Turkey earthquake, 2023. Real-time rescue coordination while rubble was still settling.
Real environments from real sensors. Any video, any device — metrically accurate 3D in minutes.
Ingest from phone cameras, drones, LiDAR, or satellite imagery. Automatic multi-modal fusion produces metrically accurate 3D reconstructions with sub-centimeter precision. No special hardware required.
Every object, material, defect, spatial relationship — encoded as a structured, queryable graph.
Query the physical world like a database. Filter by object type, material, spatial region, or semantic label. Every element is semantically tagged with full spatial context.
Fire spread on actual terrain. Structural failure on actual geometry. Real physics, real data.
Run physics simulations on real-world geometry. Model fire spread, structural loads, flood dynamics, and environmental change using captured terrain and structures as the simulation substrate.
From capture to centimeter-level metric 3D — in minutes.
Percept's foundation model processes any sensor input through three stages. Each stage builds on the last. The output is a structured, queryable, simulatable representation of the physical world.
Turn any video, LiDAR scan, or satellite image into a metrically accurate 3D model. Sub-centimeter precision, real-world coordinates, in minutes not hours.
Every object, material, defect, spatial relationship — encoded as a structured, queryable graph. Not pixels. A representation engineers and AI agents can reason over.
Fire spread on actual terrain. Structural failure on actual geometry. Flood inundation on actual topography. Real physics, real data, real predictions.
Physics simulations run on reconstructed geometry — not approximations.
Every object, every lane, every material — indexed and queryable.
The same Reconstruct → Understand → Simulate foundation, delivered in two forms: a visual operating environment for operators, and a programmatic API for builders.
Emergency managers, inspectors, city planners — upload footage, query the scene graph, run simulations. No code required.
Drone manufacturers, robotics platforms, and autonomous systems. Three endpoints. One SDK.
import percept # Reconstruct from any sensor input scene = percept.reconstruct( source="drone_capture.mp4", mode="metric_3d" ) # Query the scene graph defects = scene.query( type="structural_defect", severity="critical" ) # Simulate physics result = scene.simulate( scenario="fire_spread", duration=4, wind_speed=12.5 )
From earthquake response to bridge inspection. Real deployments. Real outcomes.
Real-time spatial intelligence during active disasters. Structural damage assessment — deployed in seconds.
Automated defect detection across bridges, pipelines, and utilities. Centimeter-level precision.
City-scale 3D reconstruction from standard capture devices. Persistent, compounding accuracy.
Predict fire spread across actual terrain with detected fuel types and wind vectors.
Corrosion detection and progression modeling at measured rates across pipeline networks.