Task and coverage specification
A written schema, label definitions, the categories and languages to cover, known difficult cases, and explicit acceptance criteria.
Custom training dataset development
We prepare and label training examples for your model from approved sources. Each release records where every example came from, which cases it covers, and what changed since the last version.
ScopeFrom a pilot sample to a full production dataset
Delivery timingSet after source review and pilot acceptance
How we check itWe check the release against the agreed coverage and quality rubric. Any effect on the model is measured separately, on test tasks kept out of training.
Data designed around what your model needs to learn
We start with sample inputs and the errors you want to reduce. With your domain expert, we define the labels and the cases the dataset must cover. Your team reviews a small batch before we produce the full collection.
Good fit
- Product teams adapting AI to a specific domain or workflow
- Research teams building their own training collections
Not the right fit
- Buyers looking for bulk data with no defined task
- Projects that rely on sources whose training rights cannot be confirmed
What you get
Reviewed training collection
Prepared records from approved sources, each with a stable ID, a link to its source, and labels where required, plus a list of what was excluded and why.
Release and handoff package
Versioned data in agreed formats, a dataset card describing contents and limits, lists of the records in each training and test split, validation results, and a working example that loads the data.
How it works
Map the learning task
We review the target model, its intended use, your available sources and usage rights, and the behavior the data needs to teach.
Approve a pilot sample
We prepare a representative pilot batch. Your domain owner reviews its coverage and labels before we expand it.
Build and accept the release
We produce the agreed collection, investigate quality failures, lock the records reserved for testing, and document each acceptance decision.
Scope, cost, and ownership
What we need from you
- A target model task and representative inputs
- Access to approved sources and their permitted training uses
- A domain owner to review the pilot sample
What affects cost
- Number, length, languages, and media types of records
- Source cleanup, labeling complexity, and reviewer effort
- Storage, export requirements, and delivery frequency
Technical scope
- JSONL, CSV, Parquet, or media with linked manifests as agreed
- Record-level source tracking, with splits grouped by source or entity where related records must stay together
Support and maintenance
At handoff, we agree who handles source changes, corrections, removal requests, and future dataset versions.
Common questions
Can you work with our existing data?
Yes. We can clean up and extend an existing collection or build a new one from approved sources. We first check access, permitted uses, quality, and gaps in coverage.
Which data types can be scoped?
Text, structured records, documents, and collections such as image-text or audio-transcript pairs. We scope media processing, specialist review, and tooling against your actual sample files.