0, str(_p)); break\n",
"\n",
"import pandas as pd\n",
"from core.bootstrap import init, update_env\n",
"\n",
"pal = init(study=\"202602_AmazonConnect\")\n",
"client, seed, config = pal.client, pal.seed_data, pal.config\n",
"assert pal.connection.get(\"status\") == \"ok\", \"Fix the connection first → 00_setup.ipynb\""
]
},
{
"cell_type": "markdown",
"id": "cc8a4e03",
"metadata": {},
"source": [
"## 1 · Report template (find or create)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "386ae38b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found existing report template xsUTbjh4iDnJ (status: active)\n"
]
}
],
"source": [
"REPORT_NAME, VENDOR = \"Amazon Connect 2026\", \"AWS\"\n",
"\n",
"report = next(\n",
" (r for r in client.list_reports()\n",
" if r.get(\"name\") == REPORT_NAME and r.get(\"vendor\") == VENDOR),\n",
" None,\n",
")\n",
"if report is None:\n",
" report = client.create_report(\n",
" name=REPORT_NAME,\n",
" vendor=VENDOR,\n",
" version=\"1.0\",\n",
" description=\"Forrester Total Economic Impact of Amazon Connect, Feb 2026\",\n",
" analysis_period_years=seed.ASSUMPTIONS[\"analysis_years\"],\n",
" discount_rate=seed.ASSUMPTIONS[\"discount_rate\"],\n",
" status=\"draft\",\n",
" )\n",
" print(f\"Created report template {report['id']}\")\n",
"else:\n",
" print(f\"Found existing report template {report['id']} (status: {report.get('status')})\")\n",
"\n",
"REPORT_ID = report[\"id\"]"
]
},
{
"cell_type": "markdown",
"id": "dab83777",
"metadata": {},
"source": [
"## 2 · Field definitions\n",
"\n",
"Derived straight from `seed_data.py`. Three methodology notes:\n",
"\n",
"- **Benefit risk adjustment** lives on the field definition — Athena applies `value × −
"- **Costs**: Athena never risk-adjusts costs, but Forrester adjusts them *upward*. We therefore push cost values pre-multiplied by `(1 + risk_adj)` in step 5, and keep the field-level adjustment at 0 so nothing is applied twice.\n",
"- **Year-0 \"Initial\" amounts** have no native slot in the TEI API, so each cost gets a companion non-annual `<key>_initial` field. The client folds these back automatically on read."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "dc46ab46",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 fields created, 11 already existed.\n"
]
}
],
"source": [
"def field_defs():\n",
" defs, sort = [], 0\n",
" for b in seed.BENEFITS:\n",
" sort += 1\n",
" defs.append({\n",
" \"table\": \"benefits\",\n",
" \"field_key\": b[\"field_key\"],\n",
" \"label\": b[\"label\"],\n",
" \"description\": b[\"notes\"][:200],\n",
" \"field_type\": \"currency\",\n",
" \"category\": b[\"category\"],\n",
" \"is_annual\": True,\n",
" \"risk_adjustment\": str(b[\"risk_adjustment\"]),\n",
" \"sort_order\": sort,\n",
" \"is_required\": True,\n",
" \"source_notes\": b[\"notes\"],\n",
" })\n",
" for c in seed.COSTS:\n",
" sort += 1\n",
" defs.append({\n",
" \"table\": \"costs\",\n",
" \"field_key\": c[\"field_key\"],\n",
" \"label\": c[\"label\"],\n",
" \"description\": c[\"notes\"][:200],\n",
" \"field_type\": \"currency\",\n",
" \"category\": c[\"category\"],\n",
" \"is_annual\": True,\n",
" \"risk_adjustment\": \"0\", # applied client-side, see note above\n",
" \"sort_order\": sort,\n",
" \"is_required\": True,\n",
" \"source_notes\": c[\"notes\"],\n",
" })\n",
" sort += 1\n",
" defs.append({\n",
" \"table\": \"costs\",\n",
" \"field_key\": f\"{c['field_key']}_initial\",\n",
" \"label\": f\"{c['label']} — initial (Year 0)\",\n",
" \"description\": \"One-time Year-0 amount (companion field).\",\n",
" \"field_type\": \"currency\",\n",
" \"category\": c[\"category\"],\n",
" \"is_annual\": False,\n",
