feat: add GenesysCX study and fix Streamlit chart key collisions

- Add 202512_GenesysCX TEI study (config, seed data, notebooks, README)
  with NPV $10.8M / ROI 266% including AI-token cost line
- Add explicit `key` parameter to all chart wrappers in app/components
  to prevent StreamlitDuplicateElementId errors when the same figure
  type renders across Summary/Benefits/Costs tabs
- Render benefits bar and cost pie charts on their respective tabs
- Add benefits_vs_costs_by_year chart wrapper

studies/202512_GenesysCX/ctm-token-calculator/tokencalc/exports.py

@@ -0,0 +1,131 @@
+"""
+Excel / CSV / JSON export.
+
+Excel uses openpyxl via pandas — multi-sheet workbooks readable in
+Excel 2019+. JSON round-trips the full input state (sites, takeouts,
+feature scopes) so a scenario can be saved and reloaded.
+"""
+
+from __future__ import annotations
+
+import dataclasses
+import json
+from pathlib import Path
+
+import pandas as pd
+
+from .inputs import CostTakeout, FeatureScope, SiteInput
+from .meters import Confidence, TokenMeter
+from .rollout import RolloutPlan
+
+
+def meters_dataframe(meters: dict[str, TokenMeter]) -> pd.DataFrame:
+    """Meter catalogue as a display/export-ready DataFrame."""
+    return pd.DataFrame(
+        [
+            {
+                "feature": m.feature,
+                "meter_type": m.meter_type.value,
+                "units_per_token": m.units_per_token or None,
+                "tokens_per_unit": m.tokens_per_unit,
+                "confidence": f"{m.confidence.icon} {m.confidence.value}",
+                "notes": m.notes,
+                "source": m.source_url or "",
+            }
+            for m in meters.values()
+        ]
+    )
+
+
+def sites_dataframe(sites: list[SiteInput]) -> pd.DataFrame:
+    rows = []
+    for s in sites:
+        d = dataclasses.asdict(s)
+        d["languages"] = ", ".join(d["languages"])
+        rows.append(d)
+    return pd.DataFrame(rows)
+
+
+def export_excel(
+    sheets: dict[str, pd.DataFrame],
+    path: str | Path,
+) -> Path:
+    """Write a multi-sheet Excel workbook. Sheet names are truncated to
+    Excel's 31-character limit."""
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with pd.ExcelWriter(path, engine="openpyxl") as writer:
+        for name, df in sheets.items():
+            df.to_excel(writer, sheet_name=name[:31], index=False)
+    return path
+
+
+def export_csv(df: pd.DataFrame, path: str | Path) -> Path:
+    path = Path(path)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    df.to_csv(path, index=False)
+    return path
+
+
+# ── JSON scenario save / load ────────────────────────────────────────
+
+def scenario_state_to_json(
+    sites: list[SiteInput],
+    takeouts: list[CostTakeout],
+    feature_scopes: list[FeatureScope],
+    path: str | Path | None = None,
+    rollout: RolloutPlan | None = None,
+) -> str:
+    """Serialize the full input state; optionally write to ``path``."""
+    state = {
+        "sites": [dataclasses.asdict(s) for s in sites],
+        "takeouts": [
+            {**dataclasses.asdict(t), "confidence": t.confidence.value}
+            for t in takeouts
+        ],
+        "feature_scopes": [
+            {
+                **dataclasses.asdict(f),
+                "adoption_curve": {str(k): v for k, v in f.adoption_curve.items()},
+            }
+            for f in feature_scopes
+        ],
+    }
+    if rollout is not None:
+        state["rollout"] = dataclasses.asdict(rollout)
+    text = json.dumps(state, indent=2)
+    if path is not None:
+        Path(path).write_text(text)
+    return text
+
+
+def scenario_state_from_json(
+    source: str | Path,
+) -> tuple[list[SiteInput], list[CostTakeout], list[FeatureScope], RolloutPlan | None]:
+    """Inverse of :func:`scenario_state_to_json`. ``source`` is a JSON
+    string or a file path. The fourth element is None for legacy files
+    saved without a rollout plan."""
+    raw = (
+        Path(source).read_text()
+        if isinstance(source, Path) or (isinstance(source, str) and source.strip().endswith(".json"))
+        else str(source)
+    )
+    state = json.loads(raw)
+    sites = [SiteInput(**s) for s in state["sites"]]
+    takeouts = [
+        CostTakeout(**{**t, "confidence": Confidence(t["confidence"])})
+        for t in state["takeouts"]
+    ]
+    scopes = [
+        FeatureScope(
+            **{
+                **f,
+                "adoption_curve": {int(k): v for k, v in f["adoption_curve"].items()},
+            }
+        )
+        for f in state["feature_scopes"]
+    ]
+    rollout = (
+        RolloutPlan(**state["rollout"]) if "rollout" in state else None
+    )
+    return sites, takeouts, scopes, rollout