Six steps, from a folder of documents to an answer you have pushed back on. This is the whole loop, not a summary of it.
Step 01
Upload whatever you already have: decks, research, exports, policy documents, past campaigns. Altiera reads them to understand your world. None of it leaves Europe and none of it trains anything for anyone else.
Step 02
One decision, phrased so it can be wrong. We start from a decision with a real outcome and a date attached, not a research topic. If the question cannot be wrong, it cannot be simulated.
Step 03
Agents are constructed from observed behavioural histories and calibrated to the population you care about. Altiera proposes the setup from what it read, and every value is visible, sourced, and yours to override. Nothing is decided behind the panel.
Step 04
The population meets the scenario and interacts. The run repeats until the distribution stops moving.
Step 05
You get the range, the confidence, and which segments break from the median. This is the point where most tools stop.
Step 06
Ask a follow-up and the population re-runs against it. The agents deliberate again on the new condition and come back sharper. The population persists between questions, so the second answer costs hours rather than a new project.
Context ingested
| pricing_deck_q3.pdf | PARSED |
|---|---|
| churn_export.csv | PARSED |
| community_guidelines.md | PARSED |
| 2025_research_readout.pptx | PARSED |
4 FILES · 1.2M TOKENS · HELD IN EU
Question
If we raise the mid tier from £9 to £12 in Q1, what happens to retention?
SIMULABLE — OUTCOME DEFINED, DATE ATTACHED
Proposed parameters
| Population | 30,000 agentsSOURCE: DEFAULT | EDIT |
|---|---|---|
| Segments | 4SOURCE: churn_export.csv | EDIT |
| Runs | 1,000SOURCE: DEFAULT | EDIT |
| Horizon | 90 daysSOURCE: pricing_deck_q3.pdf | EDIT |
| Confidence floor | 0.70SOURCE: DEFAULT | EDIT |
Running
RUN 001 ADOPTED 000 HELD 000
ONE RUN OF MANY. THREE SEEDS, THEN CONTAGION. ~20% NEVER ADOPT, AND THAT IS THE RESULT.
Retention at £12, 90 days
MEDIAN −4.1PP · SPREAD −9.2PP TO −0.6PP
CONFIDENCE: MODERATE-HIGHFollow-up
RE-DELIBERATING · 400 RUNS
ROUND TRIP ON A FOLLOW-UP: HOURS. THE POPULATION STAYS LIVE BETWEEN QUESTIONS.
The simulation engine. It maintains the behavioural substrate, constructs populations, runs the interactions and calibrates its own confidence. Five capabilities matter to you as a client.
Agents carry behavioural histories rather than a demographic tag. Two people with identical demographics who behaved differently are modelled as different people, because they are.
Agents influence each other during a run. Contagion, pile-ons, silent majorities and tipping points appear because they are simulated, not because they were assumed.
Every result carries a confidence level. A trend-break guard refuses to extrapolate through a level shift, because the most expensive simulation error is a confident straight line drawn through a discontinuity.
Sentiment models trained on generic text misread real communities. Ours arbitrate over model confidence with a hand-built lexicon, and detect code-mixing that off-the-shelf language identification gets wrong.
Every result traces back to the reasoning that produced it. Data is generated and held in Europe, which is what makes it usable for European enterprise and public-sector buyers.
The range of results across repeated runs, not a single number. Median, spread, and the shape of the tail.
A stated confidence for each result, with the reasons it is lower where it is lower.
Which parts of the population diverge from the median, and by how much. This is usually where the decision actually changes.
Which variables moved the outcome and which turned out not to matter. Useful for deciding what to test in the real world.
An auditable account of how the engine reached the result, for internal review or regulatory questions.
The population persists. Alternatives and follow-up questions are hours of work rather than a new project.
A session with the people who built the model, walking your team through what to trust and what to treat carefully.