LLM fine-tuning, IsaacSim training pipelines, on-prem GPU leasing, and self-hosted deployment. All on hardware you control.
Parameter-efficient fine-tuning (LoRA / QLoRA) of open-weight models like Llama, Qwen, and Mistral. Dataset preparation, eval harnesses, quantization, and on-prem serving. Your weights and data stay inside your network.
NVIDIA IsaacSim / IsaacLab environment authoring, synthetic dataset generation, domain randomization, and RL policy training across parallel environments. PPO, ONNX export, TensorRT-optimized inference.
Multi-step agents with tool use, RAG over private data, and self-hosted embeddings. Works offline, air-gapped, or hybrid. Integrates with your existing identity and data stores.
Gaussian Splatting for photorealistic 3D capture from video. NVIDIA Omniverse digital twins for sim-based training, inspection workflows, and high-fidelity ground truth.
Lease on-prem RTX and DGX Spark workstations for dedicated training, fine-tuning runs, or long-horizon development. Hardware sourcing, cluster build consulting, and Jetson-class edge deployment.
End-to-end pipelines for deep learning, CNNs, computer vision, and generative AI. Dataset curation, training, evaluation, and deployment. Reproducible by construction.
Send a short description of the problem. We'll tell you what's tractable, what isn't, and what the shortest path to a working prototype looks like.
