DataBeat is an AI-powered programmatic advertising monitoring and analytics platform built on LeafMesh ADK. The system automates daily yield analysis that previously took analysts 4–6 hours, replacing manual dashboard reviews with a coordinated swarm of 12+ specialized AI agents. From anomaly detection to root cause analysis to executive narrative generation — DataBeat delivers actionable insights in minutes, not hours. Multi-tenant, multi-exchange, and production-grade.

Designed a robust data ingestion pipeline supporting CSV uploads and BigQuery integration. Built smart column detection that auto-classifies metrics (currency, rates, volumes) and generates business KPI formulas like CPM, CTR, and fill rate without manual configuration.

Architected a 12+ agent swarm on LeafMesh ADK using managed mesh topology. Specialized agents handle conversation understanding, SQL generation, anomaly detection, root cause analysis, business narrative generation, and alert dispatch — all coordinated through a central manager with summarizer oversight.

Developed statistical anomaly detection using z-score analysis with multiple baselines: previous day, 7-day moving average, and day-of-week average. Built the explainer agent for automated root cause analysis and the business narrator for executive-ready reports.

Built a React dashboard with real-time KPI visualization, anomaly impact flow charts, segment analysis views, and a natural language chat interface. Implemented multi-tenant switching and role-based access control for enterprise deployment.

Validated anomaly detection accuracy against real programmatic data (175K+ rows, $922K revenue). Containerized with Docker, deployed on Kubernetes, with MongoDB persistence and Redis state management. Achieved 93–99% time reduction across all analyst workflows.
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