diff --git a/apps/pre-processing-service/app/api/endpoints/blog.py b/apps/pre-processing-service/app/api/endpoints/blog.py
index 85da62b2..68a23496 100644
--- a/apps/pre-processing-service/app/api/endpoints/blog.py
+++ b/apps/pre-processing-service/app/api/endpoints/blog.py
@@ -7,6 +7,7 @@
 from ...service.blog.blogger_blog_post_adapter import (
     BloggerBlogPostAdapter,
 )  # 수정된 import
+from app.utils.response import Response
 
 router = APIRouter()
 
@@ -36,44 +37,45 @@ async def publish(request: RequestBlogPublish):
     """
     if request.tag == "naver":
         naver_service = NaverBlogPostService()
-        result = naver_service.post_content(
+        response_data = naver_service.post_content(
             title=request.post_title,
             content=request.post_content,
             tags=request.post_tags,
         )
 
-        if not result:
+        if not response_data:
             raise CustomException(
                 "네이버 블로그 포스팅에 실패했습니다.", status_code=500
             )
-        return ResponseBlogPublish(status="success", metadata=result)
+
+        return Response.ok(response_data)
 
     elif request.tag == "tistory":
         tistory_service = TistoryBlogPostService()
-        result = tistory_service.post_content(
+        response_data = tistory_service.post_content(
             title=request.post_title,
             content=request.post_content,
             tags=request.post_tags,
         )
 
-        if not result:
+        if not response_data:
             raise CustomException(
                 "티스토리 블로그 포스팅에 실패했습니다.", status_code=500
             )
 
-        return ResponseBlogPublish(status="success", metadata=result)
+        return Response.ok(response_data)
 
     elif request.tag == "blogger":
         blogger_service = BloggerBlogPostAdapter()  # 수정: Adapter 사용
-        result = blogger_service.post_content(
+        response_data = blogger_service.post_content(
             title=request.post_title,
             content=request.post_content,
             tags=request.post_tags,
         )
 
-        if not result:
+        if not response_data:
             raise CustomException(
                 "블로거 블로그 포스팅에 실패했습니다.", status_code=500
             )
 
-        return ResponseBlogPublish(status="success", metadata=result)
+        return Response.ok(response_data)
diff --git a/apps/pre-processing-service/app/api/endpoints/keywords.py b/apps/pre-processing-service/app/api/endpoints/keywords.py
index a1028391..6c1627bd 100644
--- a/apps/pre-processing-service/app/api/endpoints/keywords.py
+++ b/apps/pre-processing-service/app/api/endpoints/keywords.py
@@ -6,11 +6,6 @@
 router = APIRouter()
 
 
-@router.get("/")
-async def root():
-    return {"message": "keyword API"}
-
-
 @router.post(
     "/search", response_model=ResponseNaverSearch, summary="네이버 키워드 검색"
 )
@@ -28,15 +23,3 @@ async def search(request: RequestNaverSearch):
     """
     response_data = await keyword_search(request)
     return response_data
-
-
-@router.post(
-    "/ssadagu/validate",
-    response_model=ResponseNaverSearch,
-    summary="사다구몰 키워드 검증",
-)
-async def ssadagu_validate(request: RequestNaverSearch):
-    """
-    사다구몰 키워드 검증 테스트용 엔드포인트
-    """
-    return ResponseNaverSearch()
diff --git a/apps/pre-processing-service/app/api/endpoints/product.py b/apps/pre-processing-service/app/api/endpoints/product.py
index ab309595..32a4dcbe 100644
--- a/apps/pre-processing-service/app/api/endpoints/product.py
+++ b/apps/pre-processing-service/app/api/endpoints/product.py
@@ -24,12 +24,12 @@ async def search(request: RequestSadaguSearch):
     """
     try:
         search_service = SearchService()
-        result = await search_service.search_products(request)
+        response_data = await search_service.search_products(request)
 
-        if not result:
+        if not response_data:
             raise CustomException(500, "상품 검색에 실패했습니다.", "SEARCH_FAILED")
 
-        return result
+        return response_data
     except InvalidItemDataException as e:
         raise HTTPException(status_code=e.status_code, detail=e.detail)
     except Exception as e:
@@ -43,12 +43,12 @@ async def match(request: RequestSadaguMatch):
     """
     try:
         match_service = MatchService()
-        result = match_service.match_products(request)
+        response_data = match_service.match_products(request)
 
-        if not result:
+        if not response_data:
             raise CustomException(500, "상품 매칭에 실패했습니다.", "MATCH_FAILED")
 
-        return result
+        return response_data
     except InvalidItemDataException as e:
         raise HTTPException(status_code=e.status_code, detail=e.detail)
     except Exception as e:
@@ -64,14 +64,14 @@ async def similarity(request: RequestSadaguSimilarity):
     """
     try:
         similarity_service = SimilarityService()
-        result = similarity_service.select_product_by_similarity(request)
+        response_data = similarity_service.select_product_by_similarity(request)
 
-        if not result:
+        if not response_data:
             raise CustomException(
                 500, "유사도 분석에 실패했습니다.", "SIMILARITY_FAILED"
             )
 
-        return result
+        return response_data
     except InvalidItemDataException as e:
         raise HTTPException(status_code=e.status_code, detail=e.detail)
     except Exception as e:
@@ -87,12 +87,12 @@ async def crawl(body: RequestSadaguCrawl):
     """
     try:
         crawl_service = CrawlService()
-        result = await crawl_service.crawl_product_detail(body)
+        response_data = await crawl_service.crawl_product_detail(body)
 
-        if not result:
+        if not response_data:
             raise CustomException(500, "상품 크롤링에 실패했습니다.", "CRAWL_FAILED")
 
-        return result
+        return response_data
     except InvalidItemDataException as e:
         raise HTTPException(status_code=e.status_code, detail=e.detail)
     except ItemNotFoundException as e:
diff --git a/apps/pre-processing-service/app/model/schemas.py b/apps/pre-processing-service/app/model/schemas.py
index 9581ad0f..18d0d99f 100644
--- a/apps/pre-processing-service/app/model/schemas.py
+++ b/apps/pre-processing-service/app/model/schemas.py
@@ -1,7 +1,10 @@
 from datetime import datetime
-from typing import Optional, List, Dict, Any
+from typing import Optional, List, Dict, Any, TypeVar, Generic
 from pydantic import BaseModel, Field, HttpUrl
 
