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CMD+RVL is built on modern data infrastructure you already recognize — but extends it with provenance, context, lineage, and admissibility so monitored outcomes and provenance records remain reproducible as data, assumptions, and outputs change over time.

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Provenance Receipt
AI Output
$2.47B adjusted NAV
2025-06-14 09:41 UTC
Transform
Weighted average
847 positions
Transform
Normalized to USD
ECB daily fix rate
Source
SEC EDGAR 10-K
CIK 0001234567
Source
FRED SOFR rates
SOFR.2025-06-13
Provenance verified5 nodes · 2 sources · full chain

A familiar stack, extended to support defensible decisions

CMD+RVL is intentionally built on standard data infrastructure patterns. The difference is a provenance layer that carries context, lineage, and trust signals past ingestion and warehousing — connecting raw sources to derived fields, analytical views, provenance records, and monitored outcomes.

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 provenance, 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 provenance record references — carried through to derived views and outcomes rather than stopping at the warehouse.
01"Show the source data, transformations, and assumptions behind this chart or dataset."
02"What changed since the last release, and which downstream views or products were affected?"
03"Where do we have gaps, latency, or revisions that could impact current conclusions?"
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CMD+RVL doesn’t replace modern data infrastructure. It extends it with context, lineage, admissibility, and provenance that hold up under real scrutiny.

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 provenance are preserved all the way to decision-facing outputs and provenance records.

01

Beyond snapshots

Datasets and views remain aligned to source revisions, with clear visibility into what changed and why.
02

Reusable outcomes

Signals, datasets, and documentation persist as artifacts that compound over time instead of being rebuilt each cycle.
03

AI-ready by construction

Structured, lineage-aware context supports downstream AI workflows, including deterministic regeneration of artifacts and the option to derive retrieval layers or embeddings when needed.
Built for:
Hedge Funds
Asset Managers
Banks
Risk Management
Enterprise Analytics
Start with concrete Signals, adopt data products for trusted inputs, and go deeper with engagements when the question demands it.
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Decisions that hold up under scrutiny. Built on open standards.
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