As infrastructure ages across the United States and budgets tighten, an Israeli startup has developed an artificial intelligence platform designed to help civil engineers and government agencies identify which public systems require urgent repair and maintenance.
Dynamic Infrastructure, founded in the eighth year ago by Saar Dickman and Amichai Cohen, created a platform that uses AI to accelerate and improve the accuracy of fault attribution when infrastructure systems fail. The company's approach emphasizes human oversight at critical decision points, with civil engineers reviewing the AI's analysis before final determinations are made about infrastructure conditions and liability.
"The company does not aim to replace civil engineers, but to become a helping hand when processing massive amounts of data," Dickman, the CEO and founder, explained. "Artificial intelligence cannot fully replace engineers and can only support them."
The platform processes information collected by certified inspection engineers or certified contractors who are paid for their work. Once an inspector provides results, the infrastructure owner becomes liable because they paid for the inspection service and are supposed to know what is happening based on the inspector's findings. The AI system then assists in analyzing that information, but human civil engineers revise the work at multiple points in the analysis, adding what Dickman described as "an extra layer of reassurance to the final result."
Expanding Public Sector Adoption
Governments from 13 US states already use Dynamic Infrastructure's platform, and Arkansas recently became the latest state to adopt the technology for infrastructure analysis. The company reported a 100% contract renewal in the second year, suggesting sustained confidence from existing government clients.
The company reported $1 million in revenue in the second year and projected that it could triple that in the coming year. Dynamic Infrastructure plans to expand to the Australian and European markets, with the stated objective of providing every public or county engineering and maintenance department with an AI-based "virtual engineer" that works alongside professional teams.
Dickman emphasized the practical necessity of such tools in an era of aging infrastructure and constrained budgets. "In a world where infrastructure is aging and budgets are limited, the system enables authorities and state transportation agencies to gain clear visibility into the condition of their assets and manage them efficiently and proactively," he said.
Technical Development and Human Judgment
The platform's development involved distinctive challenges related to distinguishing between different types of structures and their patterns of deterioration. Dickman noted that modern structures typically have 30 to 40 years of data from their construction to the present, while antique structures may be hundreds of years old, requiring the system to learn different degradation patterns.
The company had to train the system to distinguish between, for example, a brick falling from a modern brick structure and one falling from a medieval arch built 400 years ago. Dickman credited the development team's composition as crucial to the system's effectiveness: "The system was developed with a team of civil engineers, not just programmers, so they knew what the AI needed to learn, where to find mistakes and how to correct them."
Dickman shared an example from the early development of the system that illustrates both the learning curve and the importance of human oversight. A photo from one of the company's clients in Greece showed a red-haired woman standing on a bridge, and the system initially identified the woman as rust on the bridge. "That does not happen today," Dickman said, noting that the error served as both a learning experience and a reminder of why human review remains essential.
The company's emphasis on human checkpoints reflects a broader recognition that AI systems, while powerful tools for processing large datasets and identifying patterns, remain imperfect and require professional judgment. Dickman was explicit that the company's approach to liability depends on this human element: the infrastructure owner's responsibility stems from their payment for inspection services and their obligation to act on the information provided by certified professionals, with the AI system serving as a tool to help process that information efficiently.
Why This Matters:
Aging infrastructure represents a critical public policy challenge affecting public safety, economic productivity, and quality of life. The intersection of limited government budgets and deteriorating infrastructure creates pressure to prioritize repairs strategically. This platform's approach—combining AI data processing with mandatory human professional review and clear liability frameworks—reflects a center-left perspective on technology governance: tools should enhance human decision-making rather than replace it, public institutions must maintain accountability for infrastructure safety, and certification requirements and professional standards remain essential safeguards. The emphasis on keeping civil engineers in decision-making roles, ensuring certified professionals conduct inspections, and maintaining clear liability chains demonstrates how technological innovation can serve public needs while preserving institutional oversight and professional accountability. As governments adopt such systems, questions about data access, transparency in decision-making, and equitable resource allocation across communities will remain important considerations for public agencies managing these tools.