+# 제네릭 타입 변수 정의
+T = TypeVar("T")
+
 
 # 기본 요청
 class RequestBase(BaseModel):
@@ -9,46 +12,60 @@ class RequestBase(BaseModel):
 
 
 # 기본 응답
-class ResponseBase(BaseModel):
+class ResponseBase(BaseModel, Generic[T]):
+    success: bool = Field(..., title="성공유무", description="true,false")
+    data: T = Field(..., title="응답 데이터")
     status: str = Field(..., title="상태", description="요청 처리 상태")
-    pass
+    message: str = Field(..., title="메시지", description="메시지입니다.")
+
+
+# ============== 1단계: 네이버 키워드 추출 ==============
 
 
-# 네이버 키워드 추출
 class RequestNaverSearch(RequestBase):
     tag: str = Field(..., title="태그", description="데이터랩/스토어 태그 구분")
-    category: Optional[str] = Field(
-        None, title="카테고리", description="검색할 카테고리"
-    )
-    start_date: Optional[str] = Field(
-        None, title="시작일", description="검색 시작 날짜 (YYYY-MM-DD)"
-    )
-    end_date: Optional[str] = Field(
-        None, title="종료일", description="검색 종료 날짜 (YYYY-MM-DD)"
-    )
 
 
-class ResponseNaverSearch(ResponseBase):
-    category: Optional[str] = Field(None, title="카테고리", description="검색 카테고리")
+# 응답 데이터 모델
+class NaverSearchData(BaseModel):
     keyword: str = Field(..., title="키워드", description="검색에 사용된 키워드")
     total_keyword: Dict[int, str] = Field(
         ..., title="총 키워드", description="키워드별 총 검색 결과"
     )
 
 
-# 2단계: 검색
+# 최종 응답 모델
+class ResponseNaverSearch(ResponseBase[NaverSearchData]):
+    """네이버 키워드 검색 API 응답"""
+
+    pass
+
+
+# ============== 2단계: 사다구 검색 ==============
+
+
 class RequestSadaguSearch(RequestBase):
     keyword: str = Field(..., title="검색 키워드", description="상품을 검색할 키워드")
 
 
-class ResponseSadaguSearch(ResponseBase):
+# 응답 데이터 모델
+class SadaguSearchData(BaseModel):
     keyword: str = Field(..., title="검색 키워드", description="검색에 사용된 키워드")
     search_results: List[Dict] = Field(
         ..., title="검색 결과", description="검색된 상품 목록"
     )
 
 
-# 3단계: 매칭
+# 최종 응답 모델
+class ResponseSadaguSearch(ResponseBase[SadaguSearchData]):
+    """사다구 상품 검색 API 응답"""
+
+    pass
+
+
+# ============== 3단계: 사다구 매칭 ==============
+
+
 class RequestSadaguMatch(RequestBase):
     keyword: str = Field(..., title="매칭 키워드", description="상품과 매칭할 키워드")
     search_results: List[Dict] = Field(
@@ -56,14 +73,24 @@ class RequestSadaguMatch(RequestBase):
     )
 
 
-class ResponseSadaguMatch(ResponseBase):
+# 응답 데이터 모델
+class SadaguMatchData(BaseModel):
     keyword: str = Field(..., title="매칭 키워드", description="매칭에 사용된 키워드")
     matched_products: List[Dict] = Field(
         ..., title="매칭된 상품", description="키워드와 매칭된 상품 목록"
     )
 
 
-# 4단계: 유사도
+# 최종 응답 모델
+class ResponseSadaguMatch(ResponseBase[SadaguMatchData]):
+    """사다구 상품 매칭 API 응답"""
+
+    pass
+
+
+# ============== 4단계: 사다구 유사도 ==============
+
+
 class RequestSadaguSimilarity(RequestBase):
     keyword: str = Field(
         ..., title="유사도 분석 키워드", description="유사도 분석할 키워드"
@@ -78,7 +105,8 @@ class RequestSadaguSimilarity(RequestBase):
     )
 
 
-class ResponseSadaguSimilarity(ResponseBase):
+# 응답 데이터 모델
+class SadaguSimilarityData(BaseModel):
     keyword: str = Field(
         ..., title="분석 키워드", description="유사도 분석에 사용된 키워드"
     )
@@ -90,7 +118,16 @@ class ResponseSadaguSimilarity(ResponseBase):
     )
 
 
-# 사다구몰 크롤링
+# 최종 응답 모델
+class ResponseSadaguSimilarity(ResponseBase[SadaguSimilarityData]):
+    """사다구 상품 유사도 분석 API 응답"""
+
+    pass
+
+
+# ============== 사다구몰 크롤링 ==============
+
+
 class RequestSadaguCrawl(RequestBase):
     tag: str = Field(
         ...,
@@ -102,7 +139,8 @@ class RequestSadaguCrawl(RequestBase):
     )
 
 
-class ResponseSadaguCrawl(ResponseBase):
+# 응답 데이터 모델
+class SadaguCrawlData(BaseModel):
     tag: str = Field(..., title="크롤링 태그", description="크롤링 유형 태그")
     product_url: str = Field(..., title="상품 URL", description="크롤링된 상품 URL")
     product_detail: Optional[Dict] = Field(
@@ -113,30 +151,79 @@ class ResponseSadaguCrawl(ResponseBase):
     )
 
 
-# 블로그 콘텐츠 생성
-class RequestBlogCreate(RequestBase):
+# 최종 응답 모델
+class ResponseSadaguCrawl(ResponseBase[SadaguCrawlData]):
+    """사다구몰 크롤링 API 응답"""
+
     pass
 
