RISK LEADS TO LIABILITY
MITIGATING RISK BENEFITS EVERYONE
Truleo's audio analysis platform is designed to help law enforcement mitigate risk & reinforce healthy interactions between the public & the police. Truleo's automated reporting aides agencies in reducing the number of encounters that lead to lawsuits, increasing safety and rebuilding trust. Learn more about our efforts to mitigate risk.
REPORT:
RISK MANAGEMENT
SURFACING HIGH-LEVEL TRENDS & INSIGHT
Surface trends, understand the perspective of both negative and positive sentiments. Monitor changes in trends.
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Measure negative interactions against positive interactions
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Measure duration of negative interactions
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Analyze interaction breakdown
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Sort interactions by event type
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Gain insight on both negative and positive sentiment by event type
IDENTIFYING AREAS FOR FURTHER REVIEW
Surfacing the most negative or volatile videos of the day, month, quarter.
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Streamline process by identifying and color coding flagged events and the corresponding videos.
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What time, this allows you to go back and review the relevant aspect of that video as needed.
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Take necessary actions as outlined by the agency.
Mitigating risks not only increases health & safety, it begins to create an environment where both parties can expect a neutral encounter.
REPORT: EVENT CLASSIFICATION
STREAMLINING THE REVIEW PROCESS
Surface trends, understand the skew of, of negative and positive sentiment as well as look at those trends over time and how they change.
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Surface and identify the appropriate events within each video.
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Identify areas for further review
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Identify videos with either benign or no flags that are all eligible for either deletion or compression.
CUSTOM REPORTING
SURFACING INSIGHTS & STRUCTURING UNREVIEWED DATA
Surface the most negative or volatile videos of the day, month, quarter.
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Make data-driven decisions that support trust-building within your community
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Departments have many different needs and use cases
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Customization by providing you the raw data via CSV file. This CSV file allows you to be, to marry this dataset with anything internal , and surfaces and structures of data that is otherwise.