" \"risk_adjustment\": \"0\",\n",
" \"sort_order\": sort,\n",
" \"is_required\": False,\n",
" \"source_notes\": \"Year-0 lump sum; Athena treats non-annual values as Year 1.\",\n",
" })\n",
" return defs\n",
"\n",
"existing = {f[\"field_key\"] for f in client.list_fields(REPORT_ID)}\n",
"created = 0\n",
"for d in field_defs():\n",
" if d[\"field_key\"] not in existing:\n",
" client.create_field(REPORT_ID, d)\n",
" created += 1\n",
"print(f\"{created} fields created, {len(existing)} already existed.\")\n",
"\n",
"if report.get(\"status\") == \"draft\":\n",
" client.update_report(REPORT_ID, status=\"active\")\n",
" print(\"Report template activated.\")"
]
},
{
"cell_type": "markdown",
"id": "d5841b4e",
"metadata": {},
"source": [
"## 3 · Select the client\n",
"\n",
"Browse the CRM. Adjust `CLIENT_SEARCH` to narrow the list, then set `CLIENT_ID`\n",
"below (it auto-selects when exactly one client matches)."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "4070b9c2",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'TEIClient' object has no attribute 'list_clients'",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[12]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m CLIENT_SEARCH = \u001b[33m\"Global\"\u001b[39m \u001b[38;5;66;03m# e.g. \"Acme\" — empty lists everyone\u001b[39;00m\n\u001b[32m 2\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m clients = client.list_clients(search=CLIENT_SEARCH \u001b[38;5;28;01mor\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m clients:\n\u001b[32m 5\u001b[39m display(pd.DataFrame(clients)[\n\u001b[32m 6\u001b[39m [c for c in (\"id\", \"name\", \"vertical\", \"client_type\", \"employee_count\",\n",
"\u001b[31mAttributeError\u001b[39m: 'TEIClient' object has no attribute 'list_clients'"
]
}
],
"source": [
"CLIENT_SEARCH = \"\" # e.g. \"Acme\" — empty lists everyone\n",
"\n",
"clients = client.list_clients(search=CLIENT_SEARCH or None)\n",
"if clients:\n",
" display(pd.DataFrame(clients)[\n",
" [c for c in (\"id\", \"name\", \"vertical\", \"client_type\", \"employee_count\",\n",
" \"contact_center_agent_count\", \"supervisor_count\")\n",
" if c in clients[0]]\n",
" ])\n",
"else:\n",
" print(\"No clients found — create one in the Athena UI (Orbit → Clients) and re-run.\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "4e97978c",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'clients' is not defined",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m CLIENT_ID = \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;66;03m# ← set from the `id` column above, or leave for auto-pick\u001b[39;00m\n\u001b[32m 2\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m CLIENT_ID \u001b[38;5;28;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mand\u001b[39;00m len(clients) == \u001b[32m1\u001b[39m:\n\u001b[32m 4\u001b[39m CLIENT_ID = clients[\u001b[32m0\u001b[39m][\u001b[33m\"id\"\u001b[39m]\n\u001b[32m 5\u001b[39m print(f\"Auto-selected the only client: {clients[\u001b[32m0\u001b[39m][\u001b[33m'name'\u001b[39m]} (id={CLIENT_ID})\")\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m CLIENT_ID \u001b[38;5;28;01mis\u001b[39;00m \u001b[38;5;28;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[33m\"Set CLIENT_ID from the table above and re-run this cell.\"\u001b[39m\n",
"\u001b[31mNameError\u001b[39m: name 'clients' is not defined"
]
}
],
"source": [
"CLIENT_ID = None # ← set from the `id` column above, or leave for auto-pick\n",
"\n",
"if CLIENT_ID is None and len(clients) == 1:\n",
" CLIENT_ID = clients[0][\"id\"]\n",
" print(f\"Auto-selected the only client: {clients[0]['name']} (id={CLIENT_ID})\")\n",
