Build a web application that identifies software failures that do not trigger conventional errors, such as gradually degrading performance, incomplete background jobs, silently dropped events, stale cached data, and workflows that appear successful but produce incorrect outcomes.
Allow developers to upload logs, metrics, event records, and sample transaction traces. Establish baseline behavior, identify anomalies, and connect related signals into a timeline of possible failure chains.
Create a visual investigation workspace showing the earliest suspicious signal, affected components, downstream consequences, confidence levels, and recommended diagnostic tests.
Include synthetic incident generation, configurable detection rules, historical comparisons, and an exportable incident report. Provide deterministic statistical detection methods before adding optional AI-based explanations.
Never claim a correlation proves causation. Clearly distinguish observed evidence from hypotheses, protect sensitive log data, and ensure suggested remediation steps require human review.
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