palladium/studies/202602_AmazonConnect/notebooks/01_benefits.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "231c773a",
   "metadata": {},
   "source": [
    "# 01 — Benefits Analysis\n",
    "\n",
    "**Study:** Forrester *Total Economic Impact™ Of Amazon Connect* (Feb 2026)\n",
    "\n",
    "Quantify the five benefit categories Forrester identified for the\n",
    "composite organization, push them into Athena, and verify the totals\n",
    "match the published study (Benefits PV ≈ **$101.7M**)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "110d7e61",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "We add the project root to `sys.path` so the notebook can import `core` and\n",
    "the study's local modules without `pip install -e .`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c83c2758",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Project root: /Users/robert/git/palladium\n",
      "Study root:   /Users/robert/git/palladium/studies/202602_AmazonConnect\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "from pathlib import Path\n",
    "\n",
    "ROOT = Path.cwd().resolve()\n",
    "while ROOT != ROOT.parent and not (ROOT / 'core').is_dir():\n",
    "    ROOT = ROOT.parent\n",
    "if str(ROOT) not in sys.path:\n",
    "    sys.path.insert(0, str(ROOT))\n",
    "\n",
    "STUDY = ROOT / 'studies' / '202602_AmazonConnect'\n",
    "if str(STUDY) not in sys.path:\n",
    "    sys.path.insert(0, str(STUDY))\n",
    "print(f'Project root: {ROOT}')\n",
    "print(f'Study root:   {STUDY}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c371ef85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"padding:10px 14px;border-left:4px solid #0277bd;background:#e1f5fe;color:#1a1a1a;border-radius:4px;margin:6px 0;\">Study: <b>202602_AmazonConnect</b> • discount rate 10% • 3-year horizon</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import config\n",
    "import seed_data\n",
    "from core.calculations import npv, risk_adjust_benefit\n",
    "from core.notebook_helpers import charts, display, tables\n",
    "\n",
    "display.alert(\n",
    "    f'Study: <b>{config.STUDY_SLUG}</b> • discount rate {config.DISCOUNT_RATE:.0%} '\n",
    "    f'• {config.ANALYSIS_YEARS}-year horizon',\n",
    "    'info',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd94503d",
   "metadata": {},
   "source": [
    "## Benefits — nominal & risk-adjusted\n",
    "\n",
    "Forrester quantifies five benefit categories:\n",
    "\n",
    "| Ref | Benefit | Y1 | Y2 | Y3 | Risk Adj |\n",
    "|---|---|---|---|---|---|\n",
    "| At | AI-driven contact resolution efficiency | $13.9M | $23.9M | $37.8M | 15% |\n",
    "| Bt | AI-powered content & sentiment analysis | $4.6M | $5.4M | $6.3M | 15% |\n",
    "| Ct | AI-enabled forecasting & supervision | $6.7M | $9.1M | $12.4M | 15% |\n",
    "| Dt | Data-driven profit lift (conversion +20%) | $1.2M | $1.6M | $2.0M | 20% |\n",
    "| Et | Legacy solution cost savings | $6.2M | $8.0M | $10.4M | 20% |\n",
    "\n",
    "All five are seeded in `seed_data.BENEFITS` with full source notes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6177ea7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_59c3d\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_59c3d_level0_col0\" class=\"col_heading level0 col0\" >field_key</th>\n",
       "      <th id=\"T_59c3d_level0_col1\" class=\"col_heading level0 col1\" >label</th>\n",
       "      <th id=\"T_59c3d_level0_col2\" class=\"col_heading level0 col2\" >category</th>\n",
       "      <th id=\"T_59c3d_level0_col3\" class=\"col_heading level0 col3\" >risk_adjustment</th>\n",
       "      <th id=\"T_59c3d_level0_col4\" class=\"col_heading level0 col4\" >Year 1</th>\n",
