We train large multimodal diffusion and autoregressive models that synthesize production-ready parametric CAD, compliant multi-axis robotics, and finite-element stress optimized assemblies in seconds.
Continuous live demos, laboratory benchmarks, robotic kinematic trials, and keynote archives streamed directly from our New York engineering facilities.
Real-time synthesis of involute gear profiles and harmonic drive assemblies conforming to ISO-6336 load standards.
Traditional generative design merely performs basic topology optimization. Dragon AI’s foundation models understand geometric assembly graphs, tolerance stacks, kinematic degrees of freedom, and CNC milling constraints.
Input desired path vectors, velocity curves, and torque profiles. Dragon AI synthesizes closed-loop 4-bar and 6-bar mechanical linkages, compliant flexures, and harmonic gear reductions without manual kinematic solvers.
Unlike naive polygon meshes from video games, our generative pipeline outputs valid boundary representations (B-Reps), editable feature trees, native SolidWorks macros, and ISO STEP files ready for CNC 5-axis toolpaths.
A neural surrogate model trained on over 80 million Ansys and Abaqus finite element simulations predicts Von Mises stress, thermal gradients, and resonant vibration modes in under 12 milliseconds.
End-to-end co-design of robotic skeletons and actuator motor windings. Synthesizes high-torque quasi-direct drive motors with optimal mass distribution for highly agile humanoid robots and aerospace drones.
Automated generation of stochastic triply periodic minimal surface (TPMS) gyroid internal lattices for additive manufacturing, optimizing heat dissipation and acoustic dampening for hypersonic nacelles.
Agentic vision pipelines ingest legacy 1960-1990 scanned military drafts, 2D TIFF blueprints, and damaged schematics to reconstitute fully editable parametric assembly trees with GD&T annotations.
Simulate real-time parametric synthesis. Select an engineering archetype below or type custom constraints to watch the generative optimization lattice and stress analysis render live.
Beyond our proprietary model compute nodes, Dragon AI hosts New York City’s premier private physical AI collective. Headquartered at 375 Hudson Street in SoHo / Hudson Square, we bring together leading researchers from Columbia, NYU Courant, defense engineers, and venture-backed robotics founders.
Direct fiber interconnect with 400Gbps low-latency lines to local cloud testbeds.
5-axis Haas CNC mills, Markforged carbon 3D printers, and dynamic torque dynamometers.
Bi-weekly off-the-record briefings on foundational model architectures and aerospace grants.
$250,000 monthly compute grant pool awarded to promising non-profit and academic researchers.
Get 24/7 access to the Manhattan hardware labs, private Discord channel, and high-throughput model API keys.
Introducing SO(3)-equivariant continuous convolution networks capable of predicting fatigue cycles under non-uniform thermal stress.
Solving the boundary reconstruction gap to extract guaranteed manifold, C2-continuous spline geometries from unconstrained multi-view captures.
Benchmarking generative carbon-fiber linkages under uneven Manhattan gravel and asphalt testing environments with sub-millisecond feedback.
To facilitate rapid collaborative FEA streaming, neural video rendering, and heavy parametric STEP file transfers, this endpoint serves as an authorized high-bandwidth edge node in our distributed Manhattan research backbone.