Test decisions against stakeholder panels, simulate how audiences react, and map consequences — before they meet reality.
Each mode is a different lens on uncertainty. Pick the one that fits your question, or use Router for automatic mode selection.
Subject your decision to a panel of dynamically generated persona agents who debate, cross-examine, and score strategic branches (Aggressive, Defensive, Lateral). Get a mathematical confidence rating for each path. Use it when you need to stress-test a high-stakes decision against every possible objection.
Spawn up to 30 autonomous agents that form factions, react to market/geopolitical shocks, and drift in beliefs across simulated social platforms. Watch narratives evolve, factions tip, and defections happen in real time. Use it to model how an announcement, policy, or crisis will spread through public discourse.
An MCTS-inspired engine that maps every decision into a branching tree of causally linked outcomes. Each branch is scored by deterministic physics (elasticity, shocks, coupling) and stochastic variance. Use it to find the path with the best expected utility and the worst-case risk — before you move.
Bring your context. Get a simulation report.
Router & Divergence — two independent paths.
LLM analyzes intent and auto-dispatches to the optimal mode (Council, Mesh, or Tree)
Runs all three modes simultaneously on the same question and compares outputs
Cross-mode confidence scoring with disagreement highlighted — divergence is the signal
Actionable intelligence, not just logs.
A/B test every email, memo, or public statement before it goes out. Catch the misinterpretation that could become a crisis.
Simulate launch reactions across customer segments, competitors, and commentators. Identify the most expensive misunderstanding before shipping.
Model how stakeholders, regulators, and affected groups will respond to a policy change. Find the downstream pressure before it hits.
Upload your video or campaign draft and simulate how different audience segments interpret it. Optimize for clarity, sentiment, and virality.
Treat any scenario as a testable hypothesis. Run counterfactuals, inspect belief drift, and ground your forecasts in multi-agent deliberation.
Stress-test business model assumptions, pitch reactions, and market entry strategies. See which narratives survive round-by-round pressure.
A/B test every email, memo, or public statement before it goes out. Catch the misinterpretation that could become a crisis.
Simulate launch reactions across customer segments, competitors, and commentators. Identify the most expensive misunderstanding before shipping.
Model how stakeholders, regulators, and affected groups will respond to a policy change. Find the downstream pressure before it hits.
Upload your video or campaign draft and simulate how different audience segments interpret it. Optimize for clarity, sentiment, and virality.
Treat any scenario as a testable hypothesis. Run counterfactuals, inspect belief drift, and ground your forecasts in multi-agent deliberation.
Stress-test business model assumptions, pitch reactions, and market entry strategies. See which narratives survive round-by-round pressure.
Council (deliberation), Mesh (social dynamics), Tree (causal search) — each reveals different failure modes. Router auto-selects the right one. Divergence runs all three and surfaces where they disagree. Together they expose what a single answer hides.
Most AI collapses ambiguity into one confident answer. Simulith preserves and quantifies disagreement. If Council says "60% Defensive wins" but Mesh shows a faction cascade against it, that divergence is the intelligence — not a bug.
All context extraction, embedding, and simulation logic runs locally or on your infrastructure. No data leaves your environment unless you choose cloud inference.
Every simulation outcome is ingested back into your knowledge graph. Past forecasts become context for future queries. You build a living library of tested assumptions over time — not throwaway answers.
Go beyond text. Upload video, audio, or images — the engine extracts transcripts, visual context, and metadata to simulate how audiences interpret your actual content, not just your summary of it.
Post-simulation, you can chat directly with any agent to cross-examine its reasoning. "Why did you defect in Round 2?" — and get a structured answer with belief deltas. No black boxes.