diff --git a/scripts/backfill_tier.py b/scripts/backfill_tier.py
new file mode 100644
index 0000000..ece998d
--- /dev/null
+++ b/scripts/backfill_tier.py
@@ -0,0 +1,173 @@
+"""PR-B B-1 레거시 문서 tier triage 백필 도구.
+
+사용법 (GPU 서버 fastapi 컨테이너 안에서 실행):
+  docker exec hyungi_document_server-fastapi-1 \\
+      python /app/scripts/backfill_tier.py --domain safety --limit 50 --dry-run
+  docker exec hyungi_document_server-fastapi-1 \\
+      python /app/scripts/backfill_tier.py --domain safety --limit 50 --apply
+
+대상 선정 옵션 (--domain):
+  safety      : ai_domain LIKE 'Industrial_Safety%%'
+  law         : source_channel='law_monitor'
+  manual      : source_channel='manual'
+  news        : source_channel='news' (대부분 짧아 deep escalate 거의 없음)
+  drive_sync  : source_channel='drive_sync'
+  memo        : source_channel='memo'
+
+선정 규칙:
+  ai_analysis_tier IS NULL   — 아직 tier triage 안 탄 문서
+  extracted_text IS NOT NULL — 본문이 있어야 classify 가능
+  deleted_at IS NULL
+  ORDER BY created_at DESC   — 최근 문서 우선 (사용자 관심도 가정)
+
+실행:
+  - --dry-run: SELECT 카운트 + 샘플 5건 출력, 큐 변경 없음
+  - --apply:  classify stage 에 pending 으로 INSERT (ON CONFLICT DO NOTHING)
+             기존 pending/processing 행이 있으면 skip
+
+MLX 부하 고려:
+  legacy classify (primary 26B) + tier triage (4B) + deep_summary (26B escalate 시)
+  ~60~120초/건. 50건 ≈ 1시간. 100건+ 백필은 여러 세션 또는 야간 분할 권장.
+"""
+
+from __future__ import annotations
+
+import argparse
+import asyncio
+import os
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "app"))
+
+from sqlalchemy import text
+from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
+
+
+DOMAIN_FILTERS: dict[str, str] = {
+    "safety":     "ai_domain LIKE 'Industrial_Safety%'",
+    "law":        "source_channel = 'law_monitor'",
+    "manual":     "source_channel = 'manual'",
+    "news":       "source_channel = 'news'",
+    "drive_sync": "source_channel = 'drive_sync'",
+    "memo":       "source_channel = 'memo'",
+}
+
+
+COUNT_SQL = """
+SELECT COUNT(*)
+FROM documents
+WHERE deleted_at IS NULL
+  AND extracted_text IS NOT NULL
+  AND ai_analysis_tier IS NULL
+  AND {filter}
+"""
+
+SAMPLE_SQL = """
+SELECT id, LEFT(title, 60) AS title, ai_domain, source_channel,
+       LENGTH(extracted_text) AS tlen, created_at::date AS created
+FROM documents
+WHERE deleted_at IS NULL
+  AND extracted_text IS NOT NULL
+  AND ai_analysis_tier IS NULL
+  AND {filter}
+ORDER BY created_at DESC
+LIMIT :n
+"""
+
+APPLY_SQL = """
+INSERT INTO processing_queue (document_id, stage, status, attempts, max_attempts)
+SELECT id, 'classify', 'pending', 0, 3
+FROM documents
+WHERE deleted_at IS NULL
+  AND extracted_text IS NOT NULL
+  AND ai_analysis_tier IS NULL
+  AND {filter}
+ORDER BY created_at DESC
+LIMIT :n
+ON CONFLICT DO NOTHING
+RETURNING document_id
+"""
+
+
+async def run(domain: str, limit: int, apply: bool) -> int:
+    if domain not in DOMAIN_FILTERS:
+        print(f"[error] --domain 은 {list(DOMAIN_FILTERS)} 중 하나여야 함. got={domain!r}")
+        return 1
+
+    filter_clause = DOMAIN_FILTERS[domain]
+    database_url = os.getenv(
+        "DATABASE_URL",
+        "postgresql+asyncpg://pkm:pkm@postgres:5432/pkm",
+    )
+
+    engine = create_async_engine(database_url)
+    session_factory = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
+
+    async with session_factory() as session:
+        total = (await session.execute(
+            text(COUNT_SQL.format(filter=filter_clause))
+        )).scalar()
+        print(f"=== {domain} 백필 후보 ===")
+        print(f"  총 미처리 문서: {total} 건")
+        print(f"  이번 실행 대상 limit: {limit} 건")
+        print()
+
+        if total == 0:
+            print("[done] 백필할 문서 없음.")
+            await engine.dispose()
+            return 0
+
+        print("=== 샘플 (상위 5건) ===")
+        rows = (await session.execute(
+            text(SAMPLE_SQL.format(filter=filter_clause)),
+            {"n": 5},
+        )).all()
+        for r in rows:
+            print(f"  id={r.id}  ({r.created})  [{r.source_channel} / {r.ai_domain or '-'}]  "
+                  f"tlen={r.tlen}  title={r.title}")
+        print()
+
+        if not apply:
+            print(f"[dry-run] --apply 로 실제 enqueue 수행. 예상 {min(limit, total)} 건.")
+            await engine.dispose()
+            return 0
+
+        print(f"[apply] classify 큐에 {limit} 건 enqueue...")
+        result = await session.execute(
+            text(APPLY_SQL.format(filter=filter_clause)),
+            {"n": limit},
+        )
+        enqueued_ids = [r.document_id for r in result.all()]
+        await session.commit()
+        print(f"  enqueue 성공: {len(enqueued_ids)} 건")
+        if enqueued_ids:
+            sample = enqueued_ids[:10]
+            print(f"  첫 10건 id: {sample}")
+        print()
+        print("경과 관찰 쿼리:")
+        print("  docker exec hyungi_document_server-postgres-1 psql -U pkm -d pkm -c \\")
+        print("    \"SELECT stage, status, COUNT(*) FROM processing_queue \\")
+        print("      WHERE created_at > NOW() - INTERVAL '3 hours' GROUP BY 1,2 ORDER BY 1,2\"")
+
+    await engine.dispose()
+    return 0
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="PR-B tier triage 백필 도구")
+    parser.add_argument("--domain", required=True, choices=list(DOMAIN_FILTERS),
+                        help="백필 대상 도메인 선택")
+    parser.add_argument("--limit", type=int, default=50,
+                        help="한 번에 enqueue 할 최대 건수 (기본 50)")
+    mode = parser.add_mutually_exclusive_group(required=True)
+    mode.add_argument("--dry-run", action="store_true", help="쿼리만 실행, 큐 변경 없음")
+    mode.add_argument("--apply", action="store_true", help="classify 큐 실제 enqueue")
+    args = parser.parse_args()
+
+    rc = asyncio.run(run(args.domain, args.limit, args.apply))
+    sys.exit(rc)
+
+
+if __name__ == "__main__":
+    main()