Directed graph querying
Model complex systems as a directed graph: nodes are entities (infrastructure components, organizations, systems), edges are relationships (dependencies). Query the structure to trace propagation paths.
Geopolitical disruption modeling • directed graph querying • scenario planning
Complex Geopolitical Disruption Modeling
Think of complex systems as networks of dependencies: infrastructure connects to infrastructure, constraints propagate, and small failures can cascade. We use directed graph querying to map relationships, combined with vector search for meaning-based retrieval of signals and reports. Together, this supports scenario planning: explore how constraints propagate, estimate cascading risk, and test what happens when something breaks.
Directed graph querying
Scenario planning
Cascade risk modeling
Infrastructure dependency research
Vector Stream Systems builds Aevyra Geospatial for complex geopolitical disruption modeling and scenario analysis. This platform is for research and decision support, not investment advice.
The platform
Aevyra answers: "If this breaks, what moves next?"
Most dashboards show events. Aevyra shows dependency. It uses directed graph querying to connect signals to systems, constraints, and alternates—so you can explain impact, not just observe it.
Directed graph querying: map dependencies as nodes and edges, then query relationships to trace how disruptions propagate.
Scenario planning: test "what if" scenarios—model constraints, estimate cascade risk, and compare alternate routes.
Signal-aware context: pair graph structure with vector search to pull the most relevant reports and signals by meaning.
What we do
Build graph-first infrastructure intelligence (research)
Vector Stream Systems is building Aevyra Geospatial, a tool for complex geopolitical disruption modeling that combines directed graph querying with vector search. The graph maps dependencies—what connects to what, what flows where. Vector search enables meaning-based retrieval of signals and context.
Together, they power scenario planning: model how a constraint at one node propagates through the network, estimate cascading risk, and compare alternate routes. The infrastructure handles graph traversal, signal correlation, and traceability.
Scenario planning
Test "what if" scenarios against the graph. Model a disruption, see what downstream nodes are affected, estimate cascade timing, and compare mitigation options.
Dependency stress tests
Map dependencies as graph relationships. When a constraint appears, trace downstream impact and identify alternate paths.
Signal-aware retrieval
Vector search enables meaning-based retrieval. Pull the most relevant reports, signals, and context for any node or scenario—not just keyword matches.
How it works
Directed graph + vector search
The directed graph captures structure: what depends on what, what flows where. Each edge has direction (A supplies B, not just "A and B are connected"). This lets you trace propagation paths and model how disruptions cascade through the network.
Vector search adds context: semantic embeddings of reports and signals enable meaning-based retrieval. Query by concept, not just keyword, to surface the most relevant intelligence for any scenario.
Architecture
- Graph layer: entities as nodes, relationships as directed edges, supporting traversal and path-finding queries.
- Vector layer: semantic embeddings of signals and reports for meaning-based retrieval and similarity search.
- Scenario engine: test disruptions, model propagation, estimate cascade timing, and compare alternatives.
- Traceability: every query logged, every scenario reproducible, every output traceable to its inputs.
Why it matters
Structure enables foresight
In complex systems, the bottleneck isn't information—it's understanding how things connect. A constraint at one node can cascade through dependencies in ways that aren't obvious without the right model.
Dependency mapping
See the structure: what depends on what, what flows where, what breaks when something fails.
Cascade modeling
Trace propagation paths through the graph. Estimate which downstream nodes are affected and when.
Scenario comparison
Test multiple "what if" scenarios. Compare outcomes, evaluate alternates, and inform decisions.
Context retrieval
Pull relevant signals and reports by meaning. Get the context you need for any node or scenario.
We’re building graph-first infrastructure intelligence (research): systems that model dependencies, estimate cascading risk, and support informed decision-making.