 
-class ResponseBlogCreate(ResponseBase):
+# ============== 블로그 콘텐츠 생성 ==============
+
+
+class RequestBlogCreate(RequestBase):
+    keyword: Optional[str] = Field(
+        None, title="키워드", description="콘텐츠 생성용 키워드"
+    )
+    product_info: Optional[Dict] = Field(
+        None, title="상품 정보", description="블로그 콘텐츠에 포함할 상품 정보"
+    )
+    content_type: Optional[str] = Field(
+        None, title="콘텐츠 타입", description="생성할 콘텐츠 유형"
+    )
+    target_length: Optional[int] = Field(
+        None, title="목표 글자 수", description="생성할 콘텐츠의 목표 길이"
+    )
+
+
+# 응답 데이터 모델
+class BlogCreateData(BaseModel):
+    title: str = Field(..., title="블로그 제목", description="생성된 블로그 제목")
+    content: str = Field(..., title="블로그 내용", description="생성된 블로그 내용")
+    tags: List[str] = Field(
+        default_factory=list, title="추천 태그", description="콘텐츠에 적합한 태그 목록"
+    )
+
+
+# 최종 응답 모델
+class ResponseBlogCreate(ResponseBase[BlogCreateData]):
+    """블로그 콘텐츠 생성 API 응답"""
+
     pass
 
 
-# 블로그 배포
+# ============== 블로그 배포 ==============
+
+
 class RequestBlogPublish(RequestBase):
     tag: str = Field(..., title="블로그 태그", description="블로그 플랫폼 종류")
     blog_id: str = Field(..., description="블로그 아이디")
     blog_pw: str = Field(..., description="블로그 비밀번호")
     post_title: str = Field(..., description="포스팅 제목")
     post_content: str = Field(..., description="포스팅 내용")
-    post_tags: List[str] = Field(default=[], description="포스팅 태그 목록")
+    post_tags: List[str] = Field(default_factory=list, description="포스팅 태그 목록")
 
 
-class ResponseBlogPublish(ResponseBase):
-    # 디버깅 용
+# 응답 데이터 모델
+class BlogPublishData(BaseModel):
+    tag: str = Field(..., title="블로그 태그", description="블로그 플랫폼 종류")
+    post_title: str = Field(..., title="포스팅 제목", description="배포된 포스팅 제목")
+    post_url: Optional[str] = Field(
+        None, title="포스팅 URL", description="배포된 포스팅 URL"
+    )
+    published_at: Optional[str] = Field(
+        None, title="배포 시간", description="포스팅 배포 완료 시간"
+    )
+    publish_success: bool = Field(..., title="배포 성공 여부")
+
+    # 디버깅 용 (Optional로 변경)
     metadata: Optional[Dict[str, Any]] = Field(
         None, description="포스팅 관련 메타데이터"
     )
 
-    # 프로덕션 용
-    # post_url: str = Field(..., description="포스팅 URL")
+
+# 최종 응답 모델
+class ResponseBlogPublish(ResponseBase[BlogPublishData]):
+    """블로그 배포 API 응답"""
+
+    pass
diff --git a/apps/pre-processing-service/app/service/crawl_service.py b/apps/pre-processing-service/app/service/crawl_service.py
index 548df05d..f54d4db7 100644
--- a/apps/pre-processing-service/app/service/crawl_service.py
+++ b/apps/pre-processing-service/app/service/crawl_service.py
@@ -3,6 +3,7 @@
 from app.errors.CustomException import InvalidItemDataException
 from app.model.schemas import RequestSadaguCrawl
 from loguru import logger
+from app.utils.response import Response
 
 
 class CrawlService:
@@ -36,16 +37,15 @@ async def crawl_product_detail(self, request: RequestSadaguCrawl) -> dict:
             )
 
             # 응답 데이터 구성
-            response_data = {
+            data = {
                 "tag": request.tag,
                 "product_url": str(request.product_url),
                 "product_detail": product_detail,
-                "status": "success",
                 "crawled_at": time.strftime("%Y-%m-%d %H:%M:%S"),
             }
 
             logger.info(f"상품 상세 크롤링 서비스 완료: status=success")
-            return response_data
+            return Response.ok(data)
 
         except Exception as e:
             logger.error(
diff --git a/apps/pre-processing-service/app/service/keyword_service.py b/apps/pre-processing-service/app/service/keyword_service.py
index f8065fa3..01d61f37 100644
--- a/apps/pre-processing-service/app/service/keyword_service.py
+++ b/apps/pre-processing-service/app/service/keyword_service.py
@@ -1,11 +1,11 @@
 import json
 import random
-
+from app.utils.response import Response
 import httpx
-from starlette import status
 
 from ..errors.CustomException import InvalidItemDataException
 from ..model.schemas import RequestNaverSearch
+from datetime import date, timedelta
 
 
 async def keyword_search(request: RequestNaverSearch) -> dict:
@@ -16,9 +16,7 @@ async def keyword_search(request: RequestNaverSearch) -> dict:
 
     # 키워드 검색
     if request.tag == "naver":
-        trending_keywords = await search_naver_rank(
-            **request.model_dump(include={"category", "start_date", "end_date"})
-        )
+        trending_keywords = await search_naver_rank()
     elif request.tag == "naver_store":
         trending_keywords = await search_naver_store()
     else:
@@ -27,14 +25,14 @@ async def keyword_search(request: RequestNaverSearch) -> dict:
     if not trending_keywords:
         raise InvalidItemDataException()
 
-    response_data = request.model_dump()
-    response_data["keyword"] = random.choice(list(trending_keywords.values()))
-    response_data["total_keyword"] = trending_keywords
-    response_data["status"] = "success"
-    return response_data
+    data = {
+        "keyword": random.choice(list(trending_keywords.values())),
+        "total_keyword": trending_keywords,
+    }
+    return Response.ok(data)
 