       "      <th id=\"T_59c3d_level0_col5\" class=\"col_heading level0 col5\" >Year 1 (RA)</th>\n",
       "      <th id=\"T_59c3d_level0_col6\" class=\"col_heading level0 col6\" >Year 2</th>\n",
       "      <th id=\"T_59c3d_level0_col7\" class=\"col_heading level0 col7\" >Year 2 (RA)</th>\n",
       "      <th id=\"T_59c3d_level0_col8\" class=\"col_heading level0 col8\" >Year 3</th>\n",
       "      <th id=\"T_59c3d_level0_col9\" class=\"col_heading level0 col9\" >Year 3 (RA)</th>\n",
       "      <th id=\"T_59c3d_level0_col10\" class=\"col_heading level0 col10\" >Total</th>\n",
       "      <th id=\"T_59c3d_level0_col11\" class=\"col_heading level0 col11\" >Total (RA)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_59c3d_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_59c3d_row0_col0\" class=\"data row0 col0\" >ai_contact_resolution</td>\n",
       "      <td id=\"T_59c3d_row0_col1\" class=\"data row0 col1\" >AI-driven contact resolution efficiency</td>\n",
       "      <td id=\"T_59c3d_row0_col2\" class=\"data row0 col2\" >Productivity</td>\n",
       "      <td id=\"T_59c3d_row0_col3\" class=\"data row0 col3\" >0.150000</td>\n",
       "      <td id=\"T_59c3d_row0_col4\" class=\"data row0 col4\" >$13,911,040</td>\n",
       "      <td id=\"T_59c3d_row0_col5\" class=\"data row0 col5\" >$11,824,384</td>\n",
       "      <td id=\"T_59c3d_row0_col6\" class=\"data row0 col6\" >$23,932,480</td>\n",
       "      <td id=\"T_59c3d_row0_col7\" class=\"data row0 col7\" >$20,342,608</td>\n",
       "      <td id=\"T_59c3d_row0_col8\" class=\"data row0 col8\" >$37,797,760</td>\n",
       "      <td id=\"T_59c3d_row0_col9\" class=\"data row0 col9\" >$32,128,096</td>\n",
       "      <td id=\"T_59c3d_row0_col10\" class=\"data row0 col10\" >$75,641,280</td>\n",
       "      <td id=\"T_59c3d_row0_col11\" class=\"data row0 col11\" >$64,295,088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_59c3d_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_59c3d_row1_col0\" class=\"data row1 col0\" >ai_content_sentiment</td>\n",
       "      <td id=\"T_59c3d_row1_col1\" class=\"data row1 col1\" >AI-powered content and sentiment analysis savings</td>\n",
       "      <td id=\"T_59c3d_row1_col2\" class=\"data row1 col2\" >Productivity</td>\n",
       "      <td id=\"T_59c3d_row1_col3\" class=\"data row1 col3\" >0.150000</td>\n",
       "      <td id=\"T_59c3d_row1_col4\" class=\"data row1 col4\" >$4,586,620</td>\n",
       "      <td id=\"T_59c3d_row1_col5\" class=\"data row1 col5\" >$3,898,627</td>\n",
       "      <td id=\"T_59c3d_row1_col6\" class=\"data row1 col6\" >$5,358,412</td>\n",
       "      <td id=\"T_59c3d_row1_col7\" class=\"data row1 col7\" >$4,554,650</td>\n",
       "      <td id=\"T_59c3d_row1_col8\" class=\"data row1 col8\" >$6,291,680</td>\n",
       "      <td id=\"T_59c3d_row1_col9\" class=\"data row1 col9\" >$5,347,928</td>\n",
       "      <td id=\"T_59c3d_row1_col10\" class=\"data row1 col10\" >$16,236,712</td>\n",
       "      <td id=\"T_59c3d_row1_col11\" class=\"data row1 col11\" >$13,801,205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_59c3d_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_59c3d_row2_col0\" class=\"data row2 col0\" >ai_forecasting_supervision</td>\n",
       "      <td id=\"T_59c3d_row2_col1\" class=\"data row2 col1\" >AI-enabled forecasting, agent scheduling, and supervision</td>\n",
       "      <td id=\"T_59c3d_row2_col2\" class=\"data row2 col2\" >Productivity</td>\n",
       "      <td id=\"T_59c3d_row2_col3\" class=\"data row2 col3\" >0.150000</td>\n",
       "      <td id=\"T_59c3d_row2_col4\" class=\"data row2 col4\" >$6,651,680</td>\n",
