Real-Time City Simulation

Cities are
living systems. Now you can simulate them.

UrbanTwin AI builds living digital replicas of entire cities — powered by real-time inference pipelines and high-frequency state updates across every street, building, and energy flow.

Explore the Whitepaper ↗
Traffic Simulation
GPU Physics Modeling
Pedestrian Dynamics
Distributed Sim Workers
Emergency Evacuation
Real-Time Inference
Urban Heat Islands
RL Training Loops
Traffic Simulation
GPU Physics Modeling
Pedestrian Dynamics
Distributed Sim Workers
Emergency Evacuation
Real-Time Inference
Urban Heat Islands
RL Training Loops
What It Does

A digital twin for
every layer of the city.

01
Ingestion
Multi-Modal Data Fusion
Satellite imagery, IoT sensors, GIS layers, transit APIs, and weather feeds flow through real-time streaming pipelines into a unified spatial model — updated every second.
02
Simulation
Agent-Based Simulation
Millions of autonomous agents — pedestrians, vehicles, energy consumers — driven by reinforcement learning training loops and executed across distributed simulation workers running massively parallel scenarios.
03
Intelligence
Predictive Scenario Engine
Simulate large-scale evacuations, infrastructure failures, and cascading urban disruptions in real time. Test new transit lines, zoning changes, or extreme weather — and see the ripple effects before committing resources.
04
Accuracy
Continuous Calibration
The twin evolves alongside the city. Every prediction is continuously validated against live urban data streams — no drift, no stale assumptions.
05
Integration
Open API Layer
REST and GraphQL endpoints for every simulation parameter. Embed UrbanTwin intelligence directly into your existing planning stack.
Designed for Scale

Architecture that
matches the real world.

UrbanTwin is built to handle the full complexity of metropolitan-scale simulation — billions of state updates, thousands of parallel scenarios, sub-second responsiveness.

Billion-scale
State Updates
Metro-wide
Coverage
Sub-second
Query Response
1000+
Parallel Scenarios
How It Works

From raw data
to city intelligence.

01

Data Ingestion

Satellite, IoT, transit, and weather streams enter real-time streaming pipelines — normalized, spatially indexed, and pushed to high-throughput object storage in milliseconds.

02

Spatial Reconstruction

3D mesh construction, semantic layer mapping, and geospatial indexing build the physical and logical structure of the city.

03

Simulation Core

GPU-accelerated rendering and physics modeling power the simulation. Distributed workers execute scenarios at massive scale while RL training loops refine agent behavior every cycle.

04

Intelligence Layer

Scenario analysis, anomaly detection, and optimization recommendations — all validated against live urban data streams and surfaced through API and dashboard interfaces.

Under the Hood

Built for compute-intensive
urban simulation.

UrbanTwin runs on infrastructure designed for massive parallel workloads — the same class of hardware powering frontier AI labs, purpose-built for city-scale physics and behavioral modeling.
Compute
Multi-GPU Clusters
A100 / H100 nodes for GPU-accelerated rendering and physics simulation at city scale.
Scale
Distributed Simulation
Scenario workloads partitioned across nodes — massively parallel simulations with no shared bottleneck.
Storage
High-Throughput Objects
Petabyte-class object storage with sub-millisecond reads for spatial indexes and temporal snapshots.
Streaming
Real-Time Pipelines
Event-driven streaming architecture ingesting and processing millions of state updates per second.
What Happens Next

Pick a city.
We'll build the twin.

Tell us the metro area you're working in. We'll deploy a living model in under 72 hours — calibrated to your infrastructure data.

See It Live ↗