Officer Reviews

Real Officer Transparency with Officer Reviews using Our AI.
Provisional Patent Application 62/705,319

Download the App

IOS and Android App available to download to the public for free to record officer interactions.

Law Enforcement

There are over 18,000 Federal, State, and local law enforcement. agencies around the U.S. It is estimated that there are between 750,000-800,000 sworn officers who could use our software.

Police and Public Interactions

There are over 350 million interactions between police officers and the public every year. In the USA alone there are over 40 million traffic citations and roughly 11 million arrests.

Upload to the Cloud or Officers can use Reviews Internally and not Upload

With our App, the public will be able to automatically load their video to the cloud. The Officers’ will also upload and be synced with the public’s video. This will then be analyzed by our AI.

AI Sentiment Technology

Emotion recognition (or emotion detection) API identifies human emotions from facial expressions. This API can be used to monitor emotions associated with visual content, smiling, sadness, disgust, surprise, anger, fear, neutral and more. It analyzes emotional valence trends as well as detecting heart rate from the face. Our technology records prominent head and gaze directions and upload it all to the cloud.

Officer Grading Reports

Officers can be graded consistently on their reviews every 6 months, one year, three years or however often the department wants to. Officers who initially grade low can be offered additional training immediately. Focus just on officers who need training, not your entire Department.

Public Can Sort and View Video Interactions

The public can see interactions which will show full transparency of officers and the public with our AI reviews. Or we can aggregate all reviews with no videos shown and just show how entire departments and/or states rank online by filtered searches. Can be determined by each Department.

Department Expenditures

Between 2014-2016, the expenditure for police-related lawsuits in New York was $710,608,666. In Chicago alone, more than $153,133,333 were accounted for in payouts. Lets help reduce these astronomical numbers and keep employees accountable.

Officer Reviews

Our system will automatically review the officers potentially saving the Department millions from unforeseen lawsuits. Departments can make additional training to officers who score low or help with weeding out the bad apples if officers do not make grade.

Potential for Millions of dollars Saved for The City/Municipalities

Departments, the City and Municipalities would be able to lower insurance premiums by additional training to those who need it and by dismissing officers who routinely score low reviews based off our AI technology. Allocate department funds on training officers who routinely score low as opposed to the entire officer workforce.

Reviews can be gathered by officers and the public and shared to social media.

Officers and the public may share reviews on social media. Officers can gather reviews based on service done for the public and then share it via their social media pages or the Departments social media pages.

Saving Lives

Through time and transparency to the public, Officer Reviews can assist in changing the current negative sentiment towards law enforcement and ultimately help save lives. Instead of defunding police lets allocate money to AI and make the world a safer place.

Employee burnout is one of the mental health issues that AI tools are helping HR to identify.

Employees at risk of burnout may use words that suggest a problem.
This allows managers and organizational psychologists to find which employees need attention. For instance, if an employee is suddenly irritable and a possible spike in their communications the past week or month, it might indicate employee burnout or it may be extra stress.

Our AI Data Anonymization Tools protect your officers and Departments.

Data anonymization is the process of protecting sensitive information by changing or erasing the parts of it that may connect a living individual to the data or cross-connect a piece of data with other datasets. It is used to make all people that data describes completely anonymous.