diff --git a/scripts/classifier_adapters.py b/scripts/classifier_adapters.py
index 3b3933c..e2a8c9c 100644
--- a/scripts/classifier_adapters.py
+++ b/scripts/classifier_adapters.py
@@ -510,4 +510,17 @@ class EmbeddingKNNAdapter(ClassifierAdapter):
         self._orch_url_used = ""
 
     def classify(self, subject: str, body: str) -> str:
-        raise NotImplementedError
+        if not self._exemplar_embeddings:
+            self.load()
+        text = f"Subject: {subject}\n\n{body[:600]}"
+        [query_vec] = self._embed(self._node_url, [text])
+        scored: list[tuple[float, str]] = [
+            (_cosine(query_vec, vec), label)
+            for label, vecs in self._exemplar_embeddings.items()
+            for vec in vecs
+        ]
+        top_k = sorted(scored, reverse=True)[: self._k]
+        votes: dict[str, list[float]] = {}
+        for score, label in top_k:
+            votes.setdefault(label, []).append(score)
+        return max(votes, key=lambda lbl: sum(votes[lbl]))
diff --git a/tests/test_classifier_adapters.py b/tests/test_classifier_adapters.py
index feec32a..3163362 100644
--- a/tests/test_classifier_adapters.py
+++ b/tests/test_classifier_adapters.py
@@ -536,3 +536,104 @@ def test_unload_skips_delete_on_ollama_fallback_path():
     assert len(delete_calls) == 0
     assert adapter._exemplar_embeddings == {}
     assert adapter._node_url == ""
+
+
+# ---- EmbeddingKNNAdapter.classify() tests ----
+
+def _adapter_with_embeddings(exemplar_embeddings, k=3):
+    """Return a pre-loaded EmbeddingKNNAdapter (bypass load()) with given per-label vectors."""
+    from scripts.classifier_adapters import EmbeddingKNNAdapter
+    adapter = EmbeddingKNNAdapter(
+        "test", "nomic-embed-text", k=k,
+        orch_url="http://orch:7700", ollama_url="http://ollama:11434",
+    )
+    adapter._exemplar_embeddings = exemplar_embeddings
+    adapter._node_url = "http://navi:11434"
+    return adapter
+
+
+def _embed_resp(vec):
+    """Return a mock httpx response for /v1/embeddings returning a single vector."""
+    from unittest.mock import MagicMock
+    resp = MagicMock()
+    resp.raise_for_status.return_value = None
+    resp.json.return_value = {"data": [{"embedding": vec}]}
+    return resp
+
+
+def test_classify_returns_majority_vote_label():
+    from unittest.mock import patch
+    adapter = _adapter_with_embeddings({
+        "rejected": [[1.0, 0.0, 0.0], [0.9, 0.1, 0.0], [0.85, 0.15, 0.0]],
+        "neutral":  [[0.0, 1.0, 0.0]],
+    }, k=3)
+
+    # Query [1,0,0] is closest to all three "rejected" exemplars
+    with patch("httpx.post", return_value=_embed_resp([1.0, 0.0, 0.0])):
+        result = adapter.classify("We went with others", "Thank you for applying.")
+
+    assert result == "rejected"
+
+
+def test_classify_tiebreak_by_mean_score():
+    from unittest.mock import patch
+    # k=2: each label gets exactly 1 vote → tie-break by mean similarity
+    # [1,0] query: cosine to [1,0] = 1.0 ("rejected"), cosine to [0.6,0.8] ≈ 0.6 ("neutral")
+    adapter = _adapter_with_embeddings({
+        "rejected": [[1.0, 0.0]],
+        "neutral":  [[0.6, 0.8]],
+    }, k=2)
+
+    with patch("httpx.post", return_value=_embed_resp([1.0, 0.0])):
+        result = adapter.classify("Rejection", "Sorry")
+
+    assert result == "rejected"
+
+
+def test_classify_sparse_label_can_win():
+    from unittest.mock import patch
+    # "hired" has only 1 exemplar; query vector is closest to it
+    adapter = _adapter_with_embeddings({
+        "rejected": [[0.0, 0.0, 1.0], [0.0, 0.1, 0.9]],
+        "hired":    [[1.0, 0.0, 0.0]],
+    }, k=3)
+
+    # Query [1,0,0] → hired exemplar scores 1.0; rejected exemplars score ~0
+    with patch("httpx.post", return_value=_embed_resp([1.0, 0.0, 0.0])):
+        result = adapter.classify("Welcome aboard", "Your first day details")
+
+    assert result == "hired"
+
+
+def test_classify_lazy_loads_when_not_loaded():
+    from unittest.mock import patch
+    from scripts.classifier_adapters import EmbeddingKNNAdapter
+
+    exemplars = {"rejected": ["We went with others"]}
+    adapter = EmbeddingKNNAdapter(
+        "test", "nomic-embed-text", k=1,
+        orch_url="http://orch:7700", ollama_url="http://ollama:11434",
+        exemplar_texts=exemplars,
+    )
+    assert adapter._exemplar_embeddings == {}
+
+    post_urls = []
+    def mock_post(url, *, json=None, timeout=None, **kwargs):
+        post_urls.append(url)
+        from unittest.mock import MagicMock
+        resp = MagicMock()
+        resp.raise_for_status.return_value = None
+        if "/allocate" in url:
+            resp.status_code = 200
+            resp.json.return_value = {"allocation_id": "a1", "url": "http://navi:11434"}
+        else:
+            n = len((json or {}).get("input", []))
+            resp.json.return_value = {"data": [{"embedding": [1.0, 0.0]}] * n}
+        return resp
+
+    with patch("httpx.post", side_effect=mock_post):
+        result = adapter.classify("Rejection", "Sorry")
+
+    assert result == "rejected"
+    assert any("/allocate" in u for u in post_urls), "lazy load must call allocate"
+    assert adapter._exemplar_embeddings != {}