"assert CLIENT_ID is not None, \"Set CLIENT_ID from the table above and re-run this cell.\"\n",
"\n",
"profile = client.client_profile(CLIENT_ID)\n",
"CLIENT_NAME = profile[\"name\"]\n",
"print(f\"\\nClient profile — no re-entry needed downstream:\")\n",
"display(pd.DataFrame([profile]).T.rename(columns={0: CLIENT_NAME}))"
]
},
{
"cell_type": "markdown",
"id": "0ae76599",
"metadata": {},
"source": [
"### Client data → study assumptions\n",
"\n",
"Where the CRM has real numbers, they override the Forrester composite\n",
"(2,000 agents / 200 supervisors). `CLIENT_ASSUMPTIONS` is what `01_benefits.ipynb`\n",
"uses for scaling discussions."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "fcccc591",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'profile' is not defined",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m CLIENT_ASSUMPTIONS = dict(seed.ASSUMPTIONS)\n\u001b[32m 2\u001b[39m overrides = {\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[33m\"agents_fte\"\u001b[39m: profile.get(\u001b[33m\"contact_center_agent_count\"\u001b[39m),\n\u001b[32m 4\u001b[39m \u001b[33m\"supervisors_fte\"\u001b[39m: profile.get(\u001b[33m\"supervisor_count\"\u001b[39m),\n\u001b[32m 5\u001b[39m }\n\u001b[32m 6\u001b[39m rows = []\n",
"\u001b[31mNameError\u001b[39m: name 'profile' is not defined"
]
}
],
"source": [
"CLIENT_ASSUMPTIONS = dict(seed.ASSUMPTIONS)\n",
"overrides = {\n",
" \"agents_fte\": profile.get(\"contact_center_agent_count\"),\n",
" \"supervisors_fte\": profile.get(\"supervisor_count\"),\n",
"}\n",
"rows = []\n",
"for key, val in overrides.items():\n",
" if val:\n",
" rows.append({\"assumption\": key, \"Forrester composite\": seed.ASSUMPTIONS[key],\n",
" f\"{CLIENT_NAME} (CRM)\": val})\n",
" CLIENT_ASSUMPTIONS[key] = val\n",
"\n",
"if rows:\n",
" display(pd.DataFrame(rows))\n",
" scale = CLIENT_ASSUMPTIONS[\"agents_fte\"] / seed.ASSUMPTIONS[\"agents_fte\"]\n",
" print(f\"Indicative scale factor vs composite: {scale:.2f}×
" f\"(apply judgement — benefits don't all scale linearly)\")\n",
"else:\n",
" print(\"CRM has no agent/supervisor counts for this client — using the \"\n",
" \"Forrester composite organization as-is.\")"
]
},
{
"cell_type": "markdown",
"id": "422c2ed6",
"metadata": {},
"source": [
"## 4 · Pick the attachment — Proposal or Engagement\n",
"\n",
"A TEI tool must attach to exactly one of the two. Both lists below are\n",
"already filtered to the selected client."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "57dec6cf",
"metadata": {},
"outputs": [],
"source": [
"proposals = client.proposals_for_client(CLIENT_ID)\n",
"engagements = client.engagements_for_client(CLIENT_NAME)\n",
"\n",
"if proposals:\n",
" print(f\"Proposals for {CLIENT_NAME}:\")\n",
" display(pd.DataFrame([\n",
" {\"id\": p[\"id\"], \"name\": p.get(\"name\"), \"status\": p.get(\"status\"),\n",
" \"opportunity\": (p.get(\"opportunity\") or {}).get(\"name\"),\n",
" \"due_date\": p.get(\"due_date\")}\n",
" for p in proposals\n",
" ]))\n",
"if engagements:\n",
" print(f\"Engagements for {CLIENT_NAME}:\")\n",
" display(pd.DataFrame([\n",
" {\"id\": e[\"id\"], \"name\": e.get(\"name\"), \"status\": e.get(\"status\"),\n",
" \"start\": e.get(\"start_date\"), \"end\": e.get(\"end_date\")}\n",
" for e in engagements\n",
" ]))\n",
"if not proposals and not engagements:\n",
" print(f\"{CLIENT_NAME} has no proposals or engagements yet — \"\n",
" \"the next cell can create a sandbox opportunity + proposal.\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "19336bcc",
"metadata": {},
"outputs": [],
"source": [
