Need: governed AI and simulations

Governed AI and simulation workspaces on your infrastructure

Give platform and product teams one project-centric place for models and sims, under your policies, next to the data. One environment on AWS, GCP, or on-prem Kubernetes.

AI & simulations Models, sims, and apps on the project
Your control plane AWS, GCP, or on-prem Kubernetes
Project governance Access, storage, and collaborators by project

What a platform team standardizes

One environment install, then a shared catalog at containers.projecteureka.ai. Teams launch the same app containers and submit the same flow containers, under project access.

Simple install Download each container once from that catalog. Every project on the environment reuses it.
App containers Jupyter, VS Code, and a Linux desktop from the public catalog, the same set for every team.
Flow containers OpenFOAM, GROMACS, and PyTorch on a saved flow the team can submit again.
Project access Who can launch, submit, and open the data stays on the project.
A running desktop stays on the project. Stop, delete, or connect from the card.
Home, project files, and a managed bucket. This picture is Google Cloud Storage. On AWS, the same action creates an Amazon S3 bucket.

Why AI and simulation work stalls in the enterprise

Notebooks on one stack, simulation codes on another, datasets in object stores, and desktops that never match. Teams spend too many cycles on setup before they get to models and results.

projectEureka™ is a project-centric platform you deploy on infrastructure you choose: browser apps, secure long-running work, and multi-cloud buckets under project-level access.

  • Environment under policy: Run on AWS, GCP, or on-prem Kubernetes
  • Secure virtual workstations: Consistent browser desktops and apps next to data; personal laptops still work as the client

For platform, product, and R&D teams

Enterprise value shows up when multiple groups share a governed pattern: secure, consistent virtual workstations and project environments.

  • Platform teams: Standard place to land AI and simulation workspaces with clear deploy and storage options
  • Product and engineering: Shared projects for model work, evaluation, and simulation support tools
  • Applied R&D: Persistent environments for experiments that must survive overnight and handoffs
  • Security-minded IT: Project-based access, environment you operate, buckets attached under project control
Projects are how teams and data stay organized
Browser launchers for AI and simulation stacks

AI and simulation apps in the browser

Standardize the browser tools from containers.projecteureka.ai. A simple install downloads an app container once. Every project on that environment can launch it. Personal machines stay the client.

  • App containers: Jupyter, VS Code, RStudio, ParaView, and a Linux desktop. Download, then create the app.
  • Open apps where the data already is
  • Resource visibility: Cores, RAM, and GPU shown on running apps in the UI
  • The next person can open the same project

Apps and jobs that keep running

A downloaded app or a submitted flow keeps running after the laptop sleeps. The next shift supervises it from the browser, under the project's access.

  • Under the project's access, next to the data
  • Keeps running when devices disconnect or people change shifts
  • Access from anywhere your policies allow, in the browser

Platform overview → · Security architecture →

Long-running work stays in the environment you operate

How an enterprise deployment looks

The platform team installs one environment. App containers and flow containers then download from the catalog onto that environment.

Environment

AWS, GCP, or on-prem Kubernetes

Project

Team or initiative: name, collaborators, access, and scope

Catalog

App containers for the browser. Flow containers for jobs the team submits again.

Storage

Home directory, project storage, multi-cloud buckets (AWS S3, GCP GCS)

Enterprise is a licensed path with commercial engagement. Teaching and research free software is a separate path with its own terms; cloud and cluster costs always remain yours.

Multi-cloud buckets attached to the project

Storage next to AI and simulation work

Attach multi-cloud buckets no matter where the environment runs. Teams open apps where the data already lives.

  • Home directory: Personal space for each user in the environment
  • Project storage: Shared space for the team’s initiative
  • Multi-cloud buckets: AWS S3 and GCP GCS today, more to come, usable from AWS, GCP, or on-prem K8s deploys
  • OrangeFS®

OrangeFS® support → Technical details →

Who it fits

Company capability and direction

Omnibond has run cloud computing and data-heavy systems at very large scale. That experience sits behind projectEureka.

Saved flows → Now / direction → Company capability → Stories →

Start an enterprise conversation

Share your work email. We will follow up on deploy target (AWS, GCP, or on-prem K8s), AI and simulation use cases, storage needs, and pilot scope.

We typically reply to credible requests within a couple of business days. Please use a work or institutional email.

Technical details Research & education path ← Omnibond outcomes