diff --git a/src/forecast/custom_models/constant_models.py b/src/forecast/custom_models/constant_models.py
new file mode 100644
index 0000000..cc3d67a
--- /dev/null
+++ b/src/forecast/custom_models/constant_models.py
@@ -0,0 +1,68 @@
+"""Minimal forecasters for tests and as integration examples.
+
+These mirror the column layout expected by
+:func:`src.forecast.forecast.generate_forecast` and
+:func:`src.evaluation.evaluate.evaluate_forecast` (point column + ``{alias}-q-{pp}``).
+"""
+
+import numpy as np
+import pandas as pd
+from timecopilot.models.utils.forecaster import Forecaster
+
+from src.forecast.constants import QUANTILES
+
+
+class ConstantForecastModel(Forecaster):
+    """Point and quantile forecasts equal a fixed value (default ``1.0``).
+
+    Subclasses :class:`timecopilot.models.utils.forecaster.Forecaster`. Use as a
+    sanity-check baseline and a minimal template for adding models: ``forecast``
+    returns ``unique_id``, ``ds``, ``{alias}``, and ``{alias}-q-10`` … ``{alias}-q-90``.
+    """
+
+    def __init__(self, value: float = 1.0, alias: str = "constant_one") -> None:
+        self.value = float(value)
+        self.alias = alias
+
+    def forecast(
+        self,
+        df: pd.DataFrame,
+        h: int,
+        freq: str | None = None,
+        level: list[int | float] | None = None,
+        quantiles: list[float] | None = None,
+    ) -> pd.DataFrame:
+        if level is not None:
+            raise NotImplementedError(
+                "ConstantForecastModel does not support ``level``"
+            )
+        inferred = self._maybe_infer_freq(df, freq)
+        if not pd.api.types.is_datetime64_any_dtype(df["ds"]):
+            df = df.copy()
+            df["ds"] = pd.to_datetime(df["ds"])
+
+        # Last observed timestamp is shared across series in this benchmark loader.
+        max_ds = df["ds"].max()
+        future = pd.date_range(
+            start=max_ds,
+            periods=h + 1,
+            freq=inferred,
+        )[1:]
+
+        qs = QUANTILES if quantiles is None else quantiles
+        uids = df["unique_id"].unique()
+        n_u, n_f = len(uids), len(future)
+        # Vectorized (series × horizon); no per-row Python loop.
+        out = pd.DataFrame(
+            {
+                "unique_id": np.repeat(uids, n_f),
+                "ds": np.tile(np.asarray(future, dtype="datetime64[ns]"), n_u),
+                self.alias: self.value,
+            }
+        )
+        # Same constant for each quantile column; names match evaluate.py.
+        q_block = {
+            f"{self.alias}-q-{int(round(q * 100))}": np.full(n_u * n_f, self.value)
+            for q in qs
+        }
+        return pd.concat([out, pd.DataFrame(q_block)], axis=1)
diff --git a/src/forecast/forecast.py b/src/forecast/forecast.py
index aad3d7b..cb95249 100644
--- a/src/forecast/forecast.py
+++ b/src/forecast/forecast.py
@@ -18,6 +18,7 @@
     ZeroModel,
 )
 
+from src.forecast.constant_models import ConstantForecastModel
 from src.forecast.constants import QUANTILES
 
 # Model categories for infrastructure selection
@@ -27,6 +28,8 @@
     "zero_model": lambda: ZeroModel(),
     "historic_average": lambda: HistoricAverage(),
     "seasonal_naive": lambda: SeasonalNaive(),
+    # Example
+    "constant_one": lambda: ConstantForecastModel(1.0, alias="constant_one"),
     # Statistical models
     "auto_arima": lambda: AutoARIMA(),
     "auto_ets": lambda: AutoETS(),