diff --git a/app/api/v1/conversations.py b/app/api/v1/conversations.py
index 862a3fa..0a3ef0c 100644
--- a/app/api/v1/conversations.py
+++ b/app/api/v1/conversations.py
@@ -14,6 +14,7 @@ from app.schemas.conversation import (
     ConversationData,
     ConversationMessageCreateRequest,
     ConversationSummary,
+    DirectQueryRequest,
 )
 from app.services.conversations import ConversationService
 
@@ -39,6 +40,15 @@ async def create_conversation(
     return ApiResponse(data=await ConversationService(session).create(current_user, payload.title))
 
 
+@router.post("/direct-query", response_model=ApiResponse[dict])
+async def direct_query(
+    payload: DirectQueryRequest,
+    session: DatabaseSession,
+    _current_user: Annotated[UserAccount, Depends(require_permission("risk.assess", operate=True))],
+) -> ApiResponse[dict]:
+    return ApiResponse(data=await ConversationService(session).direct_query(payload.kind, payload.query))
+
+
 @router.get("/{conversation_id}", response_model=ApiResponse[ConversationData])
 async def get_conversation(
     conversation_id: int,
diff --git a/app/schemas/conversation.py b/app/schemas/conversation.py
index 3c60ee4..7dedf78 100644
--- a/app/schemas/conversation.py
+++ b/app/schemas/conversation.py
@@ -19,6 +19,11 @@ class ConversationMessageCreateRequest(ApiSchema):
     message_type: Literal["text", "image", "file", "audio"] = "text"
 
 
+class DirectQueryRequest(ApiSchema):
+    kind: Literal["supplier", "material", "cases", "solution"]
+    query: str = Field(min_length=1, max_length=2000)
+
+
 class ConversationListQuery(ApiSchema):
     limit: int = Field(default=50, ge=1, le=100)
 
diff --git a/app/services/conversations.py b/app/services/conversations.py
index 1ade4fe..f78d1b6 100644
--- a/app/services/conversations.py
+++ b/app/services/conversations.py
@@ -128,6 +128,49 @@ class ConversationService:
             raise AppException("对话创建失败", code=500, status_code=500)
         return self._data(persisted, messages=[], total=0, offset=0)
 
+    async def direct_query(self, kind: str, query: str) -> dict[str, Any]:
+        """Build a shortcut result without creating a conversation message."""
+        text = query.strip()
+        if not text:
+            raise AppException("查询内容不能为空", code=422, status_code=422)
+        if kind == "supplier":
+            return await self._supplier_card(text, {"supplier": text})
+        if kind == "material":
+            return await self._material_card(text, {"packaging": text})
+        if kind == "solution":
+            semantics = self._normalize_semantics(
+                text,
+                await self.llm.extract_semantics(text, history=[]),
+            )
+            return await self._solution_card(text, semantics)
+        results = await self._similar_cases(
+            text,
+            category=None,
+            material=None,
+            supplier=None,
+            factory=None,
+            stage=None,
+            anomaly_type=None,
+        )
+        top_cases = list(results[:3])
+        # Keep the same similarity signal used by the risk assessment card.
+        # Vector retrieval populates it in RAG; lexical fallback still gets a
+        # deterministic score so the shortcut card never loses that field.
+        for item in top_cases:
+            if item.case.similarity is None:
+                item.case.similarity = round(max(0.0, min(1.0, item.score / 100)), 3)
+        return {
+            "type": "cases",
+            "title": "案例检索",
+            "query": text,
+            "cases": [item.case.model_dump(mode="json", by_alias=True) for item in top_cases],
+            "matchedCount": len(top_cases),
+            "suggestions": [
+                "同类问题优先复用历史方案，再按判定标准复测验证",
+                "如已出货，准备客诉应对预案并对同批次抽检",
+            ],
+        }
+
     async def delete(self, conversation_id: int, user: UserAccount) -> None:
         """Delete one user-owned conversation and retain an audit trail."""
 
@@ -876,6 +919,12 @@ class ConversationService:
             "type": "solution",
             "title": "AI 案例推理生成",
             "confidence": "中",
+            "thinkingSummary": [
+                f"识别产品场景：{category}，提取包装、供应链和风险问题等关键信息",
+                f"检索历史异常案例，匹配到 {len(similar_cases)} 条相似案例并进行相似度排序",
+                f"关联判定标准库 {len(standards)} 条规则，完成规则引擎兜底校验",
+                f"综合历史案例与标准规则，整理 {len(suggestions)} 条可执行建议",
+            ],
             "analysisBase": (
                 f"目标品类「{category}」当前命中历史异常 {direct_count} 条；"
                 "方案结合已导入异常案例和标准规则生成。"