Foundations
CMD+RVL keeps sources, method notes, evidence records, and review paths attached to AI-assisted outcomes without replacing your warehouse, catalog, or GRC process.
A practical evidence layer beside the existing stack
Foundations is the operating model behind CMD+RVL outcomes. It records source state, method notes, decision records, evidence packs, and queryable references so teams can inspect a result later without rebuilding the trail.
- Source inventory
Know what was used and when.
Keep source identity, release timing, revisions, and known gaps attached to the result so later review starts from the same state of the world.See Data Products - Method context
Keep transformations explainable.
Tie derived fields and outputs to assumptions, timing, coverage, and methodology notes so changes can be reviewed instead of guessed.Compare package fit - Stack boundary
Work beside the systems already in place.
Use warehouses, catalogs, marketplaces, and delivery surfaces as they are. CMD+RVL carries the evidence record around the outcome rather than becoming the source system.Browse Snowflake Marketplace - Evidence records
Leave outputs ready for review.
Treat reports, monitors, Signals, and evidence packs as durable artifacts linked to dependencies, assumptions, and event timing.See evidence pages
How the evidence layer stays inspectable
The implementation is intentionally plain: know the sources, record the method, attach the evidence, and show what changed.
Source state
Track source identity, release timing, revisions, and known gaps so an output can be checked against the source state it used.Method notes
Carry transformations, assumptions, coverage, and review notes with derived data instead of leaving them in a separate notebook or chat thread.Evidence records
Connect outcomes, evidence packs, and queryable references so later review starts from checked evidence, not a recreated spreadsheet trail.Change visibility
Surface drift, source revisions, and downstream impact when the inputs or assumptions behind a result move.Show the source state and assumptions behind this result.
What changed since the last review, and which outputs were affected?
Where do gaps, latency, or revisions change how this result should be used?
Built to integrate, designed for later review
The stack is familiar on purpose. The difference is that context, timing, lineage, and review notes keep traveling with the decision-facing output.
Source state over snapshots
Datasets and views stay tied to source revisions so reviewers can see what changed and why.Reusable evidence
Signals, datasets, and outcome records persist as artifacts that can support the next scope.Agent-ready context
Structured references give AI workflows and deterministic tools the context they need without turning exploration into an unsupported claim.
- Hedge Funds
- Asset Managers
- Banks
- Risk Management
- Enterprise Analytics
Start with concrete Signals or data products, then choose a package when the question needs accountable delivery.
For enterprise scopes, hosting, access, and review boundaries are defined during discovery.