Source-aware
Delivered records carry the agreed source and lineage identifiers. Provenance is represented in the data and manifests, not left as a general promise.
About
A training data engineering team focused on language
We build text corpora and post-training datasets for language and generative AI — and we document them well enough that somebody else can check our work.
01
The hard part is not collecting text
why language dataCollecting text is only the first step. Knowing what was collected — which language, domain and source, under which policy, and what was removed and why — determines whether the resulting dataset can be inspected and defended later.
That is why our pipelines are built around decisions rather than volume. Every stage writes down what it did to a record: which layer rejected it, which cluster it belonged to, which reviewer looked at it, which pipeline version produced it. The result is not just cleaner data — it is data you can defend in a conversation you did not expect to have.
02
Five things that hold across every project
working principlesQuality-layered
Six independent filters, each with its own decision and reason code — never one cleaning step.
Language- and domain-aware
Language, script and taxonomy are scored and recorded, not assumed from the source.
Schema-flexible
Your fields, your unit, your IDs. We adapt to your stack rather than exporting ours.
Auditable by design
A release is a package: data, schema, dataset card, provenance manifest, quality report and rejection summary, plus record-level rejection details where permitted.
Ukraine, Vinnytsia
Talk to us
[email protected]. Open roles are listed when there are open roles.
Bring us a language, a domain or a schema.
We will help define a feasible pilot, quality criteria and a delivery format for your model.