 
-async def search_naver_rank(category, start_date, end_date) -> dict[int, str]:
+async def search_naver_rank() -> dict[int, str]:
     """
     네이버 데이터 랩 키워드 검색 모듈
     """
@@ -44,6 +42,26 @@ async def search_naver_rank(category, start_date, end_date) -> dict[int, str]:
         "Referer": "https://datalab.naver.com/shoppingInsight/sCategory.naver",
         "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36",
     }
+    categorys = [
+        "50000000",
+        "50000001",
+        "50000002",
+        "50000003",
+        "50000004",
+        "50000005",
+        "50000006",
+        "50000007",
+        "50000008",
+        "50000009",
+    ]
+    category = random.choice(categorys)
+    today = date.today()
+    yesterday = today - timedelta(days=1)
+
+    # 3. 원하는 포맷(YYYY-MM-DD)으로 변환하기
+    end_date = today.strftime("%Y-%m-%d")
+    start_date = yesterday.strftime("%Y-%m-%d")
+
     keywords_dic = {}
     async with httpx.AsyncClient() as client:
         for page in range(1, 3):
@@ -80,7 +98,6 @@ async def search_naver_store() -> dict[int, str]:
     """
     url = "https://snxbest.naver.com/api/v1/snxbest/keyword/rank?ageType=ALL&categoryId=A&sortType=KEYWORD_POPULAR&periodType=DAILY"
     headers = {}
-
     async with httpx.AsyncClient() as client:
         try:
             # API에 GET 요청을 보냅니다.
diff --git a/apps/pre-processing-service/app/service/match_service.py b/apps/pre-processing-service/app/service/match_service.py
index 9f340683..bb6e88e2 100644
--- a/apps/pre-processing-service/app/service/match_service.py
+++ b/apps/pre-processing-service/app/service/match_service.py
@@ -2,6 +2,7 @@
 from app.errors.CustomException import InvalidItemDataException
 from ..model.schemas import RequestSadaguMatch
 from loguru import logger
+from app.utils.response import Response
 
 
 class MatchService:
@@ -15,21 +16,16 @@ def match_products(self, request: RequestSadaguMatch) -> dict:
         keyword = request.keyword
         products = request.search_results
 
-        logger.info(
-            # f"키워드 매칭 서비스 시작: job_id={request.job_id}, schedule_id={request.schedule_id}, keyword='{keyword}', products_count={len(products) if products else 0}"
-            f"keyword='{keyword}'"
-        )
+        logger.info(f"키워드 매칭 서비스 시작: keyword='{keyword}'")
 
         if not products:
             logger.warning(f"매칭할 상품이 없음: keyword='{keyword}'")
-            return {
-                # "job_id": request.job_id,
-                # "schedule_id": request.schedule_id,
-                # "schedule_his_id": request.schedule_his_id,
+
+            data = {
                 "keyword": keyword,
                 "matched_products": [],
-                "status": "success",
             }
+            return Response.ok(data, "매칭상품이 존재하지 않습니다.")
 
         try:
             matcher = KeywordMatcher()
@@ -79,18 +75,12 @@ def match_products(self, request: RequestSadaguMatch) -> dict:
                 logger.info(
                     f"최고 매칭 상품: title='{best_match['title'][:30]}', score={best_match['match_info']['match_score']:.3f}"
                 )
-
-            return {
-                # "job_id": request.job_id,
-                # "schedule_id": request.schedule_id,
-                # "schedule_his_id": request.schedule_his_id,
+            data = {
                 "keyword": keyword,
                 "matched_products": matched_products,
-                "status": "success",
             }
+            return Response.ok(data)
 
         except Exception as e:
-            logger.error(
-                # f"매칭 서비스 오류: job_id={request.job_id}, keyword='{keyword}', error='{e}'"
-            )
+            logger.error(f"매칭 서비스 오류: error='{e}'")
             raise InvalidItemDataException()
diff --git a/apps/pre-processing-service/app/service/search_service.py b/apps/pre-processing-service/app/service/search_service.py
index a71d6a8d..171bd57f 100644
--- a/apps/pre-processing-service/app/service/search_service.py
+++ b/apps/pre-processing-service/app/service/search_service.py
@@ -2,6 +2,9 @@
 from app.errors.CustomException import InvalidItemDataException
 from ..model.schemas import RequestSadaguSearch
 from loguru import logger
+from app.utils.response import Response
+from datetime import datetime
+import time
 
 
 class SearchService:
@@ -15,11 +18,11 @@ async def search_products(self, request: RequestSadaguSearch) -> dict:
         keyword = request.keyword
         crawler = SearchCrawler(use_selenium=True)
 
+        # 시작 시간 기록
+        start_time = time.time()
+
         try:
-            logger.info(
-                # f"상품 검색 서비스 시작: job_id={request.job_id}, schedule_id={request.schedule_id}, keyword='{keyword}'"
-                f"keyword='{keyword}'"
-            )
+            logger.info(f"keyword='{keyword}'")
 
             # Selenium 또는 httpx로 상품 검색
             if crawler.use_selenium:
@@ -29,11 +32,13 @@ async def search_products(self, request: RequestSadaguSearch) -> dict:
 
             if not search_results:
                 logger.warning(f"검색 결과가 없습니다: keyword='{keyword}'")
-                return {
+
+                # SadaguSearchData 구조에 맞춰 response_data 생성
+                data = {
                     "keyword": keyword,
                     "search_results": [],
-                    "status": "success",
                 }
+                return Response.ok(data, "검색 결과가 없습니다.")
 
             # 상품별 기본 정보 수집 (제목이 없는 경우 다시 크롤링)
             enriched_results = []
@@ -83,15 +88,20 @@ async def search_products(self, request: RequestSadaguSearch) -> dict:
                     )
                     continue
 
+            # 검색 소요 시간 계산
+            search_time_ms = int((time.time() - start_time) * 1000)
+            logger.info(f"검색 소요 시간 = {search_time_ms}")
+
             logger.success(
                 f"상품 검색 완료: keyword='{keyword}', 초기검색={len(search_results)}개, 최종유효상품={len(enriched_results)}개"
             )
 
