Graiphic
Graiphic provides SOTA, a unified AI software ecosystem that brings deep learning, computer vision, and generative AI directly into LabVIEW environments. The platform eliminates tool fragmentation by offering modular toolkits for the entire AI lifecycle, from data annotation to model deployment, powered by an ONNX Runtime engine and graphical dataflow interface.
- Artificial Intelligence
- AI Agents
- Developer Tools
- Industrial Automation
- Software Only
Funding
Founders
Product
Problem
Engineering teams working in LabVIEW environments face significant barriers when integrating modern AI capabilities, as most deep learning frameworks are Python-centric and require moving applications outside their native development environment. This forces engineers to either abandon LabVIEW for AI tasks or maintain complex, fragmented workflows that bridge multiple programming languages and tools, increasing development costs and slowing time-to-deployment for industrial AI solutions.
Solution
SOTA is a unified AI software ecosystem designed to eliminate tool fragmentation across the entire artificial intelligence lifecycle, operating natively within LabVIEW. The platform integrates deep learning, computer vision, generative AI, and hardware acceleration toolkits into a single coherent workflow, allowing engineers to build, train, optimize, and deploy production-ready AI pipelines without leaving the LabVIEW environment. Powered by an ONNX Runtime engine and a graphical dataflow interface, SOTA supports multiple model formats including PyTorch, SafeTensors, and GGUF, with CPU-to-GPU acceleration capabilities. The ecosystem includes specialized tools such as a LabVIEW Annotation Tool for end-to-end computer vision workflows and GraphMX for connecting ONNX Runtime execution providers to LabVIEW, making industrial-grade AI accessible to engineers who need deterministic, controllable systems.
Target Audience
Primary users are industrial engineers, researchers, and educators working in LabVIEW-centric environments who need to integrate deep learning, computer vision, and AI capabilities into their existing deterministic engineering workflows without switching to Python-based development.
Features
- Unified SOTA ecosystem covering the complete AI lifecycle: data preparation, annotation, model training, optimization, and deployment
- Native LabVIEW integration with ONNX Runtime engine supporting PyTorch, SafeTensors, and GGUF model formats
- LabVIEW Annotation Tool enabling dataset import, annotation, augmentation, local model training, and project generation in one workflow
- GraphMX accelerator toolkit for optimizing computation graphs and enabling CPU-to-GPU acceleration without Python dependency
- OpenCV integration for computer vision capabilities within LabVIEW environments
- llama.cpp runtime integration allowing direct loading and execution of GGUF models without format conversion
- Modular and interoperable toolkits for deep learning, computer vision, GenAI, and accelerator workflows
- Open-source documentation and community support through GitHub and dedicated support channels