Audience
Data engineers, operations teams, and analysts wanting anomaly detection on time-series or numerical sensor data to flag abnormal behavior and monitor systems without building models manually
About LotusEye
LotusEye is a cloud-based AI anomaly detection service that automatically learns normal behavior from uploaded numerical or sensor data in CSV format and continuously calculates anomaly scores to flag deviations that may indicate faults or unexpected activity, providing alerts and visual insights without requiring users to have expertise in machine learning. It supports both wide-format CSV files, where each row represents sensor values at a timestamp, and long-format CSV with timestamp, sensor name, and value columns, and lets users upload data via drag-and-drop or through an API for scheduled automated processing. After training an AI model with normal operation data, users can upload test data to see calculated anomaly scores and review them in dashboards with time-series graphs, threshold indicators, and filters, helping teams spot unusual patterns and investigate potential issues quickly.
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