Private or customer-owned data
The data exists inside your organisation and cannot leave it casually. Isolation and policy come before pipelines.
Use case · enterprise AI teams
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.
Each one points at a different product — the decision helper below turns the symptom into a starting point.
The data exists inside your organisation and cannot leave it casually. Isolation and policy come before pipelines.
Your labels, your hierarchy, your definitions — and nothing off the shelf maps onto them cleanly.
A one-off delivery is obsolete in a quarter. What you need is a release cadence.
Your sources, taxonomy, schema and delivery channel, built as a repeatable pipeline.
Domain-scoped public text to sit alongside your private data.
Behaviour data built on your domain: instructions, preferences, evaluation.
| If you need | Start here | Why |
|---|---|---|
| To use data you already own | Custom dataset development | Transformation under your policy, inside an agreed boundary |
| To add public domain text | Monolingual corpora | Source registry with include / exclude / review decisions |
| Your own taxonomy applied | Custom dataset development | Imported as given, versioned, never silently normalised |
| Domain-expert judgement in the loop | Custom dataset development | Review levels chosen per class, with adjudication |
| A quarterly refresh | Custom dataset development | Delta releases with a changelog and a split manifest |
Where the data lives and who can reach it during the project.
how measured · agreed per project, stated in contract
Agreement between reviewers on labels that matter to you.
how measured · inter-annotator agreement, per class
Records carrying a full, resolvable lineage chain.
how measured · manifest validation
What changed between two releases, and why.
how measured · changelog + delta manifest
We will help define a feasible pilot, quality criteria and a delivery format for your model.