Assembly models market reaction as a probabilistic distribution shaped by evidence, interaction, persuasion, resistance, and audience structure.
The system is designed not to eliminate uncertainty, but to measure and expose it before launch.
A single AI response is not a market. Real adoption emerges from interaction between different audiences, incentives, objections, and evidence over time.
Assembly models this as a structured simulation rather than a static prompt response.
Instead of asking “What does AI think?”, Assembly asks “How does a population react, stabilize, resist, shift, or converge under evidence and social pressure?”
Assembly does not treat market reaction as a single generated answer. It treats reaction as an evolving distribution shaped by evidence, interaction, and uncertainty across a synthetic population.
Each Assembly run is a structured sequence — not a single forward pass. The output is the final round plus the measurements taken along the way.
Personas are instantiated with persistent priors — demographic spread, risk tolerance, competitor loyalty, proof threshold.
Cohorts are defined relative to the brief. Best-fit and hardest-to-convince segments are identified before any reaction is sampled.
Live retrieval surfaces source material — discussions, reviews, competitor framing — and personas update against it.
Initial stance, confidence, and objection vectors are logged before any peer interaction.
Personas exchange arguments. Persuasion and resistance dynamics run; positions can shift.
Per-round objection weights are tracked. Some resolve; some persist; new ones can emerge under peer pressure.
Variance across the last two rounds is tested against stability thresholds. Unstable runs are flagged for rerun.
The same brief is rerun on independent seeds. Cross-run agreement and final distribution become the retained output.
Assembly evaluates simulations using stability, convergence, framing sensitivity, and objection persistence metrics. Each one is reported alongside its operational interpretation — what high or low values mean for the trustworthiness of the run.
Composite reliability score combining calibration error, rerun stability, and framing sensitivity.
Measures whether repeated simulations produce similar distributions.
Measures how much the output distribution changes when the same brief is reframed.
Tracks which objections persist through later rounds even after evidence exposure and peer interaction.
A concrete view of the process above. The same brief, four rounds, traced from cold start to final convergence.
The run stabilized by round two, showed low framing sensitivity, and retained one unresolved switching objection requiring real-world validation. The retained output is the round-three distribution, not a single verdict.
Two simulations can end at identical distributions while representing completely different underlying dynamics. These are the within-run checks that separate genuine convergence from instability or prompt artifacts.
The same brief is rerun across independent seeds.
The brief is paraphrased multiple ways and the resulting distributions are compared.
Runs are inspected for consensus that forms too quickly or dissent that disappears without absorbing counter-evidence.
Low rerun stability, unresolved objections, or high framing sensitivity surface explicit reliability flags on the report.
A run can complete and still be invalid. These are the conditions under which the result is not delivered as a usable signal.
Post-launch information contaminates the evidence snapshot and biases the simulation toward known outcomes.
The evidence environment no longer reflects the market moment the simulation is intended to model.
Evidence coverage is too thin, too skewed, or too concentrated in a single channel.
Small framing changes produce materially different distributions.
Independent reruns fail to converge toward a stable distribution.
Assembly is designed to expose uncertainty, not hide it.
Internal checks separate stable convergence from artifact within a single run. These are the operations that separate the system itself from reality — backtests, holdouts, and category-level calibration that compound over time.
Past simulations are rescored against shipped product outcomes — sign-ups, retention, public sentiment, switching behavior. Drift per category is computed quarterly.
A subset of evidence is withheld during the run, then compared against the model's reaction once exposed. Reveals how much weight any single piece of evidence is carrying.
Simulations are run before launch; real outcomes are tracked after. Where outcomes are observable, runs are scored against them and the result feeds category calibration.
Independent reruns of the same brief surface seed-dependence. Mean cross-run distance becomes the Rerun Stability score that gates retention.
Reliability weights (α, β, γ in MFS) evolve independently per product category and audience type. Categories with more historical signal carry tighter confidence bands.
Low support counts widen the reported confidence interval rather than narrowing the result. The system surfaces uncertainty rather than smoothing over it.
The system becomes more calibrated, not by retraining the model, but by accumulating retained runs, observed outcomes, and category-level weight drift. Reliability is an output the customer sees, not an internal claim.
Submit a brief. We'll run the simulation and deliver a Market Reaction Report — built around your product, your audience, your launch question.