diff --git a/ciao/model/__init__.py b/ciao/model/__init__.py
new file mode 100644
index 0000000..dd425b4
--- /dev/null
+++ b/ciao/model/__init__.py
@@ -0,0 +1,6 @@
+"""Model prediction utilities for CIAO."""
+
+from ciao.model.predictor import ModelPredictor
+
+
+__all__ = ["ModelPredictor"]
diff --git a/ciao/model/predictor.py b/ciao/model/predictor.py
new file mode 100644
index 0000000..5efd700
--- /dev/null
+++ b/ciao/model/predictor.py
@@ -0,0 +1,37 @@
+import torch
+
+
+class ModelPredictor:
+    """Handles model predictions and class information for the CIAO explainer."""
+
+    def __init__(self, model: torch.nn.Module, class_names: list[str]) -> None:
+        self.model = model
+        self.class_names = class_names
+
+        # Ensure deterministic inference by disabling Dropout and freezing BatchNorm
+        self.model.eval()
+
+        # Robustly determine the device (fall back to CPU if model has no parameters)
+        try:
+            self.device = next(model.parameters()).device
+        except StopIteration:
+            self.device = torch.device("cpu")
+
+    def get_predictions(self, input_batch: torch.Tensor) -> torch.Tensor:
+        """Get model predictions (returns probabilities)."""
+        input_batch = input_batch.to(self.device)
+
+        with torch.no_grad():
+            outputs = self.model(input_batch)
+            probabilities = torch.nn.functional.softmax(outputs, dim=1)
+            return probabilities
+
+    def get_class_logit_batch(
+        self, input_batch: torch.Tensor, target_class_idx: int
+    ) -> torch.Tensor:
+        """Get raw logits for a specific target class across a batch of images."""
+        input_batch = input_batch.to(self.device)
+
+        with torch.no_grad():
+            outputs = self.model(input_batch)
+            return outputs[:, target_class_idx]