Инструмент проверки соответствия контрактов на базе ИИ

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Ассистент по анализу рисков контрактов

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Статья/Отчет

Автоматическое выявление юридических рисков в коммерческих контрактах с помощью ИИ для генерации отчетов о соответствии.

Содержание промпта

Копировать

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.

Копировать

Выступите в роли старшего корпоративного юридического советника и проведите глубокий анализ соответствия предоставленного стандартного коммерческого договора поставки. Сосредоточьтесь на выявлении потенциальных юридических рисков, касающихся условий оплаты, определения ответственности, прав интеллектуальной собственности и механизмов разрешения споров. На основе новейшего гражданского кодекса и соответствующих судебных интерпретаций укажите на неясные или несправедливые положения, которые могут нанести ущерб интересам покупателя. Наконец, предоставьте предложения по изменению в виде четкого структурированного списка, включая цитирование исходных положений, оценку уровня риска и конкретные предложения по пересмотренному тексту, обеспечивая строгость языка и соответствие деловым обычаям.

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Рекомендация

Ассистент по проверке рисков контрактов

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.

Обнаружение финансовых аномалий

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.

Обнаружение финансовых аномалий

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.

Обнаружение финансовых аномалий

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.

Классификация тикетов поддержки

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.

Классификация тикетов поддержки

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.