       "      <td id=\"T_59c3d_row2_col5\" class=\"data row2 col5\" >$5,653,928</td>\n",
       "      <td id=\"T_59c3d_row2_col6\" class=\"data row2 col6\" >$9,133,760</td>\n",
       "      <td id=\"T_59c3d_row2_col7\" class=\"data row2 col7\" >$7,763,696</td>\n",
       "      <td id=\"T_59c3d_row2_col8\" class=\"data row2 col8\" >$12,391,712</td>\n",
       "      <td id=\"T_59c3d_row2_col9\" class=\"data row2 col9\" >$10,532,955</td>\n",
       "      <td id=\"T_59c3d_row2_col10\" class=\"data row2 col10\" >$28,177,152</td>\n",
       "      <td id=\"T_59c3d_row2_col11\" class=\"data row2 col11\" >$23,950,579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_59c3d_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_59c3d_row3_col0\" class=\"data row3 col0\" >data_driven_profit_lift</td>\n",
       "      <td id=\"T_59c3d_row3_col1\" class=\"data row3 col1\" >Data-driven profit lift with increased conversion</td>\n",
       "      <td id=\"T_59c3d_row3_col2\" class=\"data row3 col2\" >Revenue</td>\n",
       "      <td id=\"T_59c3d_row3_col3\" class=\"data row3 col3\" >0.200000</td>\n",
       "      <td id=\"T_59c3d_row3_col4\" class=\"data row3 col4\" >$1,200,000</td>\n",
       "      <td id=\"T_59c3d_row3_col5\" class=\"data row3 col5\" >$960,000</td>\n",
       "      <td id=\"T_59c3d_row3_col6\" class=\"data row3 col6\" >$1,560,000</td>\n",
       "      <td id=\"T_59c3d_row3_col7\" class=\"data row3 col7\" >$1,248,000</td>\n",
       "      <td id=\"T_59c3d_row3_col8\" class=\"data row3 col8\" >$2,028,000</td>\n",
       "      <td id=\"T_59c3d_row3_col9\" class=\"data row3 col9\" >$1,622,400</td>\n",
       "      <td id=\"T_59c3d_row3_col10\" class=\"data row3 col10\" >$4,788,000</td>\n",
       "      <td id=\"T_59c3d_row3_col11\" class=\"data row3 col11\" >$3,830,400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_59c3d_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_59c3d_row4_col0\" class=\"data row4 col0\" >legacy_solution_savings</td>\n",
       "      <td id=\"T_59c3d_row4_col1\" class=\"data row4 col1\" >Legacy solution cost savings</td>\n",
       "      <td id=\"T_59c3d_row4_col2\" class=\"data row4 col2\" >Cost Savings</td>\n",
       "      <td id=\"T_59c3d_row4_col3\" class=\"data row4 col3\" >0.200000</td>\n",
       "      <td id=\"T_59c3d_row4_col4\" class=\"data row4 col4\" >$6,177,600</td>\n",
       "      <td id=\"T_59c3d_row4_col5\" class=\"data row4 col5\" >$4,942,080</td>\n",
       "      <td id=\"T_59c3d_row4_col6\" class=\"data row4 col6\" >$8,030,880</td>\n",
       "      <td id=\"T_59c3d_row4_col7\" class=\"data row4 col7\" >$6,424,704</td>\n",
       "      <td id=\"T_59c3d_row4_col8\" class=\"data row4 col8\" >$10,440,144</td>\n",
       "      <td id=\"T_59c3d_row4_col9\" class=\"data row4 col9\" >$8,352,115</td>\n",
       "      <td id=\"T_59c3d_row4_col10\" class=\"data row4 col10\" >$24,648,624</td>\n",
       "      <td id=\"T_59c3d_row4_col11\" class=\"data row4 col11\" >$19,718,899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x11c133a40>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = tables.benefits_table(seed_data.BENEFITS)\n",
    "df.style.format({col: '${:,.0f}' for col in df.columns if col not in ('field_key','label','category','risk_adjustment')})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "573f12d8",
   "metadata": {},
   "source": [
    "## Local validation against the PDF\n",
    "\n",
    "Re-derive the per-benefit risk-adjusted PV and confirm we land on Forrester's\n",
    "**$101,696,791** total within rounding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8cf32003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_a110f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a110f_level0_col0\" class=\"col_heading level0 col0\" >Benefit</th>\n",
