Foundations
CMD+RVL is built on modern data infrastructure you already recognize—but extends it with verification, context, lineage, and admissibility so Verified Outcomes and Evidence Packs remain reproducible as data, assumptions, and outcomes change over time.
A familiar stack, extended to support defensible decisions
CMD+RVL is intentionally built on standard data infrastructure patterns. The difference is a verification layer that carries context, lineage, and trust signals past ingestion and warehousing—connecting raw sources to derived fields, analytical views, Evidence Packs, and Verified Outcomes.
Temporal source truth preserved in full.
Public data sources are ingested in full and retained in their original form so revisions, restatements, and late updates remain inspectable. CMD+RVL preserves what was known, when it was known—enabling deterministic reprocessing and historical reconstruction rather than relying on overwritten “latest” states.
See Data Products →Context-aware transformations that propagate meaning, not just rows.
Transformations follow standard ELT patterns, but models are explicitly tied back to source state, timing, and assumptions. When data changes, CMD+RVL surfaces not just that something changed—but why, and which downstream views and conclusions are affected.
Explore Engagements →A single semantic baseline across delivery paths.
The canonical warehouse powers Signals, data products, and engagements from the same governed datasets. This keeps definitions, timing, and known gaps consistent across research, sharing, and downstream AI workflows—without repeated reconciliation.
Browse Snowflake Marketplace →Durable artifacts designed to survive review.
Analytical views are treated as durable artifacts rather than ephemeral notebooks. Each view remains linked to its data dependencies, assumptions, and event timing so Evidence Packs and Verified Outcomes can be revisited, challenged, and reused without manual reconstruction.
Explore Signals →Metadata as the control plane for trust
CMD+RVL treats metadata as a first-class system, not an afterthought. Operational metadata, semantic context, and outcomes are connected so teams can reason about verification, trust, freshness, and impact without manual backtracking.
Catalog-driven governance.
CMD+RVL maintains its own data catalog, built as a fork of Amundsen. The catalog includes ingestors, ownership, schema tracking, alerts, and operational metadata, with APIs for programmatic read and write access.Extended lineage and assurance metadata.
The metadata model is extended beyond tables and columns to capture lineage, coverage, freshness, latency, schema history, and methodology notes plus Evidence Pack references—carried through to derived views and outcomes rather than stopping at the warehouse.Built to integrate, designed to last
Because CMD+RVL is grounded in standard data stack patterns, it integrates cleanly into existing research and production environments. The differentiation is how context, timing, lineage, and verification are preserved all the way to decision-facing outputs and Evidence Packs.