Simulation

Smart Spatial Simulation

Smart Spatial Simulation & Synthetic Data Engine

Use Smart Spatial platform to create fully personalized 3D simulation environments and generate high-fidelity synthetic visual data. Build AI training datasets, test operational scenarios, and visualize edge cases — all with configurable environments, objects, and behaviors. Accelerate AI model development and simulation-based planning across industries.

What it is

Flexible Deployment

A software solution deployable on cloud or on-premises.

3D Environment Engine

Includes a 3D digital environment engine capable of recreating diverse, complex scenes.

Industry-Ready Simulation

Enables scenario simulation across industries using real or custom facility layouts.

Synthetic Data Generation

Supports the creation of photorealistic synthetic image and video datasets.

Customizable Scenes

Allows full personalization of objects, lighting, behavior, and environmental dynamics.

Automated Data Labeling

Batch-generate data with auto-labeling to train and validate vision models at scale.

AI/ML Integration

Integrates directly into AI/ML pipelines for faster, safer model development.
Smart Spatial Simulation
Why it matters

Generate Better Data

  • Generate visual datasets safely and cost-effectively
  • Train AI models on rare, dangerous, or hard-to-capture scenarios
  • Fill real-world data gaps and eliminate annotation errors
  • Eliminate privacy or safety concerns during model training

Improve Model Performance

  • Improve model generalization, accuracy, and fairness
  • Reduce time-to-train and data acquisition bottlenecks
  • Empower rapid testing cycles and continuous model improvement

Optimize Real-World Outcomes

  • Enable cross-department testing and simulation planning
  • Replicate and optimize operational scenarios before real-world rollout

Who It’s For

Smart Spatial’s Simulation & Synthetic Data Engine is ideal for teams building AI/ML models, planning facility operations, designing camera-based systems, or preparing for edge-case scenarios

Key Industry Applications

Manufacturing & Industrial

Transportation & Infrastructure

Warehousing & Logistics

Smart Buildings & Retail

Energy & Utilities

Healthcare & Hospitals

Construction & Engineering

Smart Cities & Public Safety

Defense & Aerospace

Simulation Goals We Support You Achieve

AI & Vision Model Training

  • Deliver clean, labeled, and diverse data at scale
  • Simulate complex edge cases not present in real datasets
  • Enhance generalization by varying light, occlusion, and camera angles
Supports: Object detection, segmentation, tracking, anomaly detection

Operational Scenario Testing

  • Create digital replicas of your facilities for simulation
  • Test various response scenarios and asset behaviors
  • Improve planning and mitigation strategies before rollout
Supports: Safety audits, operations planning, risk mitigation

Camera Planning & Optimization

  • Simulate camera placement with various lenses, models, and coverage fields
  • Evaluate coverage, occlusion, and optimal positioning for specific vision use cases
  • Generate synthetic data per camera configuration to validate system performance
Supports: Security, safety, operational AI, smart infrastructure planning

Custom Environment & Object Control

  • Define layout, lighting, time-of-day, object count, and agent behavior
  • Inject randomness and variation into training sets
  • Use custom prompts to expand and personalize simulations
Supports: Region-specific models, rapid experimentation

How It’s Used – Example Applications

Tunnel Safety AI

Generate synthetic video of road tunnels with lost cargo or dropped objects (e.g., cones, boxes, suitcases). Vehicles partially occlude the objects, lighting varies by scene, and assets are placed naturally as in real-world incidents. The simulation feeds annotated footage directly into ML pipelines to train and validate vision-based safety detection models.

Smart Spatial Example Applications
Smart Spatial Example Applications

Camera Planning & Placement Optimization

Simulate camera placement and coverage in complex environments to plan optimal configurations for vision-based systems. Teams can test camera locations, angles, lenses, and model selection virtually before physical installation:

  • Digitally replicate environments such as warehouses, tunnels, retail spaces, or transportation hubs.
  • Simulate camera behaviors (field of view, focal length, lens distortion).
  • Visualize coverage heatmaps and occlusion zones.
  • Generate synthetic test data for each configuration to pre-train or validate AI models.

Factory Inspection Training

Simulate manufacturing floors with variable machinery layouts, lighting conditions, and types of product defects. Used to build training datasets for AI models identifying surface issues, process bottlenecks, or equipment failures.

