Quantum simulations
Analytics tell you what already happened. Quantum runs your product forward: it drives a population of synthetic users — calibrated against the behaviour your own prediction model learned — through your real site, app, or creative, and reports what they did and what they made of it.
The workflow it exists for:
- Build a cohort — a synthetic population shaped like your real users.
- Point it at a target — your live product, a staging build, or an ad.
- Read the result per cohort, not just in aggregate.
The aggregate is where most pre-launch signal goes to die. A change that moves the average by 1% is frequently a change that helped one group and hurt another. Every Quantum figure breaks down by the groups you defined, for exactly that reason.
Two tracksDirect link to Two tracks
A cohort tests one of two things, chosen when you create it.
| Track | You are testing | Target | Agents produce |
|---|---|---|---|
| Software | A product — website, web app, or Android build | A URL or an APK | A click trace: pages reached, actions attempted, where they stopped |
| Content | A creative — video, image, or display ad | An uploaded asset in a simulated media environment | Attention and response: did they notice it, recall the brand, understand it, find it intrusive |
They share cohorts, archetypes, audiences and calibration. They differ in what a session is and what a report contains. Start at Software simulations or Content simulations.
The content track is enabled per account. If you don't see it in the app, contact support@moveo.one.
How an agent decidesDirect link to How an agent decides
Each agent's next action blends three signals. None alone would be enough, and the blend is what makes a run reproducible rather than a creative-writing exercise:
- Probability-calibrated transitions. Your prediction model exports the action distribution per probability bucket — what a user at 85% likelihood actually does next, versus one at 12%. Agents draw from those distributions rather than from a generic prior.
- Persona reasoning. Each agent carries an archetype: a described person with behavioural parameters — patience, ad tolerance, attention span, prior familiarity. A language model reasons about what that person does on this screen.
- Model-learned feature importance. The features your model weights most heavily bias what agents attend to, so a change that touches an important feature shows up in the run.
Runs are seeded, so re-running a seed reproduces the same population and the same split.
What Quantum is notDirect link to What Quantum is not
Being clear about this saves a lot of misread reports:
- It is not a substitute for a real A/B test. It is a way to know what to look for before you spend real traffic finding out.
- It is not a load test. Agents are behaviourally realistic, not concurrent at scale.
- It does not invent behaviour it never saw. A cohort is only as good as the model it was drawn from and the data that model was trained on. Every run publishes the provenance of its numbers — see Reading results.
PrerequisitesDirect link to Prerequisites
- Events flowing into Moveo One — see Installation.
- A trained prediction model. Cohorts are drawn from a model's probability buckets, so the model comes first.
- For an Android target, an APK you can upload.
NextDirect link to Next
- Cohorts — building and calibrating the population.
- Software simulations — testing a product.
- Content simulations — testing a creative.
- Reading results — what the report actually says.