Create datasets for approved records instead of leaving clean output trapped in one review session.
Datasets
Keep approved data organized and reusable.
Datasets give teams a structured place to keep approved records after review. Instead of treating clean output as a one-time export, teams can search it, edit it, enrich it, and reuse it in future workflows.
When approved rows need a home
Datasets gives reviewed data a place to stay useful.
Once data is approved, teams need more than a one-time export. They need a place to keep structured records usable across repeated workflows.
Browse and search structured collections over time.
Edit rows directly when operational data changes.
Manage dataset structure and schema-related workflow in context.
Export or enrich stored data without rebuilding it from source documents again.
How teams use Datasets
From approved rows to reusable records.
01
Create or choose a dataset
Start a new structured collection or append reviewed output into an existing one.
02
Store approved data
Move clean rows into a dataset once they are ready to be kept beyond one document.
03
Search, edit, and refine
Work with stored rows directly instead of reopening old review batches for every small update.
04
Reuse and deliver
Use datasets as a stable source for exports, repeatable structure, and downstream work.
Inside the dataset layer
What teams manage after approval.
Dataset creation
Create structured collections for different workflows instead of keeping all approved data in one undifferentiated pool.
Search and browse
Find datasets and records quickly as operational data grows over time.
Direct row editing
Update stored records in place without rebuilding the whole document workflow again.
Schema-aware workflow
Keep dataset structure visible so records stay useful and not just loosely stored.
Dataset Copilot
Use Copilot inside datasets to speed up structured cleanup and refinement work.
Export support
Use datasets as a durable source for downstream exports instead of relying only on one-time review output.
Where it fits
Datasets is the reuse layer after review.
Datasets turns approved output into durable structured data.
Review produces trusted rows. Datasets makes that trust reusable by giving teams a stable place to keep, search, and manage approved records before they are exported or reused again.
Workflow
Review -> Datasets -> Templates / Exports
Why teams use Datasets
Approved data should stay useful.
Without a structured storage layer, teams end up re-exporting, re-cleaning, or rebuilding data they already approved once. Datasets helps turn reviewed output into something operationally durable.
What teams gain
Structured reuse instead of one-off cleanup.
- Approved data stays useful beyond one document.
- Teams can build durable structured collections over time.
- Less rework when similar data comes in again.
- Cleaner handoff into templates and exports.
Keep reviewed data ready for the next step.
Datasets is where approved output becomes reusable. It helps teams move from one-off document cleanup toward repeatable structured operations.