Test the decision
before you make it

We build simulations of real populations from behaviour we observe rather than answers people report. Ask what a group of people will do, and get a distribution of outcomes with a confidence level attached, in days rather than months.

Book a scoping call See how it works

Data points

50MILLION

Signals a day

730THOUSAND

People observed

1MILLION+

COMMUNITIES MONITORED

32

WHAT THIS IS. A POPULATION MODEL, NOT A PANEL

We build a population of simulated people out of behaviour that real people actually produced, put that population in front of a decision you are considering, and let them react to it and to each other. You get back the range of things that could happen, how likely each one is, and how confident we are. Not one person's opinion, and not an average of opinions. A population, reacting.

BUILT FROM WHAT PEOPLE DID. RUN AGAINST WHAT YOU ARE ABOUT TO DO.

A dense dithered field fading from dark to light

Built from what people did. Run against what you are about to do.

HOW OUR SIMULATION WORKS: They meet the scenario and react to it and to each other

HOW A SURVEY PANEL WORKS: One at a time, in isolation

Simulation Survey panel
Who answers

A population built from what people actually did, carrying behavioural histories rather than demographic labels.

Individuals reporting what they think they would do, under observation, with an incentive to look reasonable.

How they answer

They meet the scenario and react to it and to each other, so contagion, pile-ons and silent majorities emerge during the run.

One at a time, in isolation, and the group effect is averaged in afterwards if it is modelled at all.

What comes back

A distribution of outcomes with a stated confidence level, a segment breakdown, and an account of what moved the result.

An average with a margin of error, and no way to see which parts of the population diverge or why.

Cost to ask again

Hours. The population persists, so a follow-up question re-runs against a model that already exists.

A new fieldwork cycle. Recruit, field, clean, report.

ALSO NOT: A FOCUS GROUP WITH EXTRA STEPS. NOT A POINT FORECAST. NOT A REPLACEMENT FOR JUDGEMENT.

01Pricing & packaging

Who leaves, who upgrades, and who says nothing while quietly ceasing to spend. Where the tolerance line actually sits, by segment.

02Crisis response

Simulate three responses before publishing one. Test whether an apology, a clarification or silence produces the smallest tail risk.

03Retention interventions

Which intervention keeps people, which merely delays their exit, and which accelerates it by drawing attention to the reason they were leaving.

See use cases

THREE DATA LAYERS

Where two layers agree the signal gets stronger, and where they disagree confidence drops

Surveys, interviews and panels all record a person describing a decision after the fact, under observation, with an incentive to look reasonable. That gap between what people report and what they do is not noise you can correct for, and it is the reason the model has to start somewhere else.

Ours starts inside live communities, where our own products instrument behaviour as it happens. Every message, purchase, session and quiet departure is counted by something the community was already using, so nobody was recruited and nobody was performing for a researcher. That feed is layered against your own first-party data where it exists, and against public context, and no layer is ever used alone. Where two layers agree the signal gets stronger, and where they disagree confidence drops and the report says why. The model trains only on derived insights from communities that ELO’s products live in.

OUR FULL METHOD

THE PROCESS. FROM WHAT THEY DID TO WHAT THEY WILL DO

Scroll through the three stages. It starts with a record of what thousands of real people actually did inside live communities. It ends with a forecast of how those same people would react to something you are considering doing. The simulation is everything in between. We call the record the shell and the forecast the ghost.

Scroll to run

Stage 01

Observed behaviour, on a grid

Every point is one thing a real person did inside a live community: a message, a purchase, a decision to stop showing up. Nobody was surveyed and nobody knew they were being counted, which is what makes it honest. It is also entirely in the past, and the past is not what you need.

Stage 02

The population starts moving

Each history becomes an agent, and the agents are put in a room together with your scenario. They talk, copy each other, push back and change their minds. The clusters forming here were not designed by us. They are what happens when these specific people react to each other.

Stage 03

Intent, with a confidence level

Run it a thousand times and you get a spread instead of a number. The bright contours are where the population lands most often. The width of the spread is how certain we are, and a wide one is more useful to you than a precise number we cannot stand behind.

Altiera maintains the behavioural substrate, constructs populations, runs the interactions and calibrates its own confidence. Agents carry behavioural histories rather than demographic tags, so two people with identical demographics who behaved differently are modelled as different people. They influence each other during a run, which means contagion, pile-ons, silent majorities and tipping points appear because they were simulated rather than because they were assumed.

INSIDE ALTIERA

SIX STEPS IN ONE LOOP

The population persists between questions, so the second answer costs hours rather than a new project

HOW TO START. ONE QUESTION, THEN A PROGRAMME

You have seen how it runs. This is how it starts.

PRICED PER ENGAGEMENT AGAINST RESEARCH AND DECISION BUDGETS. NDA BEFORE ANYTHING SENSITIVE.

Step 01

Scoping call

Forty-five minutes. We work out whether your question is simulable and tell you plainly if it is not. No charge, no deck.

Step 02

Pilot question

One decision, fully run, with the full deliverable set. Timeline depends on how much grounding your population needs.

Step 03

Standing capability

Your population stays live and is re-run whenever a decision comes up. Cost per question falls sharply after the first.

QUESTIONS. BEFORE YOU ASK

Is this synthetic data?

No. The population is simulated, the substrate underneath it is not. Agents are grounded in behaviour that real people produced, continuously, without being asked to describe it.

How is this different from an AI survey panel?

A panel asks simulated respondents what they would do, one at a time, and averages the answers. We place a population in a scenario and let it interact. The difference shows up precisely where it matters: contagion, tipping points and collective reactions, none of which survive averaging.

Do you need our data?

Not to start. The substrate is ours. If you have first-party data on the population you care about, it makes the result meaningfully sharper, and we will tell you how much.

Where does our data go?

Nowhere it does not need to. Data is generated and held in Europe. Client data is processed under a signed agreement and is never used to train models for anyone else.

How do we know it is right?

You do not take our word for it. Every result carries a stated confidence, and we recommend running your first simulation against a decision whose outcome you already know. That is the cheapest way to calibrate your trust, and it is the test we prefer.

Does this only work for gaming?

The substrate comes from gaming communities, which is where behaviour under pressure is best instrumented. The behaviours it captures, cooperation, defection, status, trust and belonging, are not gaming-specific. Where transfer to your population is uncertain, the confidence level reflects it.