       "      <th id=\"T_a110f_level0_col1\" class=\"col_heading level0 col1\" >Y1 (RA)</th>\n",
       "      <th id=\"T_a110f_level0_col2\" class=\"col_heading level0 col2\" >Y2 (RA)</th>\n",
       "      <th id=\"T_a110f_level0_col3\" class=\"col_heading level0 col3\" >Y3 (RA)</th>\n",
       "      <th id=\"T_a110f_level0_col4\" class=\"col_heading level0 col4\" >PV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a110f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_a110f_row0_col0\" class=\"data row0 col0\" >AI-driven contact resolution efficiency</td>\n",
       "      <td id=\"T_a110f_row0_col1\" class=\"data row0 col1\" >$11,824,384</td>\n",
       "      <td id=\"T_a110f_row0_col2\" class=\"data row0 col2\" >$20,342,608</td>\n",
       "      <td id=\"T_a110f_row0_col3\" class=\"data row0 col3\" >$32,128,096</td>\n",
       "      <td id=\"T_a110f_row0_col4\" class=\"data row0 col4\" >$51,699,827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a110f_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_a110f_row1_col0\" class=\"data row1 col0\" >AI-powered content and sentiment analysis savings</td>\n",
       "      <td id=\"T_a110f_row1_col1\" class=\"data row1 col1\" >$3,898,627</td>\n",
       "      <td id=\"T_a110f_row1_col2\" class=\"data row1 col2\" >$4,554,650</td>\n",
       "      <td id=\"T_a110f_row1_col3\" class=\"data row1 col3\" >$5,347,928</td>\n",
       "      <td id=\"T_a110f_row1_col4\" class=\"data row1 col4\" >$11,326,358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a110f_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_a110f_row2_col0\" class=\"data row2 col0\" >AI-enabled forecasting, agent scheduling, and supervision</td>\n",
       "      <td id=\"T_a110f_row2_col1\" class=\"data row2 col1\" >$5,653,928</td>\n",
       "      <td id=\"T_a110f_row2_col2\" class=\"data row2 col2\" >$7,763,696</td>\n",
       "      <td id=\"T_a110f_row2_col3\" class=\"data row2 col3\" >$10,532,955</td>\n",
       "      <td id=\"T_a110f_row2_col4\" class=\"data row2 col4\" >$19,469,777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a110f_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_a110f_row3_col0\" class=\"data row3 col0\" >Data-driven profit lift with increased conversion</td>\n",
       "      <td id=\"T_a110f_row3_col1\" class=\"data row3 col1\" >$960,000</td>\n",
       "      <td id=\"T_a110f_row3_col2\" class=\"data row3 col2\" >$1,248,000</td>\n",
       "      <td id=\"T_a110f_row3_col3\" class=\"data row3 col3\" >$1,622,400</td>\n",
       "      <td id=\"T_a110f_row3_col4\" class=\"data row3 col4\" >$3,123,065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a110f_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_a110f_row4_col0\" class=\"data row4 col0\" >Legacy solution cost savings</td>\n",
       "      <td id=\"T_a110f_row4_col1\" class=\"data row4 col1\" >$4,942,080</td>\n",
       "      <td id=\"T_a110f_row4_col2\" class=\"data row4 col2\" >$6,424,704</td>\n",
       "      <td id=\"T_a110f_row4_col3\" class=\"data row4 col3\" >$8,352,115</td>\n",
       "      <td id=\"T_a110f_row4_col4\" class=\"data row4 col4\" >$16,077,540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a110f_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_a110f_row5_col0\" class=\"data row5 col0\" >TOTAL</td>\n",
       "      <td id=\"T_a110f_row5_col1\" class=\"data row5 col1\" >$27,279,019</td>\n",
       "      <td id=\"T_a110f_row5_col2\" class=\"data row5 col2\" >$40,333,658</td>\n",
       "      <td id=\"T_a110f_row5_col3\" class=\"data row5 col3\" >$57,983,494</td>\n",
