Four engines.
One causal graph.
Manifold is a causal-intelligence terminal built on four specialized engines running in parallel on a shared scored graph.
Recovers the causal topology of a critical system from observational data — which nodes act on which, and through what edges.
- Constraint-based + score-based search over conditional independencies
- Linear PCMCI variant for time-indexed feeds
- Side-by-side panel comparison across discovery algorithms
Verifies geopolitical and structural claims about the graph — sanctions exposure, jurisdictional dependencies, control relationships.
- Formal logic over node and edge attributes
- Audit trail for every claim with traceable evidence
- Surfaces silent failure modes before they become incidents
Asks what-if questions on the causal graph — substitute a node, sever an edge, model an embargo — and computes the counterfactual world.
- Substitution counterfactuals for irreplaceability scoring
- Counterfactual recovery time estimation
- Intervention design for stress-tests
Simulates cascade dynamics across the graph and characterizes tail risk beyond traditional VaR.
- Monte Carlo cascade simulation with configurable shock distributions
- Tail-depth statistics — fat-tail severity beyond VaR
- ΩSF and ΩSX system-level fragility outputs
Criticality looks different in finance than it does in fertilizer supply chains. Engines are pluggable per domain — swap the criticality core, retain the rest of the system.
See the engines on real data.
Trial accounts come pre-loaded with curated datasets across manufacturing, energy, and finance.
