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From equation to production workflow — in one place

engicloud.ai gives you a library of +20,000 validated, Python-based engineering calculators spanning 20 disciplines and 110 subfields — each sourced from established engineering literature, implemented as tested Python functions, and ready to chain into executable modeling workflows on a visual canvas.

Four building blocks

engicloud.ai has a simple object hierarchy. Understanding these four building blocks tells you everything about how the platform works.

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Equation

A mathematical relationship from published literature — e.g. Bernoulli's equation, Fourier's law.

⚙

Calculator

Calculator: A Python function implementing one or more equations, or a call to an API — typed inputs/outputs, units, docs, unit tests. The atomic building block.

⬡

Workflow (Project)

A visual pipeline of connected calculators on the node canvas. Outputs of one feed inputs of the next. You can also feed back outputs as inputs and iterate towards a steady state.

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Team Library

A shared, access-controlled collection of calculators and workflows scoped to your organization.

Equation → Calculator → Workflow → Shared Library
1
Build Workflows

Connect calculators into modeling pipelines

Drag calculators onto the visual canvas and wire outputs to inputs. Build multi-step workflows that chain thermodynamic tables, transport models, and custom equations into a single executable pipeline.

Manage modelling complexity - from equation to product/process

Smoothly scale system oversight from basic math to deployment.

Build complex modeling workflows based on re-usable calculators

Architect sophisticated analytic systems using simple, interchangeable components.

Use expert-created python models (calculators)

Leverage pre-built, vetted analytical frameworks designed by professionals.

Choose from 20,000 synthetic models (calculators)

Tap into a massive library of artificially generated functions.

Encapsulate python code

Safely wrap scripts into secure, deployable computational assets.

Solve coupled differential equations

Concurrently analyze highly interconnected variables and multiphysics behaviors.

Create a re-usable python-based model (calculator)

Design modular mathematical logic for repeated engineering analysis.

Combine different calculators into a modeling workflow (project)

Visually link singular tools into automated analytical pipelines.

Solve differential equations

Seamlessly evaluate dynamic physical states and mathematical changes.

2
Enhance with AI

Let AI accelerate — not replace — your engineering

Use LLMs to generate new calculator code from natural-language descriptions, or apply ML methods for data-driven predictions. Every AI-generated model goes through the same validation pipeline as hand-written code.

Use ML methods for predictions

Deploy trained ML models for use by others.

Make live API calls to LLMs

Quickly query cutting-edge language engines during runtime execution.

Use an AI assistant to generate physics-based modelling workflows

Direct intelligent agents to automatically map complete simulation pipelines.

Use LLMs to generate new python-based models (calculators)

Prompt artificial intelligence to instantly draft custom algorithmic logic.

3
Connect & Integrate

Bring in your data, tools, and knowledge

Connect to external databases, spreadsheets, and internal knowledge bases. Your workflows aren't isolated — they pull live data from the systems your team already uses.

Model a whole real complex process chain

Model a whole power plans

Build modeling workflows trained on 3D simulation data

Train rapid computational pipelines using rich spatial analysis outputs.

Interact with databases from different sources

Seamlessly query and extract information across multiple external systems.

Integrate and interact with spreadsheet / excel models

Dynamically link and sync traditional tabular data and formulas.

Integrate existing internal knowledge

Centralize your proprietary company insights directly into the platform.

Store material parameters into a database for re-use

Securely archive physical properties for instant future retrieval.

Port your existing python code into re-usable calculators

Transform legacy scripts into modular, ready-to-run analytical tools.

4
Collaborate

Share models your team can actually use

Publish calculators and workflows so colleagues can run them on the visual canvas — even without writing Python. Version everything, control access, and build a shared engineering knowledge base.

Create python modeling for someone else to use on the graphcial canvas

Program backend logic for non-coders to easily arrange visually.

Build projects for collaborations across teams

Unite diverse organizational departments in joint computational efforts.

Build your own private set of calculators and projects to share

Curate secure, proprietary analytic toolkits to safely distribute internally.

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What are “synthetic models”? Each calculator is a Python implementation of a known, published equation — carefully LLM-generated from curated literature. Check here for details on how we did that.