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..bb75c3c
--- /dev/null
+++ b/ciao/model/predictor.py
@@ -0,0 +1,44 @@
+import torch
+
+
+class ModelPredictor:
+    """Handles model predictions and class information."""
+
+    def __init__(self, model: torch.nn.Module, class_names: list[str]) -> None:
+        self.model = model
+        self.class_names = class_names
+        self.device = next(model.parameters()).device
+
+    def get_predictions(self, input_batch: torch.Tensor) -> torch.Tensor:
+        """Get model predictions (returns probabilities)."""
+        with torch.no_grad():
+            outputs = self.model(input_batch)
+            probabilities = torch.nn.functional.softmax(outputs, dim=1)
+            return probabilities
+
+    def predict_image(
+        self, input_batch: torch.Tensor, top_k: int = 5
+    ) -> list[tuple[int, str, float]]:
+        """Get top-k predictions for an image."""
+        probabilities = self.get_predictions(input_batch)
+        top_probs, top_indices = torch.topk(probabilities[0], top_k)
+
+        results = []
+        for i in range(top_k):
+            class_idx = int(top_indices[i].item())
+            prob = float(top_probs[i].item())
+            class_name = (
+                self.class_names[class_idx]
+                if class_idx < len(self.class_names)
+                else f"class_{class_idx}"
+            )
+            results.append((class_idx, class_name, prob))
+        return results
+
+    def get_class_logit_batch(
+        self, input_batch: torch.Tensor, target_class_idx: int
+    ) -> torch.Tensor:
+        """Get logits for a batch of images - optimized for batched inference (directly from model outputs)."""
+        with torch.no_grad():
+            outputs = self.model(input_batch)
+            return outputs[:, target_class_idx]