Built at Data Sovereignty & Responsible AI · submitted 20 September 2026
Datalight helps teams understand their data, detect meaningful changes, and investigate them with traceable evidence—while keeping raw data local. Upload a numeric CSV to explore data quality, statistics, correlations, and short-term forecasts. Replay observations in batches to monitor abrupt changes, sustained drift, and deviations from an initial reference. Interactive charts connect each flagged interval to the measurements and rules behind it, with an explicit assessment of evidence strength. An optional AI assistant explains findings, answers follow-up questions with evidence citations, and translates natural-language requests into simple monitoring rules for users to review and apply. Only computed summaries and sanitized requests with anonymous channel identifiers reach the model; raw observations and original column names stay local. Monitoring continues even when AI is unavailable. Human judgment stays central: users can accept, question, or override decisions with a reason, while preserving the original findings and review history. Statistical changes are treated as signals for investigation, not proven diagnoses. Our demo uses industrial process data from the Tennessee Eastman benchmark and a synthetic web-service dataset to demonstrate the same monitoring workflow across domains.
