diff --git a/README.md b/README.md
index 54c7b36..24e43b0 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# Perch Hoplite
+# Hoplite Vector Database
 
 Hoplite is a system for storing large volumes of embeddings from machine
 perception models. We focus on combining vector search with active learning
diff --git a/hoplite/agile/classifier.py b/hoplite/agile/classifier.py
index f1f4edc..17000a7 100644
--- a/hoplite/agile/classifier.py
+++ b/hoplite/agile/classifier.py
@@ -15,15 +15,57 @@
 
 """Functions for training and applying a linear classifier."""
 
-from typing import Any
+import base64
+import dataclasses
+from typing import Any, Sequence
 
 from hoplite.agile import classifier_data
 from hoplite.agile import metrics
+from hoplite.db import interface as db_interface
+from hoplite.taxonomy import namespace
+from ml_collections import config_dict
 import numpy as np
 import tensorflow as tf
 import tqdm
 
 
+@dataclasses.dataclass
+class LinearClassifier:
+  """Wrapper for linear classifier params and metadata."""
+
+  beta: np.ndarray
+  beta_bias: np.ndarray
+  classes: tuple[str, ...]
+  embedding_model_config: Any
+
+  def __call__(self, embeddings: np.ndarray):
+    return np.dot(self.beta, embeddings) + self.beta_bias
+
+  def save(self, path: str):
+    """Save the classifier to a path."""
+    cfg = config_dict.ConfigDict()
+    cfg.model_config = self.embedding_model_config
+    cfg.classes = self.classes
+    # Convert numpy arrays to base64 encoded blobs.
+    beta_bytes = base64.b64encode(self.beta.tobytes())
+    beta_bias_bytes = base64.b64encode(self.beta_bias.tobytes())
+    cfg.beta = beta_bytes
+    cfg.beta_bias = beta_bias_bytes
+    with open(path, 'wb') as f:
+      f.write(cfg.to_json())
+
+  def load(self, path: str):
+    """Load a classifier from a path."""
+    with open(path, 'rb') as f:
+      cfg = config_dict.ConfigDict.from_json(f.read())
+    self.beta = np.frombuffer(base64.b64decode(cfg.beta), dtype=np.float32)
+    self.beta_bias = np.frombuffer(
+        base64.b64decode(cfg.beta_bias), dtype=np.float32
+    )
+    self.classes = cfg.classes
+    self.embedding_model_config = cfg.model_config
+
+
 def get_linear_model(embedding_dim: int, num_classes: int) -> tf.keras.Model:
   """Create a simple linear Keras model."""
   model = tf.keras.Sequential([
@@ -105,7 +147,7 @@ def train_linear_classifier(
     learning_rate: float,
     weak_neg_weight: float,
     num_train_steps: int,
-):
+) -> tuple[LinearClassifier, dict[str, float]]:
   """Train a linear classifier."""
   embedding_dim = data_manager.db.embedding_dimension()
   num_classes = len(data_manager.get_target_labels())
@@ -147,4 +189,58 @@ def train_step(y_true, embeddings, is_labeled_mask):
       'beta_bias': lin_model.get_weights()[1],
   }
   eval_scores = eval_classifier(params, data_manager, eval_idxes)
-  return params, eval_scores
+
+  model_config = data_manager.db.get_metadata('model_config')
+  linear_classifier = LinearClassifier(
+      beta=params['beta'],
+      beta_bias=params['beta_bias'],
+      classes=data_manager.get_target_labels(),
+      embedding_model_config=model_config,
+  )
+  return linear_classifier, eval_scores
+
+
+def write_inference_csv(
+    linear_classifier: LinearClassifier,
+    db: db_interface.HopliteDBInterface,
+    output_filepath: str,
+    threshold: float,
+    labels: Sequence[str] | None = None,
+):
+  """Write a CSV for all audio windows with logits above a threshold.
+
+  Args:
+    params: The parameters of the linear classifier.
+    class_list: The class list of labels associated with the classifier.
+    db: HopliteDBInterface to read embeddings from.
+    output_filepath: Path to write the CSV to.
+    threshold: Logits must be above this value to be written.
+    labels: If provided, only write logits for these labels. If None, write
+      logits for all labels.
+
+  Returns:
+    None
+  """
+  idxes = db.get_embedding_ids()
+  if labels is None:
+    labels = linear_classifier.classes
+  label_ids = {cl: i for i, cl in enumerate(linear_classifier.classes)}
+  target_label_ids = np.array([label_ids[l] for l in labels])
+  logits_fn = lambda emb: linear_classifier(emb)[target_label_ids]
+  with open(output_filepath, 'w') as f:
+    f.write('idx,dataset_name,source_id,offset,label,logits\n')
+    for idx in tqdm.tqdm(idxes):
+      source = db.get_embedding_source(idx)
+      emb = db.get_embedding(idx)
+      logits = logits_fn(emb)
+      for a in np.argwhere(logits > threshold):
+        lbl = labels[a]
+        row = [
+            idx,
+            source.dataset_name,
+            source.source_id,
+            source.offsets[0],
+            lbl,
+            logits[a],
+        ]
+        f.write(','.join(map(str, row)) + '\n')