Funding
Funding not disclosed
Founders
Product
Problem
Managing infrastructure assets involves processing diverse and complex inspection data, which often leads to delayed risk identification and inefficient budget allocation. This fragmentation hinders proactive maintenance and scalable monitoring efforts for critical infrastructure networks.
Solution
Inframindlabs provides an AI-powered platform that unifies inspection data from sources such as LiDAR, radar, and legacy reports into a comprehensive asset health overview. The platform facilitates early detection of risks by analyzing data for defects and structural anomalies, enabling optimized budget allocation through risk-ranked prioritization. It supports scalable network monitoring by automating data processing and delivering actionable insights, empowering engineers and managers with enhanced visibility for infrastructure management.
Target Audience
The primary customers are infrastructure engineers and asset managers responsible for the inspection, maintenance, and monitoring of bridges, tunnels, pipelines, and other critical networks.
Features
- Automated defect detection from multi-modal sensor data (LiDAR, radar, imagery).
- 3D digital twin navigation for interactive visualization of asset conditions.
- Historical data integration to track asset degradation and performance over time.
- Deformation and structural analysis capabilities for quantitative assessment.
- Defect measurement and severity analysis with millimetre precision.
- AI-driven risk ranking and predictive maintenance recommendations.
- Instant reporting and generation of maintenance datasets.
- Cloud-based accessibility for remote monitoring and data access.