-            return {
+            # SadaguSearchData 구조에 맞춰 response_data 생성
+            data = {
                 "keyword": keyword,
                 "search_results": enriched_results,
-                "status": "success",
             }
+            return Response.ok(data)
 
         except Exception as e:
             logger.error(f"검색 서비스 오류: keyword='{keyword}', error='{e}'")
diff --git a/apps/pre-processing-service/app/service/similarity_service.py b/apps/pre-processing-service/app/service/similarity_service.py
index c77aa8ba..516b0c63 100644
--- a/apps/pre-processing-service/app/service/similarity_service.py
+++ b/apps/pre-processing-service/app/service/similarity_service.py
@@ -2,6 +2,7 @@
 from app.errors.CustomException import InvalidItemDataException
 from ..model.schemas import RequestSadaguSimilarity
 from loguru import logger
+from app.utils.response import Response
 
 
 class SimilarityService:
@@ -26,12 +27,13 @@ def select_product_by_similarity(self, request: RequestSadaguSimilarity) -> dict
                 logger.warning(
                     f"매칭된 상품과 검색 결과가 모두 없음: keyword='{keyword}'"
                 )
-                return {
+
+                data = {
                     "keyword": keyword,
                     "selected_product": None,
                     "reason": "매칭된 상품과 검색 결과가 모두 없음",
-                    "status": "success",
                 }
+                return Response.ok(data, "매칭된 상품과 검색 결과가 모두 없습니다.")
 
             logger.info("매칭된 상품 없음 → 전체 검색 결과에서 유사도 분석")
             candidates = fallback_products
@@ -63,12 +65,14 @@ def select_product_by_similarity(self, request: RequestSadaguSimilarity) -> dict
                         logger.warning(
                             f"단일 상품 유사도 미달: similarity={similarity:.4f} < threshold={similarity_threshold}"
                         )
-                        return {
+                        data = {
                             "keyword": keyword,
                             "selected_product": None,
                             "reason": f"단일 상품 유사도({similarity:.4f}) < 기준({similarity_threshold})",
-                            "status": "success",
                         }
+                        return Response.ok(
+                            data, "단일 상품 유사도 미달 되어 상품이 존재하지않습니다."
+                        )
 
                 selected_product["similarity_info"] = {
                     "similarity_score": float(similarity),
@@ -79,13 +83,12 @@ def select_product_by_similarity(self, request: RequestSadaguSimilarity) -> dict
                 logger.success(
                     f"단일 상품 선택 완료: title='{selected_product['title'][:30]}', similarity={similarity:.4f}"
                 )
-
-                return {
+                data = {
                     "keyword": keyword,
                     "selected_product": selected_product,
                     "reason": f"단일 상품 - 유사도: {similarity:.4f} ({analysis_mode})",
-                    "status": "success",
                 }
+                return Response.ok(data)
 
             # 여러 개가 있으면 유사도 비교
             logger.info("여러 상품 중 최고 유사도로 선택...")
@@ -114,12 +117,12 @@ def select_product_by_similarity(self, request: RequestSadaguSimilarity) -> dict
                 logger.warning(
                     f"최고 유사도 미달: similarity={best_result['similarity']:.4f} < threshold={similarity_threshold}"
                 )
-                return {
+                data = {
                     "keyword": keyword,
                     "selected_product": None,
                     "reason": f"최고 유사도({best_result['similarity']:.4f}) < 기준({similarity_threshold})",
-                    "status": "success",
                 }
+                return Response.ok(data, "최고 유사도가 기준보다 미달 되었습니다.")
 
             # 유사도 정보 추가
             selected_product["similarity_info"] = {
@@ -147,13 +150,12 @@ def select_product_by_similarity(self, request: RequestSadaguSimilarity) -> dict
             logger.success(
                 f"상품 선택 완료: title='{selected_product['title'][:30]}', {reason}"
             )
-
-            return {
+            data = {
                 "keyword": keyword,
                 "selected_product": selected_product,
                 "reason": reason,
-                "status": "success",
             }
+            return Response.ok(data)
 
         except Exception as e:
             logger.error(f"유사도 분석 서비스 오류: keyword='{keyword}', error='{e}'")
diff --git a/apps/pre-processing-service/app/test/test_keyword.py b/apps/pre-processing-service/app/test/test_keyword.py
index 11bd69fa..82daefc8 100644
--- a/apps/pre-processing-service/app/test/test_keyword.py
+++ b/apps/pre-processing-service/app/test/test_keyword.py
@@ -1,16 +1,11 @@
 import pytest
 from fastapi.testclient import TestClient
 from app.main import app
+from app.utils.response import Response
 
 client = TestClient(app)
 
 
-def test_read_root():
-    response = client.get("/keywords/")
-    assert response.status_code == 200
-    assert response.json() == {"message": "keyword API"}
-
-
 @pytest.mark.parametrize(
     "tag, category, start_date, end_date",
     [
@@ -32,6 +27,7 @@ def test_search(tag, category, start_date, end_date):
     assert response.status_code == 200
 
     response_data = response.json()
-    assert response_data["status"] == "success"
-    assert "keyword" in response_data
-    assert isinstance(response_data["total_keyword"], dict)
+    assert response_data["success"] == True
+    assert response_data["status"] == "OK"
+    assert "keyword" in response_data["data"]
+    assert isinstance(response_data["data"]["total_keyword"], dict)
diff --git a/apps/pre-processing-service/app/test/test_match_service.py b/apps/pre-processing-service/app/test/test_match_service.py
index 3f50ffad..7deb043c 100644
--- a/apps/pre-processing-service/app/test/test_match_service.py
+++ b/apps/pre-processing-service/app/test/test_match_service.py
@@ -32,13 +32,14 @@ def test_match_success():
 
     assert response.status_code == 200
     data = response.json()
-    assert data["keyword"] == body["keyword"]
-    assert data["status"] == "success"
-    assert isinstance(data["matched_products"], list)
+    assert data["success"] == True
+    assert data["status"] == "OK"
+    assert data["data"]["keyword"] == body["keyword"]
+    assert isinstance(data["data"]["matched_products"], list)
 
