The city now has current, trustworthy pavement condition data for every mile surveyed, plus a complete sign inventory that ties maintenance to safety outcomes residents can feel.
Carpentersville’s street program relied on outdated and inconsistent data, making it hard to know which roads to fix and when, and leaving budgets based on estimates rather than facts. Without a clear prioritization method, the city was reactive to complaints and often had to defend choices in council meetings with limited evidence. Manual windshield surveys were slow and inconsistent, so by the time reports were compiled the information was already stale and crews were stuck in short-term fixes instead of a strategic plan.
Carpentersville chose Cyvl to rapidly capture citywide pavement and sign data using vehicle-mounted LiDAR and high-resolution sensors, surveying 96 roadway miles and inventorying 5,708 signs. Within weeks, Cyvl’s Infrastructure Intelligence platform used AI to generate network and block-level condition scores, prioritized repair lists, and defensible multi-year scenarios that align scope, budget, and crew capacity. Delivered on May 13, 2025, the city received detailed, actionable pavement condition data and map-based reports that make it simple to communicate plans and move from decision to action fast.
The city now has current, trustworthy pavement condition data for every mile surveyed, plus a complete sign inventory that ties maintenance to safety outcomes residents can feel. With clear scores, map-based justification, and budget scenarios, leaders can explain decisions in plain language and schedule work without delay. Because delivery happened in weeks rather than months, the time between data collection and project implementation shrank, so residents see improvements sooner.