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.
Flexible Deployment
3D Environment Engine
Industry-Ready Simulation
Synthetic Data Generation
Customizable Scenes
Automated Data Labeling
AI/ML Integration
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
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
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
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
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
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.
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.
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
How We Work
01.
BIM (or Scan) to Twin
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
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
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.