     # 반지가 포함된 상품들이 매칭되어야 함
-    if data["matched_products"]:
-        for product in data["matched_products"]:
+    if data["data"]["matched_products"]:
+        for product in data["data"]["matched_products"]:
             assert "match_info" in product
             assert "match_type" in product["match_info"]
             assert "match_score" in product["match_info"]
@@ -56,7 +57,9 @@ def test_match_no_results():
 
     assert response.status_code == 200
     data = response.json()
-    assert data["matched_products"] == []
+    assert data["success"] == True
+    assert data["status"] == "OK"
+    assert data["data"]["matched_products"] == []
 
 
 def test_match_no_matches():
@@ -83,5 +86,6 @@ def test_match_no_matches():
     assert response.status_code == 200
     data = response.json()
     # 매칭되지 않아도 성공으로 처리
-    assert data["status"] == "success"
-    assert isinstance(data["matched_products"], list)
+    assert data["success"] == True
+    assert data["status"] == "OK"
+    assert isinstance(data["data"]["matched_products"], list)
diff --git a/apps/pre-processing-service/app/test/test_sadagu_crawl.py b/apps/pre-processing-service/app/test/test_sadagu_crawl.py
index b419b5c6..72e4f0df 100644
--- a/apps/pre-processing-service/app/test/test_sadagu_crawl.py
+++ b/apps/pre-processing-service/app/test/test_sadagu_crawl.py
@@ -18,8 +18,10 @@ def test_crawl_success():
 
     assert response.status_code == 200
     data = response.json()
-    assert data["product_url"] == body["product_url"]
-    assert "product_detail" in data
+    assert data["success"] == True
+    assert data["status"] == "OK"
+    assert data["data"]["product_url"] == body["product_url"]
+    assert "product_detail" in data["data"]
 
 
 # def test_crawl_invalid_url():
diff --git a/apps/pre-processing-service/app/test/test_search_service.py b/apps/pre-processing-service/app/test/test_search_service.py
index d5d3a618..7ee32252 100644
--- a/apps/pre-processing-service/app/test/test_search_service.py
+++ b/apps/pre-processing-service/app/test/test_search_service.py
@@ -14,9 +14,10 @@ def test_search_success():
 
     assert response.status_code == 200
     data = response.json()
-    assert data["keyword"] == body["keyword"]
-    assert data["status"] == "success"
-    assert isinstance(data["search_results"], list)
+    assert data["success"] == True
+    assert data["status"] == "OK"
+    assert data["data"]["keyword"] == body["keyword"]
+    assert isinstance(data["data"]["search_results"], list)
 
 
 def test_search_empty_keyword():
@@ -29,7 +30,9 @@ def test_search_empty_keyword():
     # 빈 키워드라도 에러가 아닌 빈 결과를 반환해야 함
     assert response.status_code == 200
     data = response.json()
-    assert data["search_results"] == []
+    assert data["success"] == True
+    assert data["status"] == "OK"
+    assert data["data"]["search_results"] == []
 
 
 def test_search_nonexistent_keyword():
@@ -44,5 +47,6 @@ def test_search_nonexistent_keyword():
     assert response.status_code == 200
     data = response.json()
     # 검색 결과가 없어도 성공으로 처리
-    assert data["status"] == "success"
-    assert isinstance(data["search_results"], list)
+    assert data["success"] == True
+    assert data["status"] == "OK"
+    assert isinstance(data["data"]["search_results"], list)
diff --git a/apps/pre-processing-service/app/test/test_similarity_service.py b/apps/pre-processing-service/app/test/test_similarity_service.py
index 5eeba78d..6efbcdc1 100644
--- a/apps/pre-processing-service/app/test/test_similarity_service.py
+++ b/apps/pre-processing-service/app/test/test_similarity_service.py
@@ -38,13 +38,14 @@ def test_similarity_with_matched_products():
 
     assert response.status_code == 200
     data = response.json()
-    assert data["keyword"] == body["keyword"]
-    assert data["status"] == "success"
+    assert data["success"] == True
+    assert data["status"] == "OK"
+    assert data["data"]["keyword"] == body["keyword"]
 
-    if data["selected_product"]:
-        assert "similarity_info" in data["selected_product"]
-        assert "similarity_score" in data["selected_product"]["similarity_info"]
-        assert data["reason"] is not None
+    if data["data"]["selected_product"]:
+        assert "similarity_info" in data["data"]["selected_product"]
+        assert "similarity_score" in data["data"]["selected_product"]["similarity_info"]
+        assert data["data"]["reason"] is not None
 
 
 def test_similarity_fallback_to_search_results():
@@ -71,13 +72,14 @@ def test_similarity_fallback_to_search_results():
 
     assert response.status_code == 200
     data = response.json()
-    assert data["status"] == "success"
+    assert data["success"] == True
+    assert data["status"] == "OK"
 
     # 폴백 모드에서는 임계값을 통과한 경우에만 상품이 선택됨
-    if data["selected_product"]:
-        assert "similarity_info" in data["selected_product"]
+    if data["data"]["selected_product"]:
+        assert "similarity_info" in data["data"]["selected_product"]
         assert (
-            data["selected_product"]["similarity_info"]["analysis_mode"]
+            data["data"]["selected_product"]["similarity_info"]["analysis_mode"]
             == "fallback_similarity_only"
         )
 
@@ -102,9 +104,11 @@ def test_similarity_single_candidate():
 
     assert response.status_code == 200
     data = response.json()
-    assert data["selected_product"] is not None
+    assert data["success"] == True
+    assert data["status"] == "OK"
+    assert data["data"]["selected_product"] is not None
     assert (
-        data["selected_product"]["similarity_info"]["analysis_type"]
+        data["data"]["selected_product"]["similarity_info"]["analysis_type"]
         == "single_candidate"
     )
 
