BatchDAG: LLM-Planned Execution Graphs for Scalable Ad-Hoc Analysis Over Enterprise Data
BatchDAG is a system that uses a large language model to generate a typed directed acyclic graph (DAG) of operations—including SQL queries, semantic searches, and in-memory transforms—for ad-hoc analysis over enterprise-scale datasets. It introduces entity-aware batching, reducing LLM calls by up to 47x, and achieves quality comparable to expert-designed pipelines while outperforming baseline agents in controlled experiments. In production at Brevian.ai, BatchDAG processes queries over 50,000+ meetings in under 60 seconds at low cost.
Why it matters: BatchDAG demonstrates a scalable, general-purpose orchestration layer that automates complex data analysis workflows from natural language, reducing manual engineering and enabling efficient enterprise-scale analytics.
Full story at: arXiv AI/ML ↗