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Technology · Qurator

Evidence is only as good as how it’s kept.

Qurator helps Qapitol organise the data, test sets, evaluation flows and assurance artefacts needed to validate AI systems consistently — the curation layer underneath the work, so the evidence behind a sign-off is structured, versioned and repeatable, not scattered.

Scattered evidence, curated into a base
Test dataCurated test set✓ organised & versionedEval runEvaluation flow✓ organised & versionedScenarioEdge-case scenario✓ organised & versionedSyntheticSynthetic dataset✓ organised & versionedVersionDataset version✓ organised & versionedArtefactAssurance artefact✓ organised & versioned

Scattered test data, evals, scenarios and artefacts, curated into one organised, versioned evidence base. Illustrative; not a measured result.

Why it exists

Scattered evidence can’t be signed off.

AI assurance breaks when test data, evaluation logic and evidence are scattered. When the base isn’t organised, none of the work behind a sign-off holds up.

  1. Teams can’t repeat an evaluation the same way twice

  2. Behaviour can’t be compared run-to-run, version-to-version

  3. No one can prove whether quality improved or degraded

What it does

Six inputs. One organised base.

Qurator curates the raw material of assurance — data, evals, scenarios and artefacts — into one structured, versioned evidence base that an evaluation can draw on and a sign-off can stand on.

Test data curation

The right data assembled into curated, controlled test sets.

Evaluation orchestration

Repeatable evaluation flows, run the same way every time.

Scenario creation

The edge cases that matter, captured as deliberate scenarios.

Synthetic data workflows

Synthetic data generated to cover what real data can’t reach.

Dataset versioning

Every dataset versioned, so a result can be traced to its inputs.

Evidence preparation

Assurance artefacts prepared and organised, ready for sign-off.

The outcome

An evidence base you can run again.

When the evidence base is curated and versioned, an evaluation can be repeated, a result can be compared, and a sign-off can be backed. Qurator powers the Build, Evaluate and Run stages — the curation layer the rest of the control layer draws on.

← See all six platforms
  • Build test sets for LLM evaluation
  • Curate edge-case scenarios
  • Orchestrate repeatable eval runs
  • Support regression assurance
  • Prepare controlled datasets for sign-off

Make AI evaluation repeatable.

Start with an AI Exposure Snapshot, or talk to us about your specific situation.