Artificial Intelligence is no longer just a topic for research labs and big tech companies — it has hit the streets, literally.
While cities around the world try to balance increasingly tight budgets with infrastructure that never stops growing, an American startup found a pretty clever way to use technology to solve a very real problem: the potholes and cracks that show up on roads every single day.
Cyvl, a company based in Somerville, Massachusetts, is using AI to monitor, map, and analyze road conditions in real time across urban areas. The pitch is simple and powerful at the same time: instead of waiting for someone to call in a complaint or for an inspector to do a slow, manual survey, Cyvl’s technology handles that job much faster, cheaper, and more accurately. 🚗📡
Below, you will learn how this technology works in practice, what kind of impact it has on city government decisions, why Boston has become a success story for this model, and how a new platform called AskBoston.ai is taking transparency to a whole new level.
How Cyvl’s AI Collects Data from the Streets
The process starts with data collection — and this is where Cyvl sets itself apart from any traditional approach. Instead of sending teams out to walk the streets with clipboards and forms, the company equips regular vehicles with cameras and sensors that capture images and pavement data while the car simply drives around the city. This process runs continuously, without disrupting traffic, without ridiculous logistics costs, and without relying on specialized labor availability for each inspection. It is technology working while the city goes about its normal routine.
All of that raw material captured by the sensors is processed by artificial intelligence algorithms trained to identify different types of pavement issues — from small surface cracks to deep potholes that already pose a real risk to drivers and pedestrians. The AI can classify each defect by severity level, pinpoint its exact geographic location, and log everything into a structured database that municipal teams can access at any time.
According to Daniel Pelaez, co-founder and CEO of Cyvl, even seemingly minor details make a difference in planning. He explains that the specific type of crack identified by the AI can directly influence the decision to fix it now or hold off. That level of granularity simply did not exist in traditional inspection processes, where a human inspector would need about 30 minutes to manually document a single stretch of road. With Cyvl’s technology, that same analysis happens automatically as sensor-equipped vehicles drive through the city.
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The platform goes well beyond just detecting defects. It allows users to zoom in on the conditions of any road and see exactly what is going on there. Pelaez demonstrated how the system works by showing how the AI identifies, for example, alligator cracking — that pattern of interconnected cracks that looks like reptile skin — and can automatically classify up to four different types of defects on a single stretch of road. That level of detail is what makes it possible to calculate precisely how much asphalt will be needed for a repair and exactly when resurfacing should happen. 📊
Way Beyond Asphalt: What Else the Platform Can Identify
One of the most interesting features of Cyvl’s solution is that it does not stop at analyzing pavement. The platform can also catalog other elements of urban infrastructure, such as parking signs, public benches, and traffic cameras. Beyond identifying these objects, the system records the condition of each one — whether a sign is damaged, whether a bench needs maintenance, or whether a camera is operational.
This turns the tool into something much bigger than a simple pothole detector. It becomes a living digital inventory of an entire city’s infrastructure, constantly updated as equipped vehicles drive through the streets. For public officials, having access to this kind of centralized and always-current information represents a massive leap in planning and resource allocation capabilities.
City-scale data collection that could have previously taken weeks or even months using manual methods now happens in days. This completely changes how quickly city governments can respond. With up-to-date and reliable information in hand, public managers stop making decisions in the dark and start acting on real evidence. The frequency at which this data is refreshed also makes it possible to spot when an area that was in good shape starts deteriorating, even before the problem becomes visible and dangerous to people traveling through it.
Smarter and More Cost-Effective Urban Repairs
One of the biggest headaches for any city administration is figuring out where to spend the money earmarked for infrastructure maintenance. The budget is never enough to fix everything at once, and without reliable data, decisions end up being shaped by political pressure, one-off complaints from residents, or simply gut instinct. The result of this lack of structured information is predictable: misallocated resources, emergency repairs that cost far more than preventive maintenance, and roads that keep deteriorating while others get unnecessary attention.
Cyvl’s solution tackles exactly this issue. With a detailed and constantly updated map of road conditions, planning teams can build urban repair schedules based on the actual needs of each stretch. This means a road with moderate deterioration can enter a preventive maintenance cycle before it needs a full reconstruction, which typically costs five to ten times more. The logic is straightforward: addressing a problem while it is still small is always cheaper and more efficient than waiting for it to become a crisis.
As the company’s core focus — in the CEO’s own words — is using technology to provide governments with fresher, more up-to-date data about what they own, and then applying AI and software to filter that data and support decision-making, the practical outcome is urban management that finally operates with the speed and precision citizens expect.
