diff --git a/analysis/axis_classifier.py b/analysis/axis_classifier.py
index d71b932..4fd11c6 100644
--- a/analysis/axis_classifier.py
+++ b/analysis/axis_classifier.py
@@ -11,6 +11,7 @@ from typing import Dict, List, Optional, Tuple
 
 import numpy as np
 import re
+import json
 
 _logger = logging.getLogger(__name__)
 
@@ -145,6 +146,102 @@ def _classify_from_titles(titles: List[str]) -> Tuple[Optional[str], float]:
     return best_cats[0], confidence
 
 
+def _load_motion_vectors(db_path: str, window_id: str) -> Dict[int, np.ndarray]:
+    """Load SVD motion vectors for a given window from DuckDB.
+
+    Returns {motion_id: vector_array}. Returns {} on any error.
+    """
+    try:
+        import duckdb
+
+        conn = duckdb.connect(db_path, read_only=True)
+        rows = conn.execute(
+            "SELECT entity_id, vector FROM svd_vectors "
+            "WHERE entity_type = 'motion' AND window_id = ?",
+            [window_id],
+        ).fetchall()
+        conn.close()
+        result: Dict[int, np.ndarray] = {}
+        for entity_id, vector_raw in rows:
+            try:
+                mid = int(entity_id)
+                vec = np.array(json.loads(vector_raw), dtype=float)
+                result[mid] = vec
+            except Exception:
+                continue
+        return result
+    except Exception as exc:
+        _logger.debug("Failed to load motion vectors for window %s: %s", window_id, exc)
+        return {}
+
+
+def _project_motions(
+    motion_vecs: Dict[int, np.ndarray],
+    x_axis: np.ndarray,
+    y_axis: np.ndarray,
+    global_mean: np.ndarray,
+) -> Dict[int, Tuple[float, float]]:
+    """Project motion vectors onto the PCA axes after centering by global_mean.
+
+    Returns {motion_id: (x_score, y_score)}.
+    """
+    projections: Dict[int, Tuple[float, float]] = {}
+    for mid, vec in motion_vecs.items():
+        try:
+            centered = vec - global_mean
+            x_score = float(np.dot(centered, x_axis))
+            y_score = float(np.dot(centered, y_axis))
+            projections[mid] = (x_score, y_score)
+        except Exception:
+            continue
+    return projections
+
+
+def _top_motion_ids(
+    projections: Dict[int, Tuple[float, float]],
+    axis: str,
+    n: int = 5,
+) -> Dict[str, List[int]]:
+    """Return the top-n motion IDs at each pole of the given axis.
+
+    axis: 'x' or 'y'
+    Returns {'+': [motion_ids], '-': [motion_ids]} (highest positive first,
+    most negative first in the '-' list).
+    """
+    idx = 0 if axis == "x" else 1
+    sorted_ids = sorted(projections, key=lambda mid: projections[mid][idx])
+    neg_ids = sorted_ids[:n]  # most negative
+    pos_ids = sorted_ids[-n:][::-1]  # most positive
+    return {"+": pos_ids, "-": neg_ids}
+
+
+def _fetch_motion_titles(
+    db_path: str,
+    motion_ids: List[int],
+) -> Dict[int, Tuple[str, str]]:
+    """Fetch (title, date) for a list of motion IDs from DuckDB.
+
+    Returns {motion_id: (title, date_str)}. Missing IDs are omitted.
+    Returns {} on any DB error.
+    """
+    if not motion_ids:
+        return {}
+    try:
+        import duckdb
+
+        placeholders = ", ".join("?" * len(motion_ids))
+        conn = duckdb.connect(db_path, read_only=True)
+        rows = conn.execute(
+            f"SELECT id, title, date FROM motions WHERE id IN ({placeholders})",
+            motion_ids,
+        ).fetchall()
+        conn.close()
+        return {int(row[0]): (str(row[1]), str(row[2])) for row in rows}
+    except Exception as exc:
+        _logger.debug("Failed to fetch motion titles: %s", exc)
+        return {}
+
+
 def _load_ideology(csv_path: Path) -> Dict[str, Dict[str, float]]:
     """Load party ideology scores from CSV.