Why we built a support bot that investigates tickets before drafting a reply

The text discusses the development of a support bot designed to mimic the investigative process of a support engineer, rather than simply generating automatic responses to support tickets. The bot's approach prioritizes context assembly by examining live support threads, semantically similar historical tickets, and a local copy of documentation to draft internal notes for human review. This method contrasts with typical support automation that often relies on generation without sufficient grounding, leading to inaccurate and risky outputs. The system utilizes an event-driven workflow, incorporating various tools and databases like PostgreSQL with pgvector for retrieval and local documentation for file-based search, to ensure the bot is contextually informed and capable of producing reliable drafts. The design emphasizes the importance of retrieval over recall, leveraging past tickets as a valuable knowledge base, and maintaining a narrow, explicit focus to enhance trust and practicality. The primary goal is to reduce repetitive research and improve the accuracy of initial investigations, ultimately aiding human reviewers in support workflows. Future enhancements may include integrating runtime evidence to further refine the investigation process.

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