Urban Asset Twin

High-resolution urban digital twins integrate geospatial, structural, and environmental data to streamline property-level risk assessment and underwriting decisions.

Request a demo

How does it work?

Traditional property underwriting relies on static reports and manual inspections, leading to slow processes and incomplete risk profiles. Urban digital twins aggregate real-time geospatial and structural data at the asset level, enabling precise risk quantification and faster decision-making.

Enhanced Risk Accuracy: The digital twin integrates 3D structural models with historical loss and environmental data to deliver precise risk scores at the building level. This granular insight reduces unexpected claims by identifying hidden vulnerabilities.
Reduced Inspection Costs: By leveraging remote sensing and sensor feeds, underwriters can assess property condition without field visits. This reduces travel expenses and manual labor while maintaining data quality.
Faster Underwriting: Automated data ingestion and analysis shortens the underwriting cycle from weeks to hours. Faster decision-making improves operational efficiency and customer satisfaction.
Predictive Loss Modeling: Machine learning algorithms simulate various hazard scenarios using the digital twin’s detailed asset models. Insurers can forecast potential losses and adjust pricing proactively.
Geospatial Insights: High-resolution spatial analytics map risk exposures across urban landscapes, highlighting clusters of high-risk assets. This supports portfolio diversification and targeted risk mitigation strategies.
Seamless Workflow Integration: The platform connects with existing underwriting systems via APIs for real-time data exchange. This integration streamlines processes and avoids redundancy in data management.

Frequently asked questions

Have a different question and can’t find the answer you’re looking for? Reach out to our support team by sending us an email and we’ll get back to you as soon as we can.

How does an urban digital twin compare to traditional GIS for underwriting?: While GIS provides spatial mapping, digital twins layer dynamic building and environmental data for real-time risk insights. This makes digital twins more actionable than static GIS maps in underwriting workflows.
Why are digital twins popular among modern insurers?: Insurers adopt digital twins for their ability to centralize diverse data sources and automate risk analysis. Popularity stems from improved accuracy and reduced operational costs.
What limitations should I consider when using urban digital twins?: Data accuracy depends on the quality and recency of remote sensing and IoT feeds, which can vary by location. High-resolution models also require significant storage and processing resources, potentially increasing infrastructure costs.
Can alternative data sources replace LiDAR in a digital twin?: Photogrammetry and radar imagery can supplement or partially replace LiDAR for 3D modeling at lower cost. However, they may lack LiDAR’s precision in capturing fine structural details.
How often should digital twin data be updated for accurate underwriting?: Best practice is to refresh core spatial and sensor data at least quarterly and perform event-driven updates after major incidents or renovations. Frequent updates ensure risk models reflect current asset conditions.
Are there alternatives to digital twins for asset-level underwriting?: Traditional approaches rely on manual inspections, historical loss data, and third-party reports, which can be slower and less precise. While simpler to implement, they lack the real-time granularity digital twins provide.

Geospatial AI Platform

Intelligence

AI & foundation models

Deep-learning and foundation models turn raw imagery into ready-to-use insights, so you ship answers instead of training pipelines.

Experience

Conversational workflow

Ask questions in plain language and the platform responds with charts, visualizations, and next step suggestions.

Speed

GPU-accelerated cloud

Cloud-native architecture spins up on-demand GPU clusters that scale from a single scene to global archives—no manual ops, no bottlenecks.

Data

Any sensor, any format

Optical, SAR, drone, IoT, vector or raster—ingest, fuse, and analyze without conversion headaches.

Visualization

Insight you can see

Real-time 2D / 3D maps and export-ready plots make results clear for engineers, execs, and clients alike.

Boost your productivity. Experience Geospatial AI.

Turn satellite, drone, and sensor data into clear, real-time insights using powerful AI – no complex setup, just answers you can see and act on.

Request a demo