Code Four

StageSeed
HQSan Francisco, United States
IndustriesPublic SafetyAI/MLComputer VisionSaaSGovernment

About Code Four

Code Four is an AI-first public-safety startup. It focuses on applying artificial intelligence to public-safety challenges. The company serves municipal, county, state, and federal agencies. By serving government clients across multiple levels, Code Four positions itself in the public-safety technology space.

Company at a glance

Location
San Francisco, United States
LinkedInLinkedIn

Location

Life at Code Four

Frequently asked questions

Companies similar to Code Four

Ambi

Ambi is building the interface for the AI era - software and hardware that move at the speed of thought. We're creating a living memory system that turns conversations, meetings, and daily signals into one connected knowledge base, across phone, watch, and desktop. Backed and early-stage, headquartered in San Francisco with hybrid flexibility - this is a ground-floor seat on category-defining hardware.

AI/ML11-50
Arbio

Arbio, headquartered in Berlin, Germany, is building an AI-native property management platform for short-term rentals across Europe. It currently manages over 1,000 apartments and operates real properties in Berlin, Hamburg, Vienna, and Leipzig, with plans to expand to additional European cities. The company has raised a $36 million Series A funding and maintains an engineering team of five engineers. Arbio’s platform uses AI to observe, decide, and act on real-world property operations with humans in the loop, enabling AI-assisted management of short-term rental properties across Europe.

AI/ML51-200
Arga Labs

Arga Labs provides isolated sandbox environments equipped with API twins of third-party services, enabling developers and organizations to safely test and train AI agents without risking their production systems. By offering a controlled testing ground that mirrors real-world APIs, the company allows teams to experiment with AI implementations, validate workflows, and refine agent behavior in a risk-free setting. This approach addresses a critical need in AI development, where testing in live environments can introduce costly errors or security vulnerabilities.

AI/ML1-10