An AI decision simulation system.
Simulate possible futures. Adapt as reality unfolds.
Explore paths to possible outcomes, or reason forward from current facts. Bring stakeholders, constraints and changing events into one decision landscape.
Where could this lead?
End-state first
Start with distinct possible outcomes. Explore how stakeholder actions, interests and constraints shape the paths to each one. For decisions involving competing actors and different ways forward.
Regulated enterprise AI agent launch
A nine-month launch shaped by a regulator, two enterprise clients, security, legal and risk, procurement, and a model provider.
Actual workflow output from a fictional scenario. Probabilities are scenario estimates, not observed frequencies.
Another application640 buyers face overheating batteries. Consumer advocates, a marketplace, payment provider, importer, manufacturer, insurer, and safety regulator must decide who acts, pays, and carries the risk.
What changes when the facts change?
Linear reasoning
Generate next-step candidates from current facts. Confirm what happened, exclude incompatible branches, and continue from the new reality. For evolving events without a fixed endpoint.
NVIDIA Blackwell availability
An eight-month replay of how production delays, yield fixes, early cloud deployments, and full production changed the outlook.
Replay through 2025-01-08
Five evidence-backed events and four excluded candidates. A frozen historical replay, not live monitoring or a prediction accuracy claim.
Another applicationA sixteen-month replay from provisional duties to legal challenges, market adaptation, and localized European production.
Frequently Asked Questions
How do the two approaches differ?â–¾
End-state-first reasoning explores possible outcomes and paths to them. Linear reasoning generates candidates from current reality, then continues after events are confirmed.
Are these customer case studies?â–¾
The two end-state-first cases use fictional scenarios with actual workflow output. The two linear cases replay public events with reviewed nodes and sources. They are not customer endorsements or evidence of prediction accuracy.
What can I try in the demo?â–¾
The public demo offers an in-memory, end-state-first simulation and a read-only graph. It does not include persistent workspace projects, linear reasoning or historical replay.
How does Aivenir handle my data?â–¾
Aivenir processes Demo content in memory through its server-side services and OpenRouter, without writing it to the Core project or graph database. Separate technical identifiers and usage counters are stored for rate limiting and abuse prevention. Full details are in our Privacy Policy.