The Neural World Engine for
Physical Robot Intelligence
Velocore is engineering deterministic, sub-4 millisecond Vision-Language-Action (VLA) foundation models trained across synthetic physics clusters to power the next generation of industrial automation.
The $142B Failure of Scripted Automation
Modern manufacturing and logistics rely on rigid, hard-coded trajectories programmed over weeks. Any minor displacement, part deformation, or lighting shift causes catastrophic line stoppages.
Zero Adaptability
Current cobots fail when target objects shift by even 2 millimeters.
Cloud Latency Fatalities
Cloud AI API round-trips (300-800ms) result in dropped payloads and motor collisions.
Data Wall
Physical teleoperation collection costs $450/hour and risks destroying mechanical hardware.
Velocore: The Synthetic-Trained VLA Policy
We bypass slow, dangerous real-world robot data collection. We train generalist physical foundation models inside a massively parallel, ray-traced neural physics simulator generating 4.2 million interaction steps per second.
Sub-4ms Edge Runtime
Custom tensor matrix assembly executes 14.8B parameter models directly on robot controllers with zero OS jitter.
Zero-Shot Sim2Real Generalization
Policies transfer directly to physical hardware with 99.98% trajectory safety and sub-0.04mm tracking accuracy.
SE(3) Equivariant Diffusion Policy
By building 3D Euclidean rotation and translation symmetries directly into the transformer architecture, Velocore inherently understands spatial orientations without needing millions of redundant demonstrations.
Massive Domain Randomization & Sim2Real
Accelerated Compute Dependency & Scale
Training our synthetic neural world engine requires petascale tensor acceleration. Our architecture is designed from day one to consume distributed accelerator clusters with maximum hardware utilization.
Capturing the $142 Billion Robotics Revolution
Global industrial robotics, logistics, and mobile autonomy market by 2030.
High-mix bimanual electronics assembly & parcel fulfillment automation.
First 3-year recurring runtime licensing across 25 OEM partners.
Commercial Traction & Hardware Validation
Micro-Connector Insertion
Zero-shot handling of 8,500+ novel micro-connectors with 99.98% insertion reliability and 2.4x throughput increase.
Deformable Polybag Singulation
180,000+ continuous autonomous parcel picks with zero human intervention or gripper jams.
High-Margin Recurring Software Licensing
Installed directly on robot controller hardware with continuous model checkpoint pushes and telemetry monitoring.
OEM custom digital-twin policy synthesis allowing rapid commissioning of new factory assembly stations.
Defensible Multi-Layer Moat
SE(3) Equivariance
Rotational invariance eliminates the need for expensive multi-angle datasets.
Bare-Metal Tensor Kernels
Direct assembly execution achieves sub-4ms latency impossible for Python-based frameworks.
Synthetic Scale
Self-generating physics edge-cases create an unbridgeable data advantage over physical fleets.
Leadership & Systems Engineering
Marcus Sterling
FOUNDER & CEOFounding executive and deep-tech architect specializing in high-throughput tensor computing, real-time sensorimotor architectures, and geometric deep learning. Marcus is leading Velocore to build the definitive physical foundation model for general-purpose robotic autonomy.
$4.5M Seed Round Allocation
Capital Allocation
Target Milestones (18 Months)
- ✓ Launch Velocore-V6 with zero-shot bimanual tool use
- ✓ Deploy on 450+ industrial robot arms
- ✓ Reach $3.8M ARR in recurring software licenses
- ✓ Establish Series A institutional syndicate