Everything your training data goes through

Dataset import

Bring data in from Hugging Face by dataset name or upload your own JSONL files. dropoutt understands the common chat and instruction formats — including JSON-encoded messages and content blocks — and normalizes every row into inspectable records.

Imports run as background tasks, so a 50k-row dataset lands while you keep working. Every record is searchable, filterable, and taggable from the moment it arrives.

Duplicate review

Near-identical records quietly skew training runs. dropoutt fingerprints your datasets and surfaces semantic duplicates as clusters, each with a similarity score.

You review cluster by cluster: keep the best phrasing, drop the rest, and clean an entire dataset in one pass instead of eyeballing spreadsheets.

Clustering

Records group into clusters so you can see the real shape of a dataset — which topics dominate, which are underrepresented, and where the odd outliers live.

Drill into any cluster to read its records, tag them, or route them into a group for later work.

Deep analysis

Semantic, topic, language, and quality analysis run as background tasks over any dataset. Results come back as visual breakdowns you can act on: language mix, topic distribution, quality flags, and process suggestions.

Exports included — take a report to your team without giving them a login.

Synthetic generation

Generate and distill synthetic training candidates with your own provider keys. Keys arrive per-request and are never stored.

Generated candidates land in the same review pipeline as imported data — deduplicate, analyze, and curate them like anything else before they reach a training file.

Team workspaces

Workspaces keep a team working on the same data without stepping on each other: shared datasets, tasks, notifications, and a timeline of every curation decision.

Invite members with roles, hand off review work as tasks, and keep the history reviewable.

Try it first

Early access is rolling out through the waitlist.