# Generative Engineering

> Generative Engineering is an AI engineering company. Its platform automates engineering simulation: AI agents and expert engineers build parametrised pipelines spanning geometry pre-processing, simulation, and post-processing, run experiments across hundreds of design variants, and iterate in the design space without rebuilding models each time. Tagline: "Your transformation into AI engineering."

This is the main Generative Engineering website at generative.vision; the engineering blog and Substack listed under Optional are separate properties.

The platform integrates with the numerical solvers and design tools engineers already use, keeps high-fidelity simulation in the loop, and versions every function, simulation, and pipeline for full traceability. The pages below cover how it works, with engineering outcomes across robotics, architecture, product design, and bioprocessing.

## Key facts

- Automates engineering simulation workflows, pairing AI agents with expert engineers.
- Integrates with existing numerical solvers and design tools, including SimScale and Grasshopper.
- Keeps high-fidelity simulation in the loop and versions every function, simulation, and pipeline for full traceability.
- Core workflow: Define, Experiment, Iterate.
- Engineering outcomes span robotics, architecture, product design, and bioprocessing.
- Led by founders who have previously built internet-scale systems serving millions of users.

## Key concepts

- Design space: the range of possible designs defined by a model's parameters; teams iterate within it rather than on a single fixed design.
- Parametric pipeline: an automated, parametrised sequence spanning geometry pre-processing, simulation, and post-processing, run across many design variants.
- Define, Experiment, Iterate: the platform workflow — set up the pipeline, generate and score variants against performance criteria, then refine.
- High-fidelity simulation in the loop: using full simulation as the scoring signal while exploring designs, rather than lower-fidelity approximations.

## Core pages

- [Home](https://generative.vision/): Overview of Generative Engineering — a platform that automates engineering simulation and accelerates design exploration, pairing AI agents with expert engineers.
- [How It Works](https://generative.vision/how-it-works): An explanation of the Define, Experiment, Iterate workflow — describing how the platform sets up automated simulation pipelines, generates and scores hundreds of design variants against performance criteria, and iterates directly in the design space, integrating with the numerical solvers engineers already trust.
- [Case Studies](https://generative.vision/case-studies): Real-world generative engineering projects across robotics, architecture, product design, and bioprocessing.
- [Careers](https://generative.vision/careers): How to join an ambitious early-stage AI engineering team, led by founders who have previously built internet-scale systems serving millions of users.
- [Contact](https://generative.vision/contact): Get in touch with Generative Engineering via the contact form.

## Case studies

- [Robotics](https://generative.vision/case-studies/robotics): Quadrupedal robot optimised with a parametric kinematic model, reinforcement-learning locomotion, and first-principles energy simulation — feasible designs increased from 8% to 66% by increasing calf-motor capacity.
- [Architecture](https://generative.vision/case-studies/architecture): Public sculpture for the Unbuilt 2025 competition, using parametric modelling and UTCI thermal-comfort modelling to maximise shaded comfort and minimise solar exposure.
- [Pedal](https://generative.vision/case-studies/pedal): Parametric bike-pedal design in Grasshopper with automated SimScale structural testing — hundreds of variants scored across injection-moulded plastic and CNC-aluminium designs.
- [Bioreactor](https://generative.vision/case-studies/bioreactor): CFD impeller design for a bench-top stirred-tank bioreactor — 150+ configurations simulated, holding peak shear below 0.5 Pa and avoiding dead zones.

## Optional

- [Blog](https://blog.generative.vision/): Articles on applying AI to engineering and design automation.
- [LinkedIn](https://www.linkedin.com/company/generativeengineering): Company news and updates.
- [Substack](https://substack.com/@generativeengineering): Long-form writing on AI engineering from the team.