Beyond the direct savings on repair costs, there is an indirect impact that often goes unnoticed: fewer accidents and less vehicle damage caused by roads in poor condition. Potholes and deteriorated pavement cause everything from flat tires to serious crashes, generating costs for drivers, lawsuits against city governments, and public health expenses. When artificial intelligence helps anticipate and resolve these problems, the benefit extends far beyond the roads — it is reflected in the quality of life for the entire urban population. 🏙️
Boston as a Real-World Example
Boston is not just any city when it comes to urban innovation. The capital of Massachusetts has a track record of adopting emerging technologies in its public policies, and the case with Cyvl was no different. The city is one of more than 500 clients the company already serves across the United States and Australia. Boston began using the platform for systematic monitoring of its roads, integrating the data generated by the AI directly into the decision-making processes of its infrastructure teams.
Santiago Garces, Chief Information Officer for the city of Boston, points out that the partnership with Cyvl and other private-sector partners is showing that it is possible to use AI in a way that truly serves people. Instead of relying on manual inspections or scattered complaints from residents, city employees can now get a data-driven view of road conditions across entire neighborhoods.
Garces also notes that the proactive approach enabled by the technology should generate savings by preventing more catastrophic failures — the kind that cost far more to fix once they have reached a critical point. But the benefit does not stop at cost savings. The technology also helps determine when and where to make major investments, so that decisions are equitable, fair, and timely. This is an important point: the AI is not just optimizing spending, it is helping distribute resources more evenly across different neighborhoods and communities.
With Cyvl’s solution in operation, Boston managed to map the condition of hundreds of miles of roads in far less time than any traditional inspection would allow. This gave city management a view that is both panoramic and detailed at the same time — something that was simply not feasible before in practice. Infrastructure maintenance teams started working with a completely different level of precision, knowing exactly where the worst stretches are, which areas are deteriorating rapidly, and where interventions can be safely postponed without risking public safety.
AskBoston.ai: Urban Transparency on Another Level
The partnership between Cyvl and the city of Boston just gained a pretty interesting new chapter. The two parties expanded their collaboration with the launch of a website called AskBoston.ai. The platform went live recently and allows anyone to ask specific questions about particular streets or neighborhoods and receive answers based on real inspection data collected by Cyvl and the city itself.
Imagine being able to ask, for example, what the current condition of a specific street in your neighborhood is and getting an answer grounded in concrete data, not guesswork or generic estimates. That is the kind of transparency AskBoston.ai aims to deliver. It is artificial intelligence connecting technical infrastructure data directly to everyday citizens in an accessible and understandable way.
Pelaez acknowledges there may be some tweaks and minor issues as more people use the tool, but he encourages people to test the platform and share feedback. This open approach to user input is a positive sign that the company and the city government are committed to refining the experience based on real-world use, not just lab testing. 💬
More Than 500 Clients and Growing
Cyvl is no longer a startup just experimenting with a promising idea. The company has already surpassed the 500-client mark across the United States and Australia. That number shows that demand for artificial intelligence solutions applied to urban management is real and growing steadily. Cities of different sizes and contexts are recognizing that the traditional model of road inspection and maintenance can no longer keep up with current needs, and that technology can be a powerful ally in filling that gap.
The growing client base also validates the company’s approach of keeping the process as simple as possible on the operational side. There is no need to invest in special fleets or expensive equipment. The sensors and cameras are mounted on vehicles that are already naturally driving around the cities, which makes adoption much more feasible for municipalities with limited budgets. The scalability of the model is one of its greatest strengths.
What This Movement Means for the Cities of the Future
Cyvl’s story is not just about asphalt and potholes. It is about a shift in mindset regarding how cities can and should use technology to solve everyday problems. Artificial intelligence applied to urban management is still in its early stages, but cases like this clearly show that the potential is enormous — and that the first results are already concrete and measurable. The logic that works for pavement can, with the right adaptations, work for street lighting, signage, water and sewer networks, parks, and countless other assets that are part of daily city life.
Continuous, automated data collection represents a break from a model that has been in place for decades: management by crisis. Instead of acting when a problem is already too severe to ignore, cities that adopt this kind of technology gain the ability to anticipate, plan, and act preventively. This has a direct impact on how public resources are allocated, on how satisfied residents are with urban services, and on the longevity of city infrastructure itself. Well-maintained pavement lasts far longer than pavement that gets repeated, late, emergency fixes over the years.
What Cyvl is building, in essence, is a layer of intelligence on top of the physical infrastructure of cities — a kind of digital nervous system that senses, processes, and communicates the health of roads in real time. This vision, which not long ago sounded like science fiction, is becoming reality on the streets of Boston and hundreds of other cities betting on technology as an ally for good public management.
And as these systems become more accurate, more affordable, and more integrated with other urban data platforms, the possibilities will only keep growing. The future of smart cities is not just about self-driving cars or connected buildings — it starts on the ground, on the road you travel every day, now monitored by an artificial intelligence working quietly to make sure it stays in good shape. 🚀
