Struck-By-Sentinel: 

Currently, the industry relies heavily on vital voluntary reporting tools to track struck-by crashes. While critical, voluntary reporting leaves a massive gap in capturing the true systemic scale of the problem.
 

Struck-By Sentinel acts as an active intelligence network to close this gap. Moving beyond voluntary submissions, the Sentinel framework aggregates anonymized official crash records and utilizes AI-driven web and social media scrapers to identify unreported crashes hidden in unstructured public data.
 

To ensure absolute data integrity for federal and state partners, the AI architecture utilizes a strict "Human-in-the-Loop" model—leveraging computational linguistics to flag potential crashes, while relying on human analysts to verify the data and build out the database parameters.