01
Data Gap
Identify the rare condition, object, viewpoint, environment, or event the customer cannot capture well enough in the field.
Platform
Environment models, scenario generation, and synthetic data for physical AI.
Photon Echo turns customer environments into model-ready scenario libraries, structured synthetic datasets, and evaluation-ready outputs. The workflow starts with the data gap, defines the scenario, builds the environment, renders labeled outputs, and packages the result for training, testing, and system improvement.
Platform
The workflow turns a hard to capture condition into a model-ready environment and structured synthetic dataset.
02
Scenario Definition
Define actors, objects, environment, camera or sensor viewpoint, labels, and variation targets.
03
Scene And Asset Setup
Build or source scenes, objects, materials, and motion where needed.
04
Sensor And Render Configuration
Configure RGB, depth, segmentation, bounding boxes, and other available outputs.
05
Synthetic Data Generation
Generate controlled image or video datasets with synchronized labels and metadata.
06
Dataset Packaging
Deliver organized files, manifests, schemas, and documentation.
Why It Helps
Teams get a clearer path from a missing scenario to a usable dataset.
Instead of waiting for a rare field event, teams can define the condition, build the environment, generate labeled outputs from simulation, and package the data around the exact training, testing, or evaluation need.