Smart Spatial Example Applications
Smart Spatial Example Applications

Retail & Smart Building Analytics

Create shopping environments with randomized product placements, foot traffic patterns, and lighting scenarios to simulate checkout behavior, people counting, and shelf monitoring.

Success Stories

See how businesses are leveraging Smart Spatial to create innovative, efficient, and sustainable building solutions — faster than ever

Kevlinx is transforming client engagement with their BRU01 Digital Twin in Brussels. Powered by Smart Spatial’s platform, it provides high-fidelity visualization of the facility, even during construction, speeding time-to-market. Clients gain true to life access to tour the facility and learn about the various data hall layouts and cooling technologies. The Digital Twin showcases best in class engineering focused on sustainability
PNY and Automation are elevating modular data center engagement with their Digital Twin, showcased at NVIDIA GTC and Hannover Messe 2025. Built on Smart Spatial’s platform, the immersive, voice-controlled environment highlights PNY’s NVIDIA-powered HPC solutions in a lifelike, interactive 3D experience. Visitors can explore server halls, cooling systems, and power infrastructure while receiving real-time operational context. This dynamic demo accelerates customer understanding of PNY’s cutting-edge technologies, transforming complex infrastructure into an approachable and memorable experience.
HPE is redefining data center intelligence with their next-generation Operational Digital Twin. Powered by Smart Spatial, the platform transforms their conceptual Bay Area facility into an immersive, AI-driven environment that seamlessly integrates real-time monitoring, predictive analytics, and spatial computing. Users experience intuitive, voice-controlled navigation and gain deep operational insights into power, cooling, and network systems—all within a 4D interface that enables exploration through space and time. This visionary system sets the benchmark for intelligent, autonomous data center operations.

How We Work

01.

BIM (or Scan) to Twin

Building Your Foundational Simulation Environment
Duration: 1-2 Weeks

In this initial phase, the Smart Spatial team ingests your existing BIM Models (REVIT, IFC, etc.) or conducts precise 3D scans of your site and/or equipment. From this data, we create a high-fidelity 3D digital replica of your assets and their environment, complete with realistic materials and spatial accuracy. This foundational digital twin provides the core framework for your custom simulation scenarios.

Benefits: This accurately rendered digital twin immediately provides a realistic and configurable base for all your simulation needs. It allows for the virtual replication of complex real-world scenes, providing the essential visual context for developing AI training datasets, testing operational scenarios, and visualizing edge cases without the limitations or costs of physical environments.

02.

Environment & Scenario Configuration

Defining Dynamic Simulations and Behaviors
Duration: 1-3 Months

During this phase, we collaborate to define the specific parameters of your simulation environment and scenarios.

This involves:
- Customizing the 3D environment: Adjusting layouts, lighting conditions (time-of-day, weather), and material properties.
- Populating with objects and agents: Adding diverse assets, defining their appearance, placement, and behaviors (e.g., foot traffic patterns, machinery operations, specific failure modes).
- Injecting randomness and variation: Configuring parameters to ensure diverse and robust synthetic data generation, including rare or challenging "edge cases."

Benefits: This granular control allows you to create fully personalized and highly realistic simulation environments that precisely match your real-world challenges or desired training conditions. You can replicate complex operational scenarios, specific object interactions, or critical incidents that are difficult, costly, or dangerous to capture in reality.

03.

High-Fidelity Synthetic Data Generation & Integration

Fueling AI Models and Operational Planning
Duration: Per Scope

With the dynamic scenarios configured, our engine then batch-generates high-fidelity synthetic visual data (images, videos) directly from your personalized 3D simulation environment. This data comes with automated, pixel-perfect labeling, eliminating manual annotation errors and accelerating your workflow. This phase also focuses on integrating this data and your simulation outputs directly into your AI/ML pipelines or operational planning tools.

Benefits: This provides a continuous supply of clean, diverse, and perfectly labeled datasets at scale, drastically accelerating AI model development and improving generalization, accuracy, and fairness. You can test new models safely, validate camera placements, optimize real-world operational outcomes, and prepare for critical situations, significantly reducing time-to-market and enhancing overall system performance.

Experience the Smart Spatial Platform

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