THE ENGINEERING DATA PLATFORM

Build your engineering
data foundation.

Nexus connects data across your tools and workflows, preserving the context that makes it valuable. Turn raw results into trusted datasets for reuse, analytics and AI.

Explore Nexus with us

See how it works

Preserve engineering knowledge. Turn it into new capabilities.

CONNECT

QUALIFY

REUSE

KEEP CONTROL

01 / FROM RESULTS TO ASSETS

Keep the knowledge.
Move beyond the project.

Parameters, meshes, assumptions and versions give a simulation result its meaning. Nexus keeps that context with the data, so the next team can find, understand and use it.

YOUR SIMULATION WORKFLOW

Before Nexus

With Nexus

WITH NEXUS

Ready for the next use.

Find data across projects

understand its context and quality

Reuse standardized datasets

Trace versions and transformations

From project files to structured, governed and reusable engineering assets.

YOUR SIMULATION WORKFLOW

BEFORE NEXUS

Knowledge stays in project silos.

Search manually through project files

Compare heterogeneous outputs

Recheck quality and consistency

Repeat preparation for each new use

WITH NEXUS

Ready for the next use.

Find data across projects

understand its context and quality

Reuse standardized datasets

Trace versions and transformations

02 / THE NEXUS FOUNDATION

Connect. Organize. Qualify. Serve.

A data layer that works with the engineering environments your teams already use.

Connect engineering sources

Bring simulation outputs, compute environments, test data and existing stores into a connected data workflow.

Simulation · HPC · Test · Data stores

Connect engineering sources

Bring simulation outputs, compute environments, test data and existing stores into a connected data workflow.

Simulation · HPC · Test · Data stores

Organize and qualify data

Structure collections, retain metadata and apply validation logic. Turn raw results into curated, reproducible datasets.

Context · Validation · Transformation

Organize and qualify data

Structure collections, retain metadata and apply validation logic. Turn raw results into curated, reproducible datasets.

Context · Validation · Transformation

Serve data to applications

Access qualified data through Python and APIs for exploratory analysis, model training and engineering applications.

Python · APIs · Data streaming

Serve data to applications

Access qualified data through Python and APIs for exploratory analysis, model training and engineering applications.

Python · APIs · Data streaming

Deploy on your terms

Choose on-premises, cloud or hybrid deployment to fit your infrastructure and keep control of engineering knowledge.

On-premises · Cloud · Hybrid

Deploy on your terms

Choose on-premises, cloud or hybrid deployment to fit your infrastructure and keep control of engineering knowledge.

On-premises · Cloud · Hybrid

03 / BUILT FOR ENGINEERING WORKFLOWS

From raw outputs to reusable datasets.

Design pipelines in your preferred Python environment. Make preparation repeatable, preserve lineage and deliver data where it is needed.

Connect & ingest

Structure & validate

Transform & explore

Serve & reuse

01 / CONNECT & INGEST

Bring your sources into the workflow.

Connect external sources and ingest simulation outputs into collections. Keep the solver and project context available alongside the data.

Connect existing data sources

Create structured collections

Capture parameters and metadata

01 / CONNECT & INGEST

Bring your sources into the workflow.

Connect external sources and ingest simulation outputs into collections. Keep the solver and project context available alongside the data.

Connect existing data sources

Create structured collections

Capture parameters and metadata

02 STRUCTURE & VALIDATE

Give every result a usable context.

Organize items in collections and define validation logic to check consistency. Keep metadata and lineage with the data engineers need to trust.

Organize projects, collections and items

Apply custom validation rules

Preserve data origin and context

03 TRANSFORM & EXPLORE

Prepare once. Reuse the pipeline.

Write transformation logic in Python, deploy repeatable pipelines and explore the resulting datasets. Use scientific visualization and analysis to understand what the data contains.

Create reproducible, versioned datasets

Build pipelines as code

Explore large datasets and rendered views

04 SERVE & REUSE

Put trusted data within reach.

Access prepared datasets through Python and APIs. Stream data into exploratory analysis and downstream machine learning or engineering application workflows.

Access data in your preferred environment

Stream data for analysis and training

Reuse qualified datasets across workflows

Connect & ingest

Structure & validate

Transform & explore

Serve & reuse

01 / CONNECT & INGEST

Bring your sources into the workflow.

Connect external sources and ingest simulation outputs into collections. Keep the solver and project context available alongside the data.