       "      <td id=\"T_a110f_row5_col4\" class=\"data row5 col4\" >$101,696,568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x12a29a690>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "rows = []\n",
    "for b in seed_data.BENEFITS:\n",
    "    rf = b['risk_adjustment']\n",
    "    yr = [b['year_values'][str(y)] for y in (1, 2, 3)]\n",
    "    yr_ra = [risk_adjust_benefit(v, rf) for v in yr]\n",
    "    pv = npv(yr_ra, config.DISCOUNT_RATE)\n",
    "    rows.append({\n",
    "        'Benefit': b['label'],\n",
    "        'Y1 (RA)': yr_ra[0],\n",
    "        'Y2 (RA)': yr_ra[1],\n",
    "        'Y3 (RA)': yr_ra[2],\n",
    "        'PV': pv,\n",
    "    })\n",
    "df_check = pd.DataFrame(rows)\n",
    "df_check.loc[len(df_check)] = ['TOTAL', df_check['Y1 (RA)'].sum(), df_check['Y2 (RA)'].sum(), df_check['Y3 (RA)'].sum(), df_check['PV'].sum()]\n",
    "df_check.style.format({c: '${:,.0f}' for c in df_check.columns if c != 'Benefit'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3ded50c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"padding:10px 14px;border-left:4px solid #2e7d32;background:#e8f5e9;color:#1a1a1a;border-radius:4px;margin:6px 0;\">Computed Benefits PV: <b>$101,696,568</b><br>Forrester target:     <b>$101,696,791</b><br>Δ = $-223 (rounding)</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "expected_pv = 101_696_791\n",
    "computed_pv = df_check.iloc[-1]['PV']\n",
    "delta = computed_pv - expected_pv\n",
    "kind = 'success' if abs(delta) < 1_000 else 'warning'\n",
    "display.alert(\n",
    "    f'Computed Benefits PV: <b>${computed_pv:,.0f}</b><br>'\n",
    "    f'Forrester target:     <b>${expected_pv:,.0f}</b><br>'\n",
    "    f'Δ = ${delta:,.0f} (rounding)',\n",
    "    kind,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5ad453a",
   "metadata": {},
   "source": [
    "## Visualize\n",
    "\n",
    "Horizontal bar chart of risk-adjusted three-year totals — mirrors the PDF p.6\n",
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "charts.benefits_bar(seed_data.BENEFITS).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c4591f5",
   "metadata": {},
   "source": [
    "## Push to Athena\n",
    "\n",
    "When `config.TOOL_PUBLIC_ID` is set, persist the seed values to the live\n",
    "TEI tool. Otherwise this cell is a no-op so the notebook still runs\n",
    "offline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d10a54b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"padding:10px 14px;border-left:4px solid #0277bd;background:#e1f5fe;color:#1a1a1a;border-radius:4px;margin:6px 0;\">No TOOL_PUBLIC_ID set in config.py — skipped Athena push. Set <code>PALLADIUM_TOOL_PUBLIC_ID</code> in your environment or edit config.py to enable.</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if config.TOOL_PUBLIC_ID:\n",
    "    from core.tei_client import TEIClient\n",
    "\n",
    "    client = TEIClient()\n",
    "    result = client.update_values(config.TOOL_PUBLIC_ID, seed_data.BENEFITS)\n",
    "    display.alert(f'Pushed {len(seed_data.BENEFITS)} benefit rows to '\n",
    "                  f'tool <code>{config.TOOL_PUBLIC_ID}</code>.', 'success')\n",
    "else:\n",
    "    display.alert(\n",
    "        'No TOOL_PUBLIC_ID set in config.py — skipped Athena push. '\n",
    "        'Set <code>PALLADIUM_TOOL_PUBLIC_ID</code> in your environment '\n",
    "        'or edit config.py to enable.',\n",
    "        'info',\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78693c14",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "Continue with [`02_costs.ipynb`](02_costs.ipynb) →"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}