Ask what happens before it does.
Model any system as a graph. Clone it into an isolated namespace. Apply the shock: supplier failure, rate hike, patient surge, infrastructure outage. Run graph algorithms and quantum-inspired solvers. Compare outcomes across scenarios. The answer to “what should we do if this happens?” shouldn't wait for the crisis.
How it works
Five steps. No simulation software.
Model your system as a graph
Nodes are entities (suppliers, facilities, assets, patients). Edges are relationships with capacity, weight, and constraint properties. DocIntel can extract these from existing documentation automatically.
Clone into a simulation namespace
Multi-tenant isolation creates a snapshot of your live graph in a new namespace. Zero data copying — the clone is ready instantly. Run multiple scenarios in parallel, each isolated.
Apply the shock
Remove nodes (supplier failure), modify edge weights (cost increase), add new entities (new route), or change constraints. The live graph is untouched.
Run analysis and optimisation
Graph algorithms measure impact: max-flow shows capacity change, bridge detection finds new single points of failure, betweenness centrality shows who bears extra load. Quantum solvers re-optimise routing, scheduling, or allocation under the new constraints.
Compare scenarios
AI agents query all simulation namespaces, compare metrics (total flow, makespan, cost, risk), and surface the best response strategy. Results feed directly into Journey Engine workflows for action.
What runs under the hood
Everything you need. Already built in.
Graph cloningMulti-tenant namespace isolation — instant snapshot, zero copy
Max-flow / min-cutEdmonds-Karp — measures capacity and bottlenecks under any topology
Bridge detectionTarjan DFS — finds every single point of failure before it fails
Betweenness centralityWhich nodes bear the most load? Who becomes critical after a shock?
Shortest pathDijkstra weighted — re-route analysis after node/edge removal
quantum.vrpRe-optimise routing under new capacity and constraint landscape
quantum.schedulingRe-solve job/resource allocation under surge or shortage
quantum.portfolioRebalance binary allocations under rate or sector shocks
GraphRAGAI agents query all scenarios and surface the best response strategy
Use cases
Systems you can stress-test today
Supply chain resilience
Model your supplier network as a graph. Clone and remove your top 3 suppliers simultaneously. Max-flow analysis shows how throughput degrades; quantum VRP re-routes around the failure. Know your resilience before the crisis.
Clinical pathway stress testing
Simulate patient surge scenarios on your clinical pathway graph. Which stages bottleneck under 2× demand? Where does SLA breach first? Re-allocate resources via scheduling optimisation and measure the improvement.
Portfolio stress testing
Apply interest rate or sector shocks to your portfolio graph. Binary portfolio optimisation (quantum.portfolio) finds the optimal reallocation under the new constraints — not just sensitivity analysis, but actionable rebalancing.
Infrastructure failure impact
Bridge detection finds every single point of failure in your infrastructure graph. Remove it, run betweenness centrality, see which nodes are now overloaded. Quantify impact before decommissioning anything.
Urban planning & traffic modelling
Model road networks as a graph. Close a road (remove an edge), run shortest-path and max-flow. MEP flow analysis from the AEC vertical shows how congestion redistributes — without simulation software.
Opinion & influence simulation
On a belief graph, ask: if this influential voice changes position, how does opinion propagate? Temporal PageRank on the modified graph shows which clusters shift and at what speed.
FAQ
Common questions
How does Purple8 enable scenario simulation?
Purple8 combines three capabilities: (1) multi-tenant graph isolation — clone your live graph into a simulation namespace with zero data copying; (2) graph algorithms — run max-flow, betweenness centrality, bridge detection, shortest path, and PageRank on the modified graph to measure the impact of changes; (3) quantum-inspired optimisation — re-solve VRP, scheduling, or portfolio allocation on the shocked graph to find new optima. Compare results across scenarios side by side.
What kinds of systems can be simulated?
Any system that can be modelled as a graph: supply chains (suppliers, factories, distribution centres as nodes; flows and capacities as edges), clinical pathways (patients, stages, resources, bottlenecks), financial portfolios (assets, correlations, risk factors), infrastructure networks (nodes as facilities, edges as connections with capacity and failure probability), and belief or influence networks (opinion propagation modelling).
How does scenario cloning work?
Purple8's multi-tenant architecture (tenancy.py) isolates data by tenant prefix or dedicated DB path. To create a simulation scenario, you clone the live graph into a new tenant namespace, apply your hypothetical changes (remove nodes, modify edge weights, add new entities), then run your analysis. The live graph is untouched. Multiple scenarios can run in parallel — each in its own isolated namespace.
What is the difference between simulation and what-if analysis in Purple8?
What-if analysis applies a single change and measures the outcome — remove supplier X, what does flow analysis show? Simulation runs multiple scenarios in parallel, compares outcomes across them, and uses quantum-inspired optimisation to find the best response to each shock. The combination answers not just 'what happens if?' but 'what should we do if?'
Related capabilities
Model your first scenario today
Free to start. Graph cloning, algorithms, and quantum solvers are part of the base product. Nothing extra to buy.