Explainable outcomes, fast.
Run Intex and Trepp models in our cloud, harmonized with CMD+RVL’s context graph. Every cashflow, curve, and assumption has ratings-grade lineage—you just need your license.
Model in Context.
Run cashflows, prepayment models, and scenarios directly in CMD+RVL’s managed environment. Every run, assumption set, and curve is anchored in our context graph—so you can trace any number back to source data, model version, and scenario parameters, with the discipline of an analyst firm and the rigor of a future ratings platform.
Intex + Trepp in the cloud.
We execute your licensed Intex or Trepp cashflows in our environment—your licenses, your assumptions, your scenarios—then normalize all outputs into a unified context schema. Instead of static reports, you get structured, queryable, explainable model data.
Book a discovery callAll runs. All results. One view.
Waterfall curves, collateral data, prepayment vectors, WALs, and risk metrics are consolidated into a single analytics plane. Compare runs across vintages, shocks, and policy regimes without rebuilding pipelines—everything is aligned through the context graph.
See Signals in actionEvery number has a source.
Inputs, curves, assumptions, and model versions are tracked automatically. Provenance is part of the architecture, not an afterthought—giving risk, valuation, and performance teams a defendable audit trail for every output they use.
Learn about the context graphBuilt for model velocity.
Hedge funds and asset managers run hundreds of scenarios a week. Most end up as flat files that are hard to compare and impossible to audit at scale. Model-in-Context turns every run into a live, explainable dataset—linked through CMD+RVL’s context graph with full lineage, coverage, and timing.
Explainable by design.
Each model run is harmonized into a common schema and tied to the context graph—linking source data, pool and tranche attributes, curve choices, and scenario parameters. You can move from any output metric back to its inputs in seconds.Continuously aligned.
When inputs, reference data, or macro curves shift, new runs can be executed and compared against prior scenarios using the same lineage framework. Outputs stay live, consistent, and auditable—so analytics keep pace with markets instead of lagging them.Explainability is performance.
When every model run carries provenance, scenario work stops being a one-off exercise and becomes a reusable asset. Teams can test more ideas, retire bad assumptions faster, and demonstrate discipline to investors, risk committees, and regulators.