Claude Opus Real-World Business Operation Log Dataset
About this dataset
This dataset was collected from real-world business scenarios involving nearly 600 employees across all functional roles within the company. It comprehensively captures end-to-end operational logs of the Claude Opus large language model, covering daily AI usage patterns across multiple roles, including R&D, product management, legal affairs, operations, finance, and project management. The data originates from actual online API calls, agent tool interactions, multi-turn long‑dialogue sessions, and batch document processing performed on the internal AI business platform. All raw data has undergone privacy‑masking procedures to remove employee identifiers, confidential corporate information, and sensitive business data, ensuring compliance with internal data‑protection standards.
The dataset includes structured fields such as complete conversation chains, model invocation parameters, dialogue interaction texts, tool‑call traces, inference performance metrics, system exception records, and security risk labels, stored in JSONL format. It covers a wide range of real‑world use cases, including routine office Q&A, code development, parsing lengthy contractual documents, collaborative reasoning across multiple tools, testing high‑risk prompt strings, and scheduling batch tasks. This makes it suitable for research and practical applications such as agent behavior analysis, LLM system monitoring and maintenance, fine-tuning models for private enterprise use cases, content safety and risk control, and analyzing AI usage costs and efficiency.