Ember is a persistent automotive failure intelligence system designed to catch dangerous vehicle problems earlier. Major recalls often begin as scattered owner complaints—one driver reports stalling, another reports sudden power loss, another describes the same underlying failure in completely different words—and those warning signs can be hard to connect before enough damage is done. Ember searches a vehicle’s complaints and recalls, uses AI to turn messy reports into a structured failure fingerprint, then compares that pattern against previous vehicle investigations stored in MongoDB. Every search becomes long-term memory, including the component, symptoms, severity, complaint pattern, and whether similar cases were eventually recalled. That means Ember does not start from zero on each search: if a new vehicle begins showing the same kind of pattern that appeared before past recalls, Ember can recognize that precedent and surface it immediately. In short, Ember turns the history of automotive failures into reusable memory for spotting the next one earlier.
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