Use cases
Four scenarios we build data for, and a short decision helper to point you at the right product first.
One team usually needs three different things at once: a corpus to build on, a corpus to adapt with, and behaviour data to tune against. They are separate products with separate acceptance criteria.
LLM pretraining & fine-tuning →Monolingual data to build on, parallel and comparable corpora to train on, alignment and filtering you can inspect, and terminology labels that survive into the release.
Machine translation →Retrieval quality is mostly an ingestion problem. Structure that survives extraction, chunks that keep their lineage, and an evaluation set with unanswerable questions in it.
RAG & search →Most enterprise AI data work is a permissions problem before it is a modelling problem. Whose data is it, what may it be used for, and who reviews the result.
Domain-specific AI →| If you need | Start here |
|---|---|
| Data for pretraining, adaptation and post-training | LLM pretraining & fine-tuning |
| Training data for machine translation | Machine translation |
| Clean, traceable documents and evaluation data for RAG | RAG & search |
| Domain corpora from sources you are allowed to use | Domain-specific AI |
We will help define a feasible pilot, quality criteria and a delivery format for your model.