Case Study: Automating Quarterly Reports with an AI-Powered Workflow

A Ruby/Rails maintenance service (Bonsai by FastRuby.io) built an internal LLM-powered workflow to automate quarterly client reports. The system mirrors the original manual process with three layers: Data Providers (Jira, GitHub, static analysis tools), Curators (LLM-assisted summarization per report section), and Reviewers (LLM quality gates returning structured JSON with accuracy, completeness, and quality scores). A human engineer does a final review of the polished draft. Key lessons: structured workflows beat autonomous agents for predictable tasks, mapping existing human processes to AI outperforms AI-first design, and explicit review steps are essential for client-facing output quality.