Velocore bridges high-capacity multimodal Vision-Language-Action (VLA) foundation models with sub-millisecond deterministic robot actuation. We train in synthetic neural physics engines to deploy zero-shot generalist autonomy across factory lines and field robotics.
Test how Velocore's sub-millisecond neural policy stabilizes dynamic robot morphologies under real-time external torque disturbances.
An end-to-end stack engineering continuous physical cognition from multi-node synthetic cluster simulation to sub-millisecond edge silicon execution.
Simulates millions of parallel kinematic interactions per second with continuous rigid body dynamics, non-linear contact friction, and photorealistic ray-traced sensor pipelines.
Formulates complex robotic manipulation not as discrete classifications, but as continuous generative diffusion trajectories invariant under 3D Euclidean spatial rotations.
Proprietary parallel tensor matrix routines compiled down to bare-metal hardware. Executes 14.8B parameter multimodal tokens in 3.8ms with zero operating system jitter.
Massive domain randomization across physical mass, joint damping, and surface compliance guarantees policies trained purely in simulation deploy zero-shot on physical robots.
Unlocking non-stop operations in mission-critical environments where traditional scripted automation fails.
Bimanual robotic manipulation for sub-millimeter component alignment, deformable wire routing, and wafer handling with zero mechanical damage.
Autonomous parcel singulation, high-mix tote picking, and automated trailer depalletization handling arbitrary geometries and deformable bags.
Autonomous quadruped and wheeled rover fleets navigating petrochemical plants, offshore turbines, and nuclear facilities with real-time anomaly isolation.
Explore our 12-slide deep-tech investment deck detailing the physical AI foundation model, market expansion, capital efficiency, and executive roadmap.
Meet the founding executive driving Velocore's deep-tech research, proprietary kernel engineering, and commercial expansion.
Chief Executive Officer & Head of Systems Architecture
Marcus Sterling is an enterprise systems engineer and deep-tech founder specializing in high-throughput tensor acceleration, real-time sensorimotor architectures, and geometric foundation models for robotics. He established Velocore to eliminate the latency and fragility barriers preventing general-purpose autonomous robotics from scaling into physical industrial environments.
Standard deep learning frameworks introduce substantial runtime overhead due to Python execution layers and dynamic memory allocation. Velocore compiles trained VLA models into bare-metal C++ matrix kernels with warp-level register reuse and INT4/FP8 quantization, dispatching directly to edge hardware registers in 3.8 milliseconds.
Our synthetic physics engine requires extreme high-throughput parallel compute. We orchestrate clusters of 512+ high-performance tensor accelerators running synchronous physics rollouts and ray-traced depth generation at 4.2 million steps per second, which justifies our aggressive engagement with premier cloud compute credit programs.
Yes. Our runtime interfaces through standard real-time industrial protocols including EtherCAT, CAN-FD, and ROS 2 over deterministic Linux kernels. We support standard 6-DoF and 7-DoF manipulators (such as KUKA, FANUC, Universal Robots, and Franka Emika) as well as custom bimanual and quadruped systems.
Every generated trajectory passes through a deterministic Control Barrier Function (CBF) filter and real-time kinematic envelope verifier running at 1,000 Hz. If a proposed action breaches torque thresholds or proximity bounds, the system smoothly interpolates to an impedance-safe damping state within 1 millisecond.
Join our closed pilot program for electronics manufacturers, automated logistics operators, and robotics OEMs.