How Two MIT Dropouts Use AI to Cut Police Paperwork in the US
US police departments are drowning in paperwork, slowing response times and burning valuable resources. Code Four, a startup founded by two 19-year-old MIT dropouts, just raised $2.7 million to automate report writing across 25 police departments nationwide. This isn't a simple software upgrade—it's a shift in how law enforcement manages time and information using AI-generated documentation.
Founded by CEO George Cheng and CTO Dylan Nguyen, Code Four deploys AI to extract actionable reports from bodycam footage, redact sensitive data, and generate text transcriptions and summaries from video records. Officers still review AI drafts, but the system slashes paperwork hours, freeing officers to focus on fieldwork.
“The public safety sector has lagged in tech adoption despite high mission demands,” Nguyen said. This gap presents an opportunity for systemic leverage by automating low-value tasks, enabling policing with fewer distractions.
Automating report duties amplifies officer productivity without replacing human judgment.
Why AI in Policing is More Leverage Than Risk
Conventional wisdom warns that AI in policing risks bias and errors, leading to distrust and backlash. Code Four challenges this by embedding AI as a co-pilot that still requires human validation. This approach flips the script: AI accelerates the *process constraint* of documenting and interpreting footage—not replaces officers.
Unlike other nascent AI policing tools that aim for full automation or facial recognition (which face heavy regulatory and public hurdles), Code Four focuses on augmenting human work. This respects enforcement culture and legal standards, positioning it for faster adoption and scaling—similar to how smart sales strategies build lasting business advantages.
They’ve built a subscription model charging $30 per officer per month, ensuring recurring revenue while maintaining scalability. This pricing aligns incentives with deployments across departments ranging in size and budget.
Mechanics of Leverage: From Footage to Court-Ready Reports
Code Four’s AI ingests hours of unstructured video and outputs preliminary drafts that include redacted footage and transcriptions. This reduces the manual bottleneck of report assembly—one of the biggest friction points in police workflows.
Other providers either require costly manual tagging or risk low accuracy with black-box AI. Code Four combines proprietary AI with human review, creating a scalable feedback loop that improves over time. This approach mirrors the leverage unlocked by automation without losing the human touch.
Within just two months, they've launched 8 pilots and engaged in conversations with 17 additional departments, validating demand and accelerating network effects in a fragmented market of nearly 18,000 U.S. police departments and 2,500 district attorney offices.
Forward-Looking: What Police and Other Institutions Should Watch
The constraint shifted from tedious documentation to streamlined, tech-augmented operational focus. Departments that adopt Code Four’s AI co-pilot save officer time, reduce errors, and tighten compliance in record-keeping—a rare system that operates without constant human intervention but preserves critical review.
This model can extend beyond policing—to city surveillance, government transparency initiatives, even security in other sectors. It offers a blueprint for public institutions balancing technology with trust and accountability.
For governments and startups alike, investing in AI that improves human workflows—not replaces them—is the real leverage play.
Learn more about Code Four’s approach in the public pitch deck.
Related Tools & Resources
For law enforcement agencies and organizations aiming to streamline documentation and reduce paperwork burdens, effective PDF management is critical. Tools like Foxit provide robust PDF editing and document management capabilities that complement AI-driven report automation, ensuring digital documents remain accurate, secure, and easy to handle throughout the workflow. Learn more about Foxit →
Full Transparency: Some links in this article are affiliate partnerships. If you find value in the tools we recommend and decide to try them, we may earn a commission at no extra cost to you. We only recommend tools that align with the strategic thinking we share here. Think of it as supporting independent business analysis while discovering leverage in your own operations.
Frequently Asked Questions
How does AI help reduce police paperwork?
AI automates the extraction of actionable reports from bodycam footage by generating text transcriptions and summaries, and redacting sensitive data. This process cuts down paperwork hours significantly, allowing officers to spend more time on fieldwork.
What is the cost of AI report automation per police officer?
Some AI report automation services offer a subscription model costing around $30 per officer per month, which is scalable and aligns incentives with police department budgets.
Can AI replace human judgment in police report writing?
No, AI acts as a co-pilot that drafts reports but requires human review and validation, preserving human judgment while increasing productivity.
How many police departments currently use AI report automation pilots?
Early implementations include about 8 pilot programs with 17 additional police departments in active discussions, demonstrating growing demand across the US.
What challenges does AI face in policing applications?
AI in policing faces concerns about bias and errors, and regulatory hurdles especially related to face recognition. Focusing on augmenting human work instead of full automation helps overcome these challenges.
How does AI in police report writing improve workflow?
AI reduces manual bottlenecks by automatically generating preliminary report drafts from hours of unstructured video footage, decreasing time spent on documentation and improving compliance.
Is AI report automation scalable for different sized police departments?
Yes, subscription pricing models are designed to be scalable across departments of various sizes and budgets, supporting widespread adoption.
Can AI report automation be applied beyond policing?
This technology can extend to other public institutions like city surveillance and government transparency initiatives, balancing tech efficiency with trust and accountability.