Quantitative risk platform
One control plane for every model decision.
Build, validate, monitor, and govern quantitative models inside one inspectable risk environment.
Model evidence chainAccepted for reviewDataset 18 / Challenger 04 / Policy 12
72ReviewComposite model risk
Inspect the model before it changes the decision.
Move the stress level and compare the forecast, leading drivers, decision threshold, and retained evidence.
Decision under analysis
Portfolio early warning
PD migration and loss forecast
Composite risk72Review
Accepted baselineAdverse scenario
NowModeled horizon
Driver contribution
Illustrative sample data for product demonstration. Model design, performance, thresholds, and actions are validated against each customer's data and control requirements.
Six modules. One evidence trail.
Start with the capability needed for one risk decision, then connect more modules without losing lineage.
Model Studio
Compare statistical, machine learning, graph, and rules-based models against the same accepted evidence.
Risk Scoring
Calibrate scores, grades, limits, thresholds, overrides, and review queues.
Scenario Lab
Run deterministic, probabilistic, sensitivity, and reverse stress simulations.
Model Monitoring
Track quality, drift, stability, performance, overrides, and outcomes across versions.
Evidence Store
Keep lineage, datasets, tests, approvals, decisions, and exports connected.
Decision Integration
Serve governed outputs through APIs, files, events, analyst workbenches, and existing systems.
Stress the operating decision.
Change the scenario and inspect how the composite score, leading driver, and recommended action move together.
Composite risk
/100
ReviewBaselineSevere
Leading driverPayment velocity
Recommended actionTighten review threshold
Evidence retainedFeatures, model version, policy, reviewer
Fit the control environment already in place.
Deploy in a dedicated tenant or an agreed private environment, with boundaries defined in the SOW.
Warehouses and lakes Operational systems Streams and files
Private quantitative coreModel, score, simulate, govern
Development, validation, production, and audit evidence remain connected to the accepted decision policy.
Analyst workbench Decision API Audit evidence
IdentityCustomer administrator invitations and approved rolesLineageSource, feature, model, policy, action, and reviewerReleaseValidation gates and model acceptanceOperationsMonitoring, change records, and recovery
Commercial planning
Model the deployment before writing the SOW.
$3k-$9kIllustrative monthly quantitative AI range per customer environmentOpen pricing calculator