Every serious engineering discipline simulates before it builds. Aerospace flies the aircraft in software before a rivet is placed. Drug discovery now tests molecules against virtual cells before the wet lab. Go-to-market still tests in production, one motion at a time, a quarter per lesson, with real budget as the lab fee. The simulator closes that gap.
One living picture of your go-to-market that interventions can be tested against. It gets built from three layers.
Read from the sources the OS is already connected to. Your CRM, your ad accounts, your product analytics, your calls, your billing, your search data, plus the motions you have already run and how they went. In an install the state loads itself, you never type in what the OS already knows.
145 plays with impact and ease scored from real builds across B2B and B2C companies, motion by motion, maturity by maturity. This is the collective wisdom of the practitioners who deploy the OS, encoded so a simulation can use it.
Every deployment that runs a play reports the outcome back. The simulator re-ranks on your numbers as they arrive, so the longer the OS runs, the more the recommendations are yours instead of the community's average.
Take the current state. Branch it into scenario arms, each arm a different move or sequence of moves. Play each arm forward against the pattern library. Read out what comes back, ranked. Then remember, because the next simulation starts from everything the last one learned. The state persists, the way a real quarter does.
Because every arm starts from the same state, the readouts stay consistent with each other. Compare an outbound push against a budget shift and you are comparing like with like, the same company, the same constraints, the same hours available.
The move borrowed from experimental science. Clone the scenario, change one input, run both arms. The diff shows exactly what your change moved. No year of live testing to learn that lowering CAC reshuffles your first sprint.
Forward, you pick a move and the simulator plays out where it ranks against everything else you could do with the same hours. Backward, you name the target instead. More pipeline, lower CAC, better retention. The simulator works back from the target to the plays that reach it for teams shaped like yours, sequenced into a 90-day plan with quick wins funding the big bets.
Backward mode is the one teams use most. Teams wake up wanting the number to move.
The ranking runs on encoded practitioner heuristics, the pattern library above. It is a simulator in the way a flight simulator is one, built from the recorded experience of people who have flown the routes. It is honest about what it is. It does not predict your revenue. It does not claim a trained model of your market. Every recommendation prints its assumptions. When a number appears it carries its provenance.
What sharpens it is deployments. Each install that reports outcomes back makes the next simulation smarter, the way recorded flights made flight simulators better.
The simulator is live inside the GTM OS demo. Build a plan, clone the scenario, change one input and watch the arms diverge.
Run the simulator in the live demo →