01
Multimodal fusion
Merge CAD geometry, visual, depth, inertial, and LiDAR signals into a coherent spatial model with time-synced confidence.
Realtime scene understanding
CAD-native spatial AI for environments that move, adapt, and reason.
We turn CAD context and raw sensor streams into structured spatial intelligence for robotics, autonomy, mapping, and industrial digital twins.
Core capabilities
Built for systems that need to understand space, not just see it.
02
World reconstruction
Build dense scene maps, semantic layers, and motion-aware geometry grounded in design intent and reliable as environments change.
03
Decision-ready outputs
Export tracking, occupancy, and trajectory signals that plug cleanly into autonomy, analytics, and simulation pipelines.
System workflow
A spatial pipeline designed for real-world complexity.
Ingest synchronized sensor feeds from moving platforms and fixed infrastructure.
Assemble geometry, semantics, and motion fields into a live 3D scene graph.
Send navigation, safety, and optimization outputs where decisions need to happen.
Why it matters
From warehouse fleets to digital twins, every environment becomes computable.
CAD Spatial AI is most valuable when design geometry, semantics, and live sensor data are treated as one system. That is how machines move with more confidence, operators understand change faster, and simulation stays anchored to reality.
Latency envelope
< 20 ms
Scene updates
60 Hz
Deployment modes
Edge + cloud
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