The Dimensions
The readiness score is a summary; the products are the file-level signals and the filtered slice. Unrun dimensions are excluded from the score, not counted as zero.
The Data Quality Status Tag
Quality verdicts share one flag:Data Quality Status. A document tagged with it is either redundant (a duplicate or superseded copy, written by the platform’s duplicate detection), expired (out of date for the use case’s freshness rule), or otherwise flagged as unfit, such as irrelevant. A document with no Data Quality Status tag has passed every quality check, which is why the AI-ready slice uses a single not_exists condition.
Files are tagged and excluded, never deleted, so every decision is auditable and reversible.
Readiness Is Per Use Case
A slice is a use case. Readiness scopes to a slice and applies that use case’s own rules: which quality dimensions matter, what the freshness cutoff is, which document types are relevant, and whether sensitivity is part of the gate. The same file can be ready for one slice and not another.Where It Runs
- In the app, the Data Quality screen walks the flow: Summary, Uniqueness, Freshness, Metadata, and AI Ready Data, with review steps for duplicates and expiring files. Background jobs appear in the Command Center.
- Through the SDK, the whole flow is triggerable programmatically: run the scan, write the signals, and produce the curated slice inside your own pipeline. Prepare an AI-Ready Dataset is the complete headless walkthrough, ordered so the expensive classification step only ever runs on documents that survive the free filters.
Next Steps
Prepare an AI-Ready Dataset
The full readiness flow through the SDK.
Keep Answers Current Over Time
The freshness dimension in depth.
Protect Sensitive Data
The opt-in sensitivity gate.
Data Slices
The slice conditions that enforce readiness.