The city moved from reactive to proactive the moment the dataset arrived on November 27, 2023, compressing months of fieldwork and analysis into information leaders could act on immediately.
Rensselaer was working with outdated and inconsistent road information, so leaders didn’t know which streets to fix or when, and paving budgets were hard to defend. Without clear data, staff were stuck playing defense to resident complaints—answering “Why not my road?” in meetings and fielding a steady stream of 311 calls and emails. This reactive posture slowed maintenance decisions and extended the time residents waited for safer, smoother streets.
Rensselaer chose Cyvl to rapidly survey the entire network using vehicle-mounted LiDAR and sensors, scanning 43 roadway miles and capturing detailed surface condition data along with an inventory of 1,774 signs. Cyvl’s Infrastructure Intelligence platform used AI to transform this data into condition scores, prioritized repair lists, and defensible maintenance plans the city could stand behind. Delivered in weeks by November 27, 2023, the city received detailed, actionable pavement condition data and clear reports to build a comprehensive plan quickly and communicate the path forward to residents.
The city moved from reactive to proactive the moment the dataset arrived on November 27, 2023, compressing months of fieldwork and analysis into information leaders could act on immediately. With block-by-block condition scores across 43 miles and a verified inventory of 1,774 signs, crews can sequence work logically, fix issues faster, and show residents what’s next and why. Data-backed plans improved budget defense, scheduling, and public communication, so projects start sooner and are coordinated around community needs.