Graiphic Studio / The future of dataflow

R&D and Innovation Center.

Building the future of dataflow programming.

With Graiphic Studio and FROG, we are creating a new foundation for graphical software: programs engineers can build visually, inspect in detail and evolve alongside AI.

Explore Graiphic Studio Discover FROG
The first FROG IDE / Active development

Graiphic Studio in action

Explore the Front Panel, Block Diagram and Source views in our graphical programming environment.

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Graiphic Studio / A new foundation

Why this changes graphical programming.

Our ambition is to bring visual engineering and AI-assisted software creation into the same workflow, with the program itself open to inspection.

01 / Understand

One program. Three coordinated views.

Front Panel, Block Diagram and structured .frog source describe the same program. Engineers can move between the interface, the dataflow logic and its JSON representation to understand how the software is built.

02 / Create

A language for engineers and AI.

Graiphic created FROG from the ground up. Its open, structured source is designed so software tools and AI can generate programs whose graphs, connections and structure remain available for human review.

03 / Build

An engineering effort from language to execution.

We are developing the language architecture, execution contracts and Studio together. Public specifications, conformance tests and working examples turn that vision into an engineering foundation for modular graphical software.

See the progress. Studio is in active development. The video demonstrates its authoring environment; runtime coverage, compilation and deployment capabilities continue to evolve.

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From research to product

Research that becomes products.

SOTA and EKILOHA illustrate two paths from our R&D and Innovation Center: a software ecosystem and an energy solution developed from NEST research.

SOTA manager: selecting the Deep Learning Toolkit for LabVIEW
SOTA ecosystem managerSource: Graiphic

SOTA: AI natively in LabVIEW.

After five years of development, Graiphic achieved a major engineering milestone: bringing AI natively into LabVIEW. With SOTA, engineers train and run models using LabVIEW VIs without having to write their application logic in an external language. Deep Learning, Computer Vision, Accelerator and GenAI work within one ecosystem, with local training to keep industrial image datasets in your own environment.

Youssef Menjour exploring software architectures at the Graiphic research whiteboard
Ideas, architecture and experimentation in our own lab.
Our lab / Our commitment

Innovation is in our DNA. We fund the work.

Graiphic built and financed its own R&D lab because innovation is central to what we do. We finance our research from within Graiphic, giving our engineers the freedom to pursue long-term ideas and turn them into technologies they can put to work.

Industrial experience feeds the research; the research creates new tools. SOTA and EKILOHA grew from this commitment. FROG and Graiphic Studio are where we are building the next chapter.

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On the international stage

Research comes to life before an audience of engineers.

On June 9, 2025, Youssef Menjour presented SOTA to the ONNX community at Intel’s Santa Clara campus. A concrete expression of our ambition: turn research into technology engineers can use within LabVIEW architectures.

Read the article and explore the event
Intel · Santa Clara · 2025

SOTA at the ONNX Community Meetup

Youssef Menjour’s full presentation: AI, ONNX Runtime and graph computing in LabVIEW.

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Published research

Six publications documenting our research.

These papers describe architectures and research directions. They do not all represent shipped features.

GO whitepapers library
  1. 01

    SOTA GO

    ONNX graphs and visual orchestration in LabVIEW.

    Read the paper
  2. 02

    GO HW

    From models to systems: hardware, I/O and heterogeneous targets.

    Read the paper
  3. 03

    FROG

    A proposal for an open graphical dataflow language.

    Read the paper
  4. 04

    GO GenAI

    Orchestrating models, logic and memory in one graph.

    Read the paper
  5. 05

    GO IML

    Learning informed by constraints, knowledge and physics.

    Read the paper
  6. 06

    NEST

    A local building energy architecture whose research informs EKILOHA.

    Read the paper
Verifiable results

Measure. Publish. Make it reproducible.

Methods and source files are available alongside the results.

Work in progress

From graphical programming to smarter energy.

Each project shows its current status and links to a public resource.