Connect existing data sources

Create structured collections

Capture parameters and metadata

Projects → Collections → Items

Organize data with a structure engineers can navigate.

Pipelines as code

Build preparation logic in Python and deploy it at scale.

Lineage & observability

Understand where data comes from and how it changes.

04 / A FOUNDATION FOR WHAT COMES NEXT

One data foundation. More ways to build on it.

Nexus prepares and serves the data. Your analytics tools, AI frameworks and engineering applications put it to work.

Exploratory data analysis

Inspect large datasets, compare results and understand data quality before taking the next step.

AI & Physics-ML

Supply reproducible, contextualized datasets to the model training workflows you choose.

Digital twins & optimization

Make engineering data accessible to applications that explore configurations and support design decisions.

Engineering AI agents

Give specialized agents access to engineering knowledge, data and validated results through your applications.

05 / SOVEREIGNTY BY DESIGN

Your tools will change. Your knowledge should endure.

Keep ownership of the data and know-how your teams create. Nexus supports an open data foundation that can evolve with your infrastructure.

Discuss your environment

YOUR INFRASTRUCTURE. YOUR CHOICE.

On-premises

Within your own infrastructure.

Cloud

For managed data workflows.

Hybrid

Across your engineering environments.

OPEN INTEGRATION · DATA OWNERSHIP

SHARED DATA. DEFINED RESPONSIBILITIES.

Data Owner

Defines and validates.

Data Steward

Checks and qualifies.

Data Engineer

Prepares and transforms.

Data User

Accesses and reuses.

06 / YOUR QUESTIONS

Where Nexus fits.

Start with your data. Keep the workflows you trust.

Nexus

Does Nexus replace our simulation tools?

No. Nexus connects data from existing solvers, compute environments and stores. It provides the data foundation around the tools your engineering teams already use.

Nexus

What makes the data reusable?

Nexus combines structured collections, contextual metadata, validation and repeatable transformation pipelines. Traceability helps teams understand the origin and preparation of each dataset.

Nexus

Can our team work in Python?

Yes. Python access and pipelines as code let engineers connect sources, prepare datasets and use data in their preferred development and analysis environments.

Nexus

Does Nexus train and deploy AI models?

Nexus focuses on data ingestion, preparation and access. Model training, model registries, inference services and custom AI applications sit downstream of that foundation, including within Miura’s broader ML services.

Nexus

How can we evaluate Nexus for our workflow?

Discuss your sources, data preparation needs and infrastructure with the Miura team. The Nexus Pioneers program offers collaboration on real workflows, with demos, prototypes and co-design sessions ahead of broader release.

BUILD WITH YOUR DATA

Preserve engineering knowledge. Turn it into new capabilities.

Let’s start with your sources, your workflows and what you want to build next.

Talk to the Nexus team

Explore the Pioneers program

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Build your engineering data foundation.

Build your engineering data foundation.

Keep the knowledge. Move beyond the project.

Keep the knowledge. Move beyond the project.

Ready for the next use.

Knowledge stays in project silos.

Ready for the next use.

Connect. Organize. Qualify. Serve.

Connect. Organize. Qualify. Serve.

Connect engineering sources

Connect engineering sources

Organize and qualify data

Organize and qualify data

Serve data to applications

Serve data to applications

Deploy on your terms

Deploy on your terms

From raw outputs to reusable datasets.

From raw outputs to reusable datasets.

Bring your sources into the workflow.

Bring your sources into the workflow.

Give every result a usable context.

Prepare once. Reuse the pipeline.

Put trusted data within reach.

Bring your sources into the workflow.

One data foundation. More ways to build on it.

One data foundation. More ways to build on it.

Exploratory data analysis

AI & Physics-ML

Digital twins & optimization

Engineering AI agents

Your tools will change. Your knowledge should endure.

Your tools will change. Your knowledge should endure.

On-premises

Cloud

Hybrid

Data Owner

Data Steward

Data Engineer

Data User

Where Nexus fits.

Where Nexus fits.

Does Nexus replace our simulation tools?

What makes the data reusable?

Can our team work in Python?

Does Nexus train and deploy AI models?

How can we evaluate Nexus for our workflow?

Preserve engineering knowledge. Turn it into new capabilities.

Build your engineering
data foundation.

Build your engineering
data foundation.

Keep the knowledge.
Move beyond the project.

Keep the knowledge.
Move beyond the project.