@@ -122,5 +126,7 @@ def test_similarity_no_candidates():
 
     assert response.status_code == 200
     data = response.json()
-    assert data["selected_product"] is None
-    assert "검색 결과가 모두 없음" in data["reason"]
+    assert data["success"] == True
+    assert data["status"] == "OK"
+    assert data["data"]["selected_product"] is None
+    assert "검색 결과가 모두 없음" in data["data"]["reason"]
diff --git a/apps/pre-processing-service/app/utils/response.py b/apps/pre-processing-service/app/utils/response.py
new file mode 100644
index 00000000..305f080e
--- /dev/null
+++ b/apps/pre-processing-service/app/utils/response.py
@@ -0,0 +1,25 @@
+class Response:
+    @staticmethod
+    def ok(data: dict, message: str = "OK") -> dict:
+        """성공 응답"""
+        return {"success": True, "data": data, "status": "OK", "message": message}
+
+    @staticmethod
+    def error(message: str = "오류가 발생했습니다", data: dict = None) -> dict:
+        """에러 응답"""
+        return {
+            "success": False,
+            "data": data or {},
+            "status": "ERROR",
+            "message": message,
+        }
+
+    @staticmethod
+    def not_found(message: str = "결과를 찾을 수 없습니다", data: dict = None) -> dict:
+        """검색 결과 없음"""
+        return {
+            "success": True,  # 에러가 아닌 정상 처리
+            "data": data or {},
+            "status": "NOT_FOUND",
+            "message": message,
+        }
diff --git a/apps/pre-processing-service/poetry.lock b/apps/pre-processing-service/poetry.lock
index d47e6783..bb05b60a 100644
--- a/apps/pre-processing-service/poetry.lock
+++ b/apps/pre-processing-service/poetry.lock
@@ -507,6 +507,18 @@ all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.8)", "httpx (>
 standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.8)", "httpx (>=0.23.0)", "jinja2 (>=3.1.5)", "python-multipart (>=0.0.18)", "uvicorn[standard] (>=0.12.0)"]
 standard-no-fastapi-cloud-cli = ["email-validator (>=2.0.0)", "fastapi-cli[standard-no-fastapi-cloud-cli] (>=0.0.8)", "httpx (>=0.23.0)", "jinja2 (>=3.1.5)", "python-multipart (>=0.0.18)", "uvicorn[standard] (>=0.12.0)"]
 
+[[package]]
+name = "filelock"
+version = "3.19.1"
+description = "A platform independent file lock."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "filelock-3.19.1-py3-none-any.whl", hash = "sha256:d38e30481def20772f5baf097c122c3babc4fcdb7e14e57049eb9d88c6dc017d"},
+    {file = "filelock-3.19.1.tar.gz", hash = "sha256:66eda1888b0171c998b35be2bcc0f6d75c388a7ce20c3f3f37aa8e96c2dddf58"},
+]
+
 [[package]]
 name = "flatbuffers"
 version = "25.2.10"
@@ -519,6 +531,46 @@ files = [
     {file = "flatbuffers-25.2.10.tar.gz", hash = "sha256:97e451377a41262f8d9bd4295cc836133415cc03d8cb966410a4af92eb00d26e"},
 ]
 
+[[package]]
+name = "fsspec"
+version = "2025.9.0"
+description = "File-system specification"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "fsspec-2025.9.0-py3-none-any.whl", hash = "sha256:530dc2a2af60a414a832059574df4a6e10cce927f6f4a78209390fe38955cfb7"},
+    {file = "fsspec-2025.9.0.tar.gz", hash = "sha256:19fd429483d25d28b65ec68f9f4adc16c17ea2c7c7bf54ec61360d478fb19c19"},
+]
+
+[package.extras]
+abfs = ["adlfs"]
+adl = ["adlfs"]
+arrow = ["pyarrow (>=1)"]
+dask = ["dask", "distributed"]
+dev = ["pre-commit", "ruff (>=0.5)"]
+doc = ["numpydoc", "sphinx", "sphinx-design", "sphinx-rtd-theme", "yarl"]
+dropbox = ["dropbox", "dropboxdrivefs", "requests"]
+full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
+fuse = ["fusepy"]
+gcs = ["gcsfs"]
+git = ["pygit2"]
+github = ["requests"]
+gs = ["gcsfs"]
+gui = ["panel"]
+hdfs = ["pyarrow (>=1)"]
+http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
+libarchive = ["libarchive-c"]
+oci = ["ocifs"]
+s3 = ["s3fs"]
+sftp = ["paramiko"]
+smb = ["smbprotocol"]
+ssh = ["paramiko"]
+test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
+test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
+test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard ; python_version < \"3.14\""]
+tqdm = ["tqdm"]
+
 [[package]]
 name = "google"
 version = "3.0.0"
@@ -764,6 +816,28 @@ files = [
     {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
 ]
 
+[[package]]
+name = "hf-xet"
+version = "1.1.10"
+description = "Fast transfer of large files with the Hugging Face Hub."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+markers = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""
+files = [
+    {file = "hf_xet-1.1.10-cp37-abi3-macosx_10_12_x86_64.whl", hash = "sha256:686083aca1a6669bc85c21c0563551cbcdaa5cf7876a91f3d074a030b577231d"},
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 zstd = ["zstandard (>=0.18.0)"]
 
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+
+[package.extras]
+all = ["safetensors[jax]", "safetensors[numpy]", "safetensors[paddlepaddle]", "safetensors[pinned-tf]", "safetensors[quality]", "safetensors[testing]", "safetensors[torch]"]
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+quality = ["ruff"]
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+testing = ["h5py (>=3.7.0)", "huggingface-hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools-rust (>=1.5.2)"]
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+torch = ["safetensors[numpy]", "torch (>=1.10)"]
+
 [[package]]
 name = "scikit-learn"
 version = "1.7.2"
@@ -2147,6 +2459,39 @@ files = [
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 ]
 
