diff --git a/scripts/ingest_conversations.py b/scripts/ingest_conversations.py
new file mode 100644
index 0000000..0e752f7
--- /dev/null
+++ b/scripts/ingest_conversations.py
@@ -0,0 +1,156 @@
+"""
+Nightly ingestion of Aaron AI conversations into pgvector.
+Chunks by exchange (user + assistant together), deduplicates by conversation_id + index.
+Only indexes conversations with more than 3 exchanges.
+"""
+import sqlite3
+import psycopg2
+import json
+import os
+from pathlib import Path
+from datetime import datetime
+from dotenv import load_dotenv
+from sentence_transformers import SentenceTransformer
+
+load_dotenv(Path.home() / "aaronai" / ".env")
+
+CONVERSATIONS_DB = str(Path.home() / "aaronai" / "conversations.db")
+PG_DSN = os.getenv("PG_DSN", "dbname=aaronai user=aaronai password=aaronai_db_password host=localhost")
+MIN_EXCHANGES = 3
+
+print("Loading embedding model...")
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+def get_conversations():
+    conn = sqlite3.connect(CONVERSATIONS_DB)
+    conn.row_factory = sqlite3.Row
+    c = conn.cursor()
+    c.execute("""
+        SELECT c.id, c.title, c.created_at, c.updated_at,
+               COUNT(m.id) as message_count
+        FROM conversations c
+        JOIN messages m ON m.conversation_id = c.id
+        GROUP BY c.id
+        HAVING message_count >= ?
+        ORDER BY c.updated_at DESC
+    """, (MIN_EXCHANGES * 2,))
+    convos = c.fetchall()
+    conn.close()
+    return convos
+
+def get_messages(conversation_id):
+    conn = sqlite3.connect(CONVERSATIONS_DB)
+    conn.row_factory = sqlite3.Row
+    c = conn.cursor()
+    c.execute("""
+        SELECT role, content, timestamp
+        FROM messages
+        WHERE conversation_id = ?
+        ORDER BY timestamp ASC
+    """, (conversation_id,))
+    msgs = c.fetchall()
+    conn.close()
+    return msgs
+
+def chunk_conversation(convo, messages):
+    chunks = []
+    title = convo['title'] or 'Untitled'
+    conv_id = convo['id']
+    
+    # Pair user + assistant exchanges
+    pairs = []
+    i = 0
+    msgs = list(messages)
+    while i < len(msgs):
+        if msgs[i]['role'] == 'user':
+            user_msg = msgs[i]['content']
+            asst_msg = msgs[i+1]['content'] if i+1 < len(msgs) and msgs[i+1]['role'] == 'assistant' else ''
+            pairs.append((user_msg, asst_msg))
+            i += 2
+        else:
+            i += 1
+    
+    # Sliding window of 2 exchanges
+    for idx in range(0, len(pairs), 2):
+        window = pairs[idx:idx+2]
+        text_parts = []
+        for user, asst in window:
+            if user:
+                text_parts.append(f"You: {user[:500]}")
+            if asst:
+                text_parts.append(f"Aaron AI: {asst[:500]}")
+        
+        if not text_parts:
+            continue
+            
+        chunk_text = f"[Aaron AI conversation: {title}]\n\n" + "\n\n".join(text_parts)
+        chunk_id = f"aaronai_conv_{conv_id}_{idx}"
+        chunks.append((chunk_id, chunk_text, {
+            "source": f"Aaron AI: {title}",
+            "type": "aaronai_conversation",
+            "created_at": convo['updated_at'],
+        }))
+    
+    return chunks
+
+def run():
+    pg = psycopg2.connect(PG_DSN)
+    cur = pg.cursor()
+    
+    conversations = get_conversations()
+    print(f"Found {len(conversations)} conversations with {MIN_EXCHANGES}+ exchanges")
+    
+    total_added = 0
+    total_skipped = 0
+    
+    for convo in conversations:
+        messages = get_messages(convo['id'])
+        chunks = chunk_conversation(convo, messages)
+        
+        if not chunks:
+            continue
+        
+        ids = [c[0] for c in chunks]
+        
+        # Check which already exist
+        cur.execute("SELECT id FROM embeddings WHERE id = ANY(%s)", (ids,))
+        existing = set(r[0] for r in cur.fetchall())
+        new_chunks = [c for c in chunks if c[0] not in existing]
+        
+        if not new_chunks:
+            total_skipped += len(chunks)
+            continue
+        
+        # Embed and insert
+        texts = [c[1] for c in new_chunks]
+        embeddings = embedder.encode(texts, show_progress_bar=False).tolist()
+        
+        for (chunk_id, chunk_text, meta), embedding in zip(new_chunks, embeddings):
+            cur.execute("""
+                INSERT INTO embeddings (id, document, embedding, source, type, created_at, metadata)
+                VALUES (%s, %s, %s::vector, %s, %s, %s, %s)
+                ON CONFLICT (id) DO UPDATE SET
+                    document = EXCLUDED.document,
+                    embedding = EXCLUDED.embedding,
+                    source = EXCLUDED.source,
+                    type = EXCLUDED.type,
+                    created_at = EXCLUDED.created_at
+            """, (
+                chunk_id, chunk_text, embedding,
+                meta['source'], meta['type'], meta['created_at'],
+                json.dumps(meta)
+            ))
+        
+        pg.commit()
+        total_added += len(new_chunks)
+        total_skipped += len(existing)
+    
+    cur.execute("SELECT COUNT(*) FROM embeddings")
+    total = cur.fetchone()[0]
+    pg.close()
+    
+    print(f"Done: {total_added} chunks added, {total_skipped} already indexed")
+    print(f"Corpus total: {total} chunks")
+
+if __name__ == "__main__":
+    run()