"# Set exactly ONE of these (ids from the tables above). Leave both None to\n",
"# auto-pick — single existing proposal/engagement wins; otherwise a sandbox\n",
"# opportunity + proposal is created for the client.\n",
"PROPOSAL_ID = config.PROPOSAL_ID # or e.g. 42\n",
"ENGAGEMENT_ID = config.ENGAGEMENT_ID # or e.g. 7\n",
"\n",
"if PROPOSAL_ID is None and ENGAGEMENT_ID is None:\n",
" if len(proposals) == 1 and not engagements:\n",
" PROPOSAL_ID = proposals[0][\"id\"]\n",
" print(f\"Auto-selected proposal {PROPOSAL_ID}: {proposals[0].get('name')}\")\n",
" elif len(engagements) == 1 and not proposals:\n",
" ENGAGEMENT_ID = engagements[0][\"id\"]\n",
" print(f\"Auto-selected engagement {ENGAGEMENT_ID}: {engagements[0].get('name')}\")\n",
" elif not proposals and not engagements:\n",
" opp = client.create_opportunity(\n",
" name=f\"{CLIENT_NAME} — CX Transformation (sandbox)\",\n",
" client_id=CLIENT_ID,\n",
" description=\"Created by Palladium 00_provision for the Amazon Connect TEI.\",\n",
" )\n",
" prop = client.create_proposal(\n",
" name=f\"{CLIENT_NAME} — Amazon Connect TEI (sandbox)\",\n",
" opportunity_id=opp[\"id\"],\n",
" status=\"Draft\",\n",
" )\n",
" PROPOSAL_ID = prop[\"id\"]\n",
" print(f\"Created opportunity {opp['id']} and proposal {PROPOSAL_ID} for {CLIENT_NAME}.\")\n",
" else:\n",
" raise SystemExit(\"Multiple options — set PROPOSAL_ID or ENGAGEMENT_ID above and re-run.\")\n",
"\n",
"assert (PROPOSAL_ID is None) != (ENGAGEMENT_ID is None), \\\n",
" \"Set exactly one of PROPOSAL_ID / ENGAGEMENT_ID.\"\n",
"attach = {\"proposal\": PROPOSAL_ID} if PROPOSAL_ID else {\"engagement\": ENGAGEMENT_ID}\n",
"print(f\"Attaching via: {attach}\")"
]
},
{
"cell_type": "markdown",
"id": "0056bce9",
"metadata": {},
"source": [
"## 5 · Tool instance (find or create) & seed the published values"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "017ae9db",
"metadata": {},
"outputs": [],
"source": [
"from core.tei_client import AthenaAPIError\n",
"\n",
"def _report_id_of(t):\n",
" r = t.get(\"report\")\n",
" return r.get(\"id\") if isinstance(r, dict) else r\n",
"\n",
"def _matches_attachment(t):\n",
" if PROPOSAL_ID is not None:\n",
" opp = t.get(\"opportunity\") or {}\n",
" return t.get(\"proposal\") == PROPOSAL_ID or opp.get(\"proposal_id\") == PROPOSAL_ID\n",
" eng = t.get(\"engagement\")\n",
" eng_id = eng.get(\"id\") if isinstance(eng, dict) else eng\n",
" return eng_id == ENGAGEMENT_ID\n",
"\n",
"candidates = [t for t in client.list_tools() if _report_id_of(t) == REPORT_ID]\n",
"tool = next((t for t in candidates if _matches_attachment(t)),\n",
" candidates[0] if len(candidates) == 1 else None)\n",
"\n",
"if tool is None:\n",
" try:\n",
" tool = client.create_tool(\n",
" report_public_id=REPORT_ID,\n",
" name=f\"{CLIENT_NAME} — Amazon Connect TEI\",\n",
" **attach,\n",
" )\n",
" print(f\"Created tool {tool['id']} attached to {attach}\")\n",
" except AthenaAPIError as e:\n",
" if e.status_code == 409: # DUPLICATE_INSTANCE\n",
" raise SystemExit(\n",
" \"An active tool already exists for this report + attachment. \"\n",
" \"Find it with client.list_tools() or pick a different proposal/engagement.\"\n",
" ) from e\n",
" raise\n",
"else:\n",
" print(f\"Found existing tool {tool['id']} (status: {tool.get('status')})\")\n",
"\n",
"TOOL_ID = tool[\"id\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "20e2a736",
"metadata": {},
"outputs": [],
"source": [
"payload = []\n",
"for b in seed.BENEFITS: # nominal; Athena risk-adjusts via the field definition\n",
" payload.append({\n",
" \"field_key\": b[\"field_key\"],\n",
" \"year_values\": b[\"year_values\"],\n",
" \"notes\": b[\"notes\"],\n",
