Every example here is a finished study: a decision an engineer owns, the method that settles it, and a part that ships with it. Run one as it stands and you have a real result in minutes.
Then order it your way. Say what is different about your part, your material or what you are optimising, and your agent adapts the recipe. The method and the guardrails stay put.
Python 3.10 · Windows / macOS / Linux · the first recipe needs no account at all
Each example names the decision it answers, what it costs to run, and where to point it at your own part.
Open a finished result on the real part - surface or draggable solid cut, time slider, field toggles - and pull the numbers behind it as plain arrays. A reference result and its geometry ship with the example, so it runs before you have an account.
The whole loop in one script: mesh locally, upload the geometry, choose a material, run AI filling, read the peak cavity pressure. Swap in your STL and it is your part. The result comes back viewer-ready.
Gate count, then placement, then process, then validation. Every candidate scored for filling pressure, fill evenness and weld lines - and the reason behind each cut written to the project's decision log as it is made.
Which parameters matter, where to centre them, how wide the window may be. One config file drives a wide sweep over three variables, screening, adaptive refinement at the feasibility boundary and a set of numerical confirmation decks - then emits the spec and the audit trail behind it.
Bring it to a community session and walk us through it. The people in the room are working on the same problems, and what you learned the hard way saves someone else a week.
If you have never worked with agentic tooling, you are not alone. Most of our customers had not either when they started with the toolkit. We wrote down everything you need to get going · choosing a harness, a checklist for the first session with the prompts to paste, how to keep the context an agent needs, and what usually goes wrong.
Point an agent at this page and it can choose the right example and fetch that example’s page on its own. The kit carries its context with it: how the platform is organised, which KPIs hold up, the traps that cost real time, and the guardrails worth keeping.
Read toolkit.simcon.ai, then set up the API Toolkit.One subscription covers up to 5 people · monthly, cancel any time.