Before designing a solution, we conduct a deep dive into the city's unique challenges by working closely with local governments, community stakeholders, and industry experts.

🔹 Example: A city struggling with traffic congestion may not know whether the issue is caused by poor road infrastructure, inefficient traffic light synchronization, or a lack of public transit options. Our team uses AI-powered mobility analysis to track traffic patterns, identify bottlenecks, and recommend real-time traffic optimization strategies.

Once we identify key challenges, our team conducts in-depth research to analyze potential solutions using real-world data and emerging technologies.

🔹 Example: A city with high crime rates in specific districts can leverage predictive policing models powered by machine learning. Our research might involve analyzing crime trends, identifying high-risk areas, and developing an AI-driven alert system that can help law enforcement allocate resources efficiently.

We work with HBCU students, researchers, and city leaders to develop and test prototypes in controlled environments before deploying them in live urban settings.

🔹 Example: A city aiming to reduce energy consumption could test a prototype for smart streetlights that automatically adjust brightness based on real-time pedestrian and vehicle activity. PCF's team would simulate different scenarios, ensuring that the technology functions optimally before citywide deployment.

After refining the prototypes, we move to the pilot phase, where selected cities test the solutions in real-world conditions.

🔹 Example: In a city struggling with pedestrian safety, we might implement a 6-month pilot where AI-powered crosswalk cameras monitor pedestrian movements, analyze near-miss incidents, and provide automated traffic light adjustments to improve safety.

Once a solution has been successfully piloted, we help cities scale the technology for long-term adoption, ensuring that it can be integrated into city infrastructure with minimal disruption.

🔹 Example: After proving that an AI-based water management system reduces leaks and optimizes distribution, the city can expand the technology to cover all districts—reducing water waste and lowering operational costs.