The Center for Data Innovation recently spoke with Dan McCarthy, Vice President of Cyvl, a Massachusetts-based company that uses AI models and high-resolution mapping to help cities better manage their infrastructure. McCarthy explained how Cyvl’s technology classifies the condition of city assets and predicts which roads and sidewalks will need repair next, helping local governments move from reactive to proactive infrastructure management.
David Kertai: What problem is Cyvl solving in infrastructure management?
Dan McCarthy: Cities and towns across the country spend billions of dollars each year maintaining and repairing roads, sidewalks, and other public assets. Yet many still rely on outdated, inconsistent, or incomplete data when planning projects. This leads to wasted funds, project delays, and unsafe conditions for residents.
Cyvl gives governments a clear, real-time view of their infrastructure through our Infrastructure Intelligence Platform. The platform continuously updates insights on every street, sidewalk, and other assets, enabling officials to make data-driven decisions that stretch budgets further and improve community safety.
Kertai: How do you collect data to map infrastructure?
McCarthy: Cyvl uses compact LiDAR sensors and high-resolution cameras that mount on any standard vehicle, turning ordinary city or contractor fleets into powerful survey tools. As these vehicles drive, they capture millions of data points and map the city in 3D to create a detailed digital twin of the built environment. This approach allows us to survey an entire city in days rather than months, at a fraction of the traditional cost.
Kertai: How does Cyvl’s platform analyze collected data to detect infrastructure issues?
McCarthy: Cyvl’s AI platform uses machine learning to automatically detect and classify road conditions, pinpointing cracks, potholes, faded markings, and other defects. Beyond detection, our AI models predict where deterioration will occur next, allowing cities to schedule preventative maintenance before problems worsen. Our Infrastructure Intelligence Platform summarizes these results in an interactive dashboard where users can explore condition scores, risk predictions, and capital planning tools.
Kertai: Can you share examples of Cyvl’s technology helping cities?
McCarthy: In Maple Grove, Minnesota, rapid population and development growth created a surge in road use and wear, causing many of the city’s older roads to need repairs sooner than expected. This repair demand overwhelmed the city’s maintenance program, which had limited staff and funding. However, by adopting Cyvl’s AI-powered pavement assessments, Maple Grove analyzed four times more roads using its existing budget and freed up resources to focus on the most urgent and cost-effective repairs. The result: longer pavement life, lower costs, and greater public trust through transparent, data-backed decisions.
In Green Bay, Wisconsin, Cyvl helped the city survey 526 miles of roadway in just weeks using vehicle-mounted LiDAR and high-resolution sensors. The data enabled a proactive, data-driven paving program that improved budgeting and significantly accelerated repairs.
Kertai: What new capabilities do you plan to provide for cities’ infrastructure using AI?
McCarthy: We aim to give towns and cities a living, comprehensive view of their infrastructure so they can build faster and create safer, more resilient communities. Cyvl’s data help cities make smarter decisions across the entire infrastructure lifecycle, from planning and budgeting to maintenance and long-term investment.
Our Pavement Management Planner enables dynamic one-, three-, and five-year plans that automatically update as projects are completed, helping public works teams prioritize work, justify budgets, and coordinate efficiently. We’re also expanding our data collection to cover not just roads, but assets like sewers, drainage systems, and other elements of the built environment. By combining richer datasets with smarter AI models, we help cities move from static assessments to real-time infrastructure awareness, anticipating issues, planning proactively, and investing with confidence.