" })\n",
"for c in seed.COSTS: # risk-adjusted UP client-side (Forrester methodology)\n",
" factor = 1 + c[\"risk_adjustment\"]\n",
" payload.append({\n",
" \"field_key\": c[\"field_key\"],\n",
" \"year_values\": {y: round(v * factor, 2) for y, v in c[\"year_values\"].items()},\n",
" \"initial\": round(c[\"initial\"] * factor, 2),\n",
" \"notes\": c[\"notes\"],\n",
" })\n",
"\n",
"client.update_values(TOOL_ID, payload)\n",
"print(f\"Pushed values for {len(payload)} fields.\")"
]
},
{
"cell_type": "markdown",
"id": "697794fd",
"metadata": {},
"source": [
"## 6 · Calculate & verify against the published study"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b7ac5d24",
"metadata": {},
"outputs": [],
"source": [
"summary = client.calculate(TOOL_ID)\n",
"client.print_summary(TOOL_ID)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "13d84001",
"metadata": {},
"outputs": [],
"source": [
"# Published Forrester totals (3-yr risk-adjusted PV @ 10%)\n",
"PUBLISHED = {\n",
" \"total_benefits_pv\": 101_696_791,\n",
" \"total_costs_pv\": 22_983_076,\n",
" \"net_present_value\": 78_713_715,\n",
" \"roi_percentage\": 342,\n",
"}\n",
"# Tolerance: Athena discounts Year-0 'initial' amounts as Year 1 (Forrester\n",
"# leaves Year 0 undiscounted) — expected drift is ~$0.1M on costs (≈0.15%).\n",
"TOLERANCE = 0.02\n",
"\n",
"rows, ok = [], True\n",
"for key, expected in PUBLISHED.items():\n",
" actual = float(summary.get(key) or 0)\n",
" diff = (actual - expected) / expected\n",
" rows.append({\"metric\": key, \"published\": f\"{expected:,.0f}\",\n",
" \"athena\": f\"{actual:,.0f}\", \"diff\": f\"{diff:+.2%}\"})\n",
" ok &= abs(diff) <= TOLERANCE\n",
"\n",
"display(pd.DataFrame(rows))\n",
"payback = summary.get(\"payback_period_months\")\n",
"print(f\"Payback: {payback} months (published: <6 months)\")\n",
"assert ok, f\"Server totals drifted more than {TOLERANCE:.0%} from the published study — investigate before proceeding.\"\n",
"print(\"✅ Verified — Athena reproduces the published Forrester totals.\")"
]
},
{
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"source": [
"## 7 · Save a baseline version & persist IDs"
]
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"if not client.list_versions(TOOL_ID):\n",
" client.save_version(TOOL_ID, note=\"Baseline — published Forrester TEI figures (Feb 2026), moderate scenario.\")\n",
" print(\"Saved version 1 (baseline).\")\n",
"\n",
"ids = {\n",
" \"PALLADIUM_REPORT_PUBLIC_ID\": REPORT_ID,\n",
" \"PALLADIUM_TOOL_PUBLIC_ID\": TOOL_ID,\n",
"}\n",
"if PROPOSAL_ID is not None:\n",
" ids[\"PALLADIUM_PROPOSAL_ID\"] = str(PROPOSAL_ID)\n",
"if ENGAGEMENT_ID is not None:\n",
" ids[\"PALLADIUM_ENGAGEMENT_ID\"] = str(ENGAGEMENT_ID)\n",
"\n",
"env_path = update_env(**ids)\n",
"print(f\"Saved to {env_path}:\")\n",
"for k, v in ids.items():\n",
" print(f\" {k}={v}\")"
]
},
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"id": "571b48c3",
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"source": [
"## Done\n",
"\n",
"The sandbox now has a live, calculated Amazon Connect TEI tool attached to\n",
"the selected client's proposal/engagement — with the client's CRM profile\n",
"(industry, agent counts, revenue) flowing into the tool automatically.\n",
"\n",
"- **Continue the analysis** → `01_benefits.ipynb` → `04_export.ipynb` (they pick up the IDs from `.env` via `config.py`)\n",
"- **Interactive editing** → `make app` / `streamlit run app/main.py` — the tool appears in the sidebar\n",
"- **CLI sanity check** → `python -m palladium summary $PALLADIUM_TOOL_PUBLIC_ID`"
]
}
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