Enterprise Context Engine
Bring the CMD+RVL context graph in-house—your own private, analyst-grade substrate for explainable models, agent workflows, and ratings-grade lineage.
A private context layer your models can trust
Deploy CMD+RVL’s context graph directly into your own environment. Run it on AWS, Azure, or hybrid. We keep customer data private—while capturing lineage, timing, and features so every outcome becomes verifiable, governable, and ready for audit.
Public and internal sources.
Connect public and internal datasets, filings, cash-flows, events, and proprietary feeds through one normalization layer. The engine preserves source-of-truth lineage across every transformation and timestamp, giving you an institutional memory for models, analysts, and agents.
Request Architecture OverviewDecisions for every output.
Every outcome—model output, derived metric, or event-driven feature—retains full lineage, decision history, and feature provenance. Your analysts and AI systems get a ratings-grade audit trail for how results were formed, why they changed, and whether they should be trusted.
Request Architecture OverviewTo people, models, and agents.
Serve context to analysts, scoring systems, risk models, and agents from a single authoritative substrate. The engine integrates beside your warehouse—Snowflake, Databricks, BigQuery—without forcing a rebuild of existing pipelines.
Request Architecture OverviewHow it fits
The context engine adapts to your architecture: cloud, hybrid, or on-prem. It enriches your warehouse with lineage, schema tracking, event alignment, and model governance metadata—without requiring you to rip or replace anything.
Private deployment.
Deploy the engine inside your VPC using Docker on AWS or Azure. Customer data never leaves your environment. Only lineage, metadata, and features flow through the system—creating an explainable, internally governed context layer.Model-in-Context.
Run cash-flow, prepayment, risk, or custom analytics beside the context layer. Ground agent prompts, LLM reasoning, and model inputs with assurance-grade context and full provenance. Every model becomes explainable by design.Get the architecture overview
We’ll map deployment patterns, data boundaries, and security controls—from cloud to hybrid to on-prem. See how the context engine becomes the backbone for explainable analytics, agent workflows, and emerging assurance standards.