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Generate missing coverage inside the same curation workflow

Direct specialized agents in a shared thread, then review generated records beside the source data they extend.

Synthetic data interactive preview

@schema-scout @coverage-analyst @quality-reviewer Create three diverse reasoning rows for underrepresented topics. Preserve the schema, avoid overlap, and flag uncertain outputs.

How the workflow fits together

Generation starts from the gaps you can see. Agents coordinate around schema, coverage, and quality constraints while streamed candidates remain subject to human review.

  1. Describe the coverage gap

    Start from underrepresented topics, languages, or reasoning patterns.

  2. Coordinate specialized agents

    Keep schema, coverage, and quality checks visible in one thread.

  3. Review before use

    Accept, reject, deduplicate, and analyze candidates before training.

Preserve schema constraints

Generate candidates that match the structure of the source dataset.

Avoid blind augmentation

Create records against measured gaps rather than adding undirected volume.

Keep humans in control

Treat generated rows as review candidates, not automatic training data.

Fill the gaps in your next training set

Join the waitlist for early access to collaborative synthetic generation.

Synthetic data · dropoutt