World0™ gives AI agents synthetic experience.

A simulation platform that stays connected as agents plan, act and adapt.

Synthetic environments give agents a place to rehearse consequential business tasks, experience changing conditions and bring useful lessons into real work.

Experience matters when the task touches real resources.

A purchasing plan affects cash and inventory. A pricing plan affects demand, capacity and profit. Each choice changes the conditions for the next.

World0's connected platform gives agents synthetic experience through task rehearsal. Supported agents practice across different conditions, identify weak assumptions and carry useful lessons into real work.

The connection continues as the task unfolds, bringing new context into simulation and useful experience back to the agent.

See how it works

Understand a pricing system before the next change.

World0 runs AI pricing agents and revenue management pricing systems in synthetic markets with responsive competitors. A scoped comparison shows how the system or configuration behaves and what happens to prices, profit and other agreed outcomes.

The engagement includes the findings and the records a technical reviewer needs to recompute them.

Explore pricing measurement

The delivery includes the record behind the findings.

01

Findings report

The findings report describes the comparison, results and scope.

02

Measurement design

The design identifies the exact conditions and measures used.

03

Complete run record

The complete run record contains the measured decisions and outcomes.

04

Analysis and checker

The analysis code and checker allow technical reviewers to recompute the reported values.

Explore pricing measurement

The model, its settings and the market shape the outcome.

World0's pricing research shows how different AI models, configurations and market interactions produce different business outcomes. In the tested AI configurations, smaller models earned higher closing profits than the frontier model. Changing reasoning and answer settings also changed the result.

The research examines behavior across successive decisions in synthetic markets.

1.5 2 3 4 6 8 10 price where the market settled, model units, logarithmic above joint-profit profit vs rule competitive 1.64 joint-profit 2.40 Frontier model claude-fable-5 15 markets claude-fable-5, median 6.2337, 15 of 15 above the joint-profit level median 6.23 15 of 15 0.01× exploratory probe Mid model, more room to answer claude-sonnet-5 answer allowance 512, 10 markets claude-sonnet-5, median 6.1290, 10 of 10 above the joint-profit level median 6.13 10 of 10 0.01× exploratory probe Mid model claude-sonnet-5 standard settings, 100 markets claude-sonnet-5, median 6.0000, 100 of 100 above the joint-profit level median 6.00 100 of 100 0.03× confirmatory arm Mid model gpt-5.6-terra answers directly, 15 markets gpt-5.6-terra, median 5.0000, 13 of 15 above the joint-profit level median 5.00 13 of 15 0.19× preregistered arm Mid model, allowed to think first gpt-5.6-terra deliberation on, 6 markets gpt-5.6-terra, median 2.2108, 2 of 6 above the joint-profit level median 2.21 2 of 6 1.18× exploratory probe Small model gpt-5.6-luna standard settings, 100 markets gpt-5.6-luna, median 2.1513, 35 of 100 above the joint-profit level median 2.15 35 of 100 1.08× confirmatory arm Small model claude-haiku-4-5-20251001 standard settings, 10 markets claude-haiku-4-5-20251001, median 2.0875, 1 of 10 above the joint-profit level median 2.09 1 of 10 1.12× preregistered arm Hand written rule written pricing rule published in full, 30 markets written pricing rule, median 1.6433, 0 of 30 above the joint-profit level median 1.64 0 of 30 1.00× the control preregistered, median exploratory probe, median one market Profit is the arm’s evaluation-window profit per agent divided by the written rule’s, which prices at the competitive level.
Eight pricing configurations, one synthetic market, four agents each, every market drawn where it settled. Recomputed from the run logs at build time and checked against the published readouts. The two 100-market rows are the confirmatory arms of the agent study and the small-model study; the rows marked exploratory are probes of 6 to 15 markets and carry no preregistered claim. The six-market probe changed both reasoning effort and answer allowance; it measures their combined configuration.

Explore the research

Bring a consequential task into the conversation.

Synthetic experience
for AI agents.

Discuss the work an agent needs to perform, or a pricing deployment or configuration the team needs to understand.

Talk to World0