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Backed By Urbint
Use this AI to predict & prevent infrastructure damage for utility & construction firms.
Urbint's AI analyzes real-world data to predict and prevent infrastructure incidents, pinpointing high-risk areas and recommending proactive interventions.
Analyze environmental, infrastructure, and community data
Identify and prioritize high-risk areas
AI-powered recommendations
Real-time action assignment
Integrates with existing workflows
SOC 2 Type 2 certified security
AI-Powered Damage Prevention
AI-Powered Worker Safety: Smart Job Safety Briefs (JSBs) identifying high-energy hazards and direct controls at the point of work.
AI-Powered Damage Prevention: Identifies and prioritizes high-risk excavations (including No-Call-Ins) for targeted intervention.
Emergency Preparedness & Response: AI-driven storm impact prediction and comprehensive storm management (crew coordination, logistics, resource staging).
Real-Time Risk Intelligence: Alerts on threats to workers and infrastructure up to a week in advance.
Model of the World: Dynamic digital twin combining environmental, community, and infrastructure data.
Real user experiences from across different platforms
“If our safety observations are focused on compliance, checklists, and paperwork, we are missing an important opportunity. When performed well, site visits allow us to learn from front-line employees, focus on the energy, and improve our ability to control the 'stuff that kills you' (STKY). To be effective in these engagements, we need technology like Urbint that surfaces STKY so that we engage in the right places, at the right time, about the most important things.”
National Grid VP Walter Fromm (via Case Study)
Pre-2022
ng to transition from reactive to predictive risk management.
us Injuries and Fatalities (SIFs) and excavation damage.
onal data or a small field workforce.
ogistics software.
Transforming operational risk from reactive to predictive by providing hyper-specific, actionable AI intelligence to prevent serious incidents, reduce costs, and enhance grid and community resilience.
Pricing is customized and not publicly disclosed (common for enterprise utility software). Requires integration with existing complex utility IT systems, which can be an implementation challenge. AI predictive accuracy is dependent on the quality and volume of internal customer data provided.