Think it through.
You bring the question. Simmis builds a living model of your domain — your team, your data, your constraints — tests scenarios, and shows you what matters.
Agents handle the modeling. You make the call.
Ask
Type a question, upload files, or connect your existing tools. Start from scratch or fork a shared model.
Explore
Agents build models, test scenarios, and surface what you couldn't see before.
Decide
Inspect the reasoning. Compare outcomes. Act with confidence.
Try it before you commit
A decision is a fork: spin up a version, compare outcomes, keep what works — or discard it for free. The same move, whether you're planning your week or a policy.
Your week, your projects
A fork is "let me try planning the week differently." A merge is "this is now my plan." Branch a plan, compare versions, keep the one that holds — in one place that remembers every step you took to get there.
Shared decisions
A fork is "what if A moves to B?" The diff is what you review on Friday. Everyone works in one versioned world — propose changes as branches, see the consequences before they land, merge what you agree on.
Policies & incentives
A fork is "what if we change the incentive?" Run it over real data and watch the effects. A merge is a policy change made in software before it's made in life — tested, not guessed.
You already simulate. You just do it in your head.
Every decision you make is a mental simulation — predicting futures, weighing trade-offs, modeling constraints. The trouble is that mental simulations are invisible. They can't be shared, inspected, or improved. When they're wrong, there's no record of why. Your organization's reasoning starts from scratch every time.
Simmis makes that reasoning visible. You ask a question — agents structure your knowledge into a model to answer it. That model persists, grows, and stays grounded because it's actively used. Organization is the consequence, not the prerequisite. Knowledge that participates in inference can't silently rot.
The platform compounds like GitHub does for code. Fork a model someone else built, adapt a template, start from shared knowledge. The system observes what actually happens and refines its predictions — the longer you use it, the sharper it gets. Read the full vision →
Notes
Visual essays on simulation, modeling, and the infrastructure behind it.