RomeoApps

quality operations / synthetic lab

Annotation quality is an operating system

Turn ambiguity into labels a model can trust.

This RomeoApps-owned lab makes the hidden work visible: write the rule, measure two-rater agreement, test against a gold set, adjudicate exceptions, then automate only inside a reviewable boundary.

Run the synthetic batch See the EUR 250 sprint

No customer dataNo client-results claimHuman acceptance retained

Synthetic evidence. Twelve invented commercial-property excerpts; no insurer, customer or production dataset.

Every displayed metric is recomputed in your browser.

01 / inspect the batch

A disagreement is a design input, not annotator noise.

Use the review control to inspect the source, the two labels, the gold answer and the exact guideline. Adjudications update the queue but never rewrite the original annotations.

PAIR AGREEMENT—policy ≥ 85%
GOLD ACCURACY / A—policy ≥ 90%
GOLD ACCURACY / B—policy ≥ 90%
REVIEW LOAD—disagreements / total

Refresh the page to reset this local demonstration.

RecordAttributeAnnotator AAnnotator BState

02 / encode the judgment

Guidelines have rules, exceptions and an escape hatch.

RULE 01

Label observable evidence.

Use the source excerpt, not a likely real-world assumption. If evidence is absent, mark unknown.

RULE 02

Mixed beats forced certainty.

When two material classes are explicitly present, use the available mixed or partial label instead of choosing the dominant one.

RULE 03

Unknown is not none.

none requires affirmative absence. Missing, illegible or out-of-scope evidence remains unknown.

ESCALATE

Do not invent a taxonomy.

Route contradictions, unseen combinations and business-rule conflicts to adjudication with the source span attached.

03 / automate inside the boundary

The LLM can suggest. The quality policy decides.

Automation is useful when confidence, sampling and failure behavior are explicit. The model never silently promotes its own pre-label into accepted ground truth.

  1. 01
    Pre-label

    Return label, confidence, evidence span and rule ID.

  2. 02
    Route

    Below 0.92 confidence, unseen classes and contradictions go to a human.

  3. 03
    Sample

    Review 100% of new taxonomies and at least 25% of stable batches.

  4. 04
    Hold

    Stop release below 85% agreement or 90% gold accuracy.

COST / THROUGHPUT SCENARIO

editable
Base cost / label
—
Rework cost / label
—
Effective cost / accepted label
—
Accepted rows / hour
—

Scenario math only. Change the inputs to model your own operation; no market rate or production result is implied.

04 / leave an audit trail

Export the state that produced the decision.

The download includes the immutable synthetic annotations, current adjudications, policy thresholds and recomputed metrics. It contains no browser identifiers or network data.

FIXED FIRST UNIT EUR 250 five business days

RomeoApps Annotation Quality Sprint

Make one labeling workflow measurable before you scale it.

For one taxonomy and up to 100 safe redacted or synthetic samples: guideline rewrite, gold-set audit, agreement and rework metrics, adjudication rules, and a human-reviewed automation map. You receive the source workbook or SQL, findings and acceptance evidence.

  • Written-first fit and fixed scope
  • No credentials or raw customer data for fit
  • DPA and access boundary before protected data
  • No unreviewed model output promoted to ground truth
Send the written fit brief