+[[package]]
+name = "tokenizers"
+version = "0.22.0"
+description = ""
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
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+    {file = "tokenizers-0.22.0-cp39-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8337ca75d0731fc4860e6204cc24bb36a67d9736142aa06ed320943b50b1e7ed"},
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+    {file = "tokenizers-0.22.0-cp39-abi3-musllinux_1_2_i686.whl", hash = "sha256:1626cb186e143720c62c6c6b5371e62bbc10af60481388c0da89bc903f37ea0c"},
+    {file = "tokenizers-0.22.0-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:da589a61cbfea18ae267723d6b029b84598dc8ca78db9951d8f5beff72d8507c"},
+    {file = "tokenizers-0.22.0-cp39-abi3-win32.whl", hash = "sha256:dbf9d6851bddae3e046fedfb166f47743c1c7bd11c640f0691dd35ef0bcad3be"},
+    {file = "tokenizers-0.22.0-cp39-abi3-win_amd64.whl", hash = "sha256:c78174859eeaee96021f248a56c801e36bfb6bd5b067f2e95aa82445ca324f00"},
+    {file = "tokenizers-0.22.0.tar.gz", hash = "sha256:2e33b98525be8453f355927f3cab312c36cd3e44f4d7e9e97da2fa94d0a49dcb"},
+]
+
+[package.dependencies]
+huggingface-hub = ">=0.16.4,<1.0"
+
+[package.extras]
+dev = ["tokenizers[testing]"]
+docs = ["setuptools-rust", "sphinx", "sphinx-rtd-theme"]
+testing = ["black (==22.3)", "datasets", "numpy", "pytest", "pytest-asyncio", "requests", "ruff"]
+
 [[package]]
 name = "tqdm"
 version = "4.67.1"
@@ -2169,6 +2514,81 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
+[[package]]
+name = "transformers"
+version = "4.56.1"
+description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
+optional = false
+python-versions = ">=3.9.0"
+groups = ["main"]
+files = [
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+    {file = "transformers-4.56.1.tar.gz", hash = "sha256:0d88b1089a563996fc5f2c34502f10516cad3ea1aa89f179f522b54c8311fe74"},
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+
+[package.dependencies]
+filelock = "*"
+huggingface-hub = ">=0.34.0,<1.0"
+numpy = ">=1.17"
+packaging = ">=20.0"
+pyyaml = ">=5.1"
+regex = "!=2019.12.17"
+requests = "*"
+safetensors = ">=0.4.3"
+tokenizers = ">=0.22.0,<=0.23.0"
+tqdm = ">=4.27"
+
+[package.extras]
+accelerate = ["accelerate (>=0.26.0)"]
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+audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+benchmark = ["optimum-benchmark (>=0.3.0)"]
+chat-template = ["jinja2 (>=3.1.0)"]
+codecarbon = ["codecarbon (>=2.8.1)"]
+deepspeed = ["accelerate (>=0.26.0)", "deepspeed (>=0.9.3)"]
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+dev-torch = ["GitPython (<3.1.19)", "GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.26.0)", "beautifulsoup4", "codecarbon (>=2.8.1)", "cookiecutter (==1.7.3)", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "kenlm", "kernels (>=0.6.1,<=0.9)", "libcst", "libcst", "librosa", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "num2words", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "pandas (<2.3.0)", "parameterized (>=0.9)", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rich", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict_core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (!=1.0.18,<=1.0.19)", "tokenizers (>=0.22.0,<=0.23.0)", "torch (>=2.2)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic_lite (>=1.0.7)", "urllib3 (<2.0.0)"]
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+ftfy = ["ftfy"]
+hf-xet = ["hf_xet"]
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+natten = ["natten (>=0.14.6,<0.15.0)"]
+num2words = ["num2words"]
+onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
+onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
+open-telemetry = ["opentelemetry-api", "opentelemetry-exporter-otlp", "opentelemetry-sdk"]
+optuna = ["optuna"]
+quality = ["GitPython (<3.1.19)", "datasets (>=2.15.0)", "libcst", "pandas (<2.3.0)", "rich", "ruff (==0.11.2)", "urllib3 (<2.0.0)"]
+ray = ["ray[tune] (>=2.7.0)"]
+retrieval = ["datasets (>=2.15.0)", "faiss-cpu"]
+ruff = ["ruff (==0.11.2)"]
+sagemaker = ["sagemaker (>=2.31.0)"]
+sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
+serving = ["accelerate (>=0.26.0)", "fastapi", "openai (>=1.98.0)", "pydantic (>=2)", "starlette", "torch (>=2.2)", "uvicorn"]
+sigopt = ["sigopt"]
+sklearn = ["scikit-learn"]
+speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (>=2.15.0)", "datasets (>=2.15.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "libcst", "mistral-common[opencv] (>=1.6.3)", "nltk (<=3.8.1)", "parameterized (>=0.9)", "psutil", "pydantic (>=2)", "pytest (>=7.2.0)", "pytest-asyncio", "pytest-order", "pytest-rerunfailures", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.11.2)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+tf = ["keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1,<0.14.0)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+tiktoken = ["blobfile", "tiktoken"]
+timm = ["timm (!=1.0.18,<=1.0.19)"]
+tokenizers = ["tokenizers (>=0.22.0,<=0.23.0)"]
+torch = ["accelerate (>=0.26.0)", "torch (>=2.2)"]
+torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
+torchhub = ["filelock", "huggingface-hub (>=0.34.0,<1.0)", "importlib_metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.22.0,<=0.23.0)", "torch (>=2.2)", "tqdm (>=4.27)"]
+video = ["av"]
+vision = ["Pillow (>=10.0.1,<=15.0)"]
+
 [[package]]
 name = "trio"
 version = "0.30.0"
@@ -2336,4 +2756,4 @@ h11 = ">=0.9.0,<1"
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.11,<3.14"
-content-hash = "a2a9a376c855a2599db8f932f42029ee9f016babdf0f004cb53690caa8f5a330"
+content-hash = "d02c427c36a8894fe681d04a4c0c45de45d5793a4d264375992ce1104096f620"
diff --git a/apps/pre-processing-service/pyproject.toml b/apps/pre-processing-service/pyproject.toml
index 42997650..c724ee24 100644
--- a/apps/pre-processing-service/pyproject.toml
+++ b/apps/pre-processing-service/pyproject.toml
@@ -19,7 +19,7 @@ gunicorn = ">=23.0.0,<24.0.0"
 requests = ">=2.32.5,<3.0.0"
 bs4 = ">=0.0.2,<0.0.3"
 selenium = ">=4.35.0,<5.0.0"
-#transformers = ">=4.56.0,<5.0.0"
+transformers = ">=4.56.0,<5.0.0"
 numpy = ">=2.3.2,<3.0.0"
 scikit-learn = ">=1.7.1,<2.0.0"
 python-dotenv = ">=1.1.1,<2.0.0"