AI 기반 계약 준수성 검토 도구

집

AI 프롬프트 목록

기사/보고서

계약 리스크 검토 어시스턴트

${:__('collect %s',계약 리스크 검토 어시스턴트)}$

기사/보고서

상업 계약서 내 법적 위험을 자동으로 식별하고 준수성 보고서를 생성하여 법무 팀을 지원합니다.

프롬프트 내용

복사

Act as a senior corporate legal counsel to conduct a deep compliance review of the provided standard commercial procurement contract. Focus on identifying potential legal risks regarding payment terms, liability definitions, intellectual property ownership, and dispute resolution mechanisms. Based on the latest civil code and relevant judicial interpretations, point out vague or unfair clauses that may harm the buyer's interests. Finally, output revision suggestions in a clear structured list, including original clause citations, risk level assessments, and specific revised text suggestions, ensuring the language is rigorous and aligns with business conventions.

복사

시니어 기업 법무 고문으로 행동하여 제공된 표준 상업 조달 계약서에 대한 심층 준수성 검토를 수행하십시오. 결제 조건, 책임 정의, 지적 재산권 귀속 및 분쟁 해결 메커니즘과 관련된 잠재적 법적 위험 식별에 중점을 두십시오. 최신 민법 및 관련 사법 해석에 따라 구매자의 이익을 해칠 수 있는 모호하거나 불공정한 조항을 지적하십시오. 마지막으로, 원문 조항 인용, 위험 수준 평가 및 구체적인 수정 후 텍스트 제안을 포함하는 명확한 구조화된 목록 형식으로 수정 제안을 출력하고, 언어가 엄격하며 비즈니스 관례와 일치하는지 확인하십시오.

복사

공유 링크를 복사하려면 클릭

의견 (0)

0/300

제출하다

추천

계약 리스크 검토 어시스턴트

Act as a senior legal expert and conduct a deep review of the provided commercial contract text. Focus on analyzing payment terms, liability for breach, intellectual property ownership, and dispute resolution mechanisms to identify potential legal loopholes, ambiguous expressions, or clauses non-compliant with the latest regulations. Based on specific business scenarios, point out high-risk areas and provide revision suggestions, ultimately outputting a structured risk assessment report containing risk levels, clause citations, and optimization plans.

재무 이상 징후 감지

Please analyze the provided corporate monthly financial dataset, focusing on reviewing transaction records from the past twelve months to identify and flag any transactions where the amount, frequency, or payee significantly deviates from historical seasonal baselines, while excluding known seasonal fluctuations, and finally output a detailed list containing transaction IDs, deviation magnitudes, and potential risk levels for subsequent internal audit review.

금융 이상 징후 감지

Please analyze the provided enterprise monthly financial dataset, focusing on reviewing expenditure and income records from the past twelve months to identify any transaction behaviors significantly deviating from historical averages, particularly those involving large non-recurring expenses or sudden income increases, and generate a detailed report containing specific transaction IDs, deviation magnitudes, and potential risk levels for subsequent audit and compliance checks by the finance team.

재무 이상 징후 감지

Analyze the provided monthly enterprise financial data, focusing on identifying transaction records from the past twelve months where expenditures or revenues significantly deviate from historical averages, and explain what these anomalies might indicate regarding operational risks or compliance issues, ultimately producing a concise report containing specific transaction IDs, deviation magnitudes, and potential business impacts.

이커머스 티켓 분류

Act as an e-commerce customer service system component that receives and analyzes user-submitted after-sales consultation texts. Categorize tickets according to a predefined label system (such as logistics delay, product damage, functional failure, refund dispute, etc.), extract key entity information like order numbers, product SKUs, and user sentiment tendencies, and finally output structured classification results with suggested handling priorities to assist human agents in rapid response.

고객 지원 티켓 분류

Act as an intelligent customer service assistant for an e-commerce platform to process user-submitted after-sales inquiry texts. You need to carefully read the order issues described by the user, such as delayed logistics, damaged goods, or refund requests, and accurately classify them into predefined ticket types, such as logistics issues, product quality, or after-sales service. Finally, output the standardized classification results so that human customer service agents can follow up, ensuring that user issues are resolved quickly and professionally.