We spawn agent swarms that model how people behave inside software. Every swarm begins with your real users, so it shows you how your market will react before they do. This is how Sentia grounds a decision when the evidence you need does not exist yet.
Every audience is built from signals you already own: interview transcripts, support tickets, survey responses, and session replays. Each agent carries hundreds of behavioral signals, and thousands of them run at once, so a study that took six weeks and eight people comes back the same afternoon.
Every agent is grounded in behavioral science and aware of the moment your users are living through, from the events shaping what people notice to the beliefs and moods moving underneath them. An audience reflects the present, not a snapshot of whenever the data happened to be gathered.
Populations are drawn against census and population data, market by market, so a panel is representative of somewhere real rather than of the internet in general.
Anything can produce a confident answer now. The hard part, and the only part worth paying for, is knowing when to believe one. A simulator is not a survey with better manners; it is a causal model of your users, and a causal model has to be held to what actually happens.
So nothing here is a one-off study you commission and file. It is an instrument that stays, answers the next question and the one after that, and gets measurably better every time you ship. The value does not depreciate the moment the deck is delivered.
A simulation you cannot check is an opinion with a number on it.
Fig. 1Diverse and representative populations
An audience is named the way your team already names one, and built from the evidence you already hold about it.
Four questions, and the fourth is the one that makes the other three worth anything.
01
Put the swarm in front of your product as it stands and watch where attention goes, where effort is abandoned, and which kinds of user never get where you assumed they would. This is the baseline, and most teams have never actually seen theirs.
02
Ask what happens if you raise the price, strip a step out of onboarding, or change a default that most people never touch, and the swarm works through the new version the way your users would, returning who moves, who leaves, and what stopped the ones who walked away. It is an intervention you get to run before you intervene.
03
Point it at a decision you already made and take the other branch. Counterfactuals are how a team learns from its own history instead of relitigating it, and they are impossible with people you can only interview once.
04
Every run is a dated, checkable claim. When the change ships, the outcome comes back and scores the run that predicted it. Accuracy is earned on your product in your market and it improves with each release, which is the difference between a vendor telling you they are accurate and you being able to check.
Three movements, repeated for as long as you keep shipping.
01
Bring your product and the evidence you already hold about the people in it. The model learns every surface you own and everyone who touches it, and the swarm inherits that instead of guessing at it.
02
Put a real decision in front of it. Thousands of grounded agents work the situation through and return a distribution with its reasoning attached, not a verdict you have to take on faith.
03
Ship, and the outcome comes back to the model. Every release is one more observation, so the thing is measurably better on your next question than it was on this one, and it stays that way.
We envision a world where a company can see the consequence of a decision before committing to it, and owns the intelligence that showed them. Not research rented by the study, but foresight the company keeps.
The result is a different way of deciding, with more conviction and less hope.
—Where this goes next
Simulate consequential outcomes before they happen.