Annotation handbook
A label taxonomy with definitions, worked examples, ambiguity rules, exclusions, and an escalation route for difficult records.
Data annotation and quality review
We define labeling rules, review annotated examples, and investigate disagreements. The release includes the label definitions and our quality findings.
ScopeLabel design, a pilot to align reviewers, and scoped production
Delivery timingProduction timing is set once reviewers are aligned on the pilot
How we check itQuality reports include audit sampling and disagreement analysis. They report label consistency separately from whether labels are correct for the task.
Labels your team can explain and reproduce
We write label definitions with your domain reviewers and use sample records to find ambiguous cases. The review process records how each disagreement is resolved. We confirm reviewer expertise and availability before committing to a full batch.
Good fit
- Teams with inconsistent labels or unclear annotation instructions
- Specialist AI projects that need human judgment and a record of who reviewed what
Not the right fit
- Buyers seeking open-ended bulk labeling at a fixed per-record price
- Projects that treat model-generated labels as verified expert labels
What you get
Consistent review workflow
Configured tasks, reviewer permissions, shared practice examples, a sample of records reviewed twice, and a record of how disagreements were settled.
Label quality release
Labeled records showing who reviewed each one, agreement measures suited to the task, coverage by category, and a documented correction queue.
How it works
Design the labeling task
We study representative source material and turn what the model must learn into clear labels and reviewer instructions.
Align reviewers on shared examples
Reviewers label the same sample, we investigate their disagreements, and we revise the handbook before committing to production volume.
Review and resolve exceptions
We audit production batches, settle ambiguous labels, and release accepted records with each quality decision recorded.
Scope, cost, and ownership
What we need from you
- Representative records and a draft label objective
- A domain expert to settle disagreements, and agreed reviewer qualifications
- An access-controlled labeling environment and data-handling requirements
What affects cost
- Minutes of review per record and specialist expertise
- Media length, label detail, and how often disagreements need settling
- Annotation tooling, access setup, and audit sampling depth
Technical scope
- Classification, extraction, preference ranking, and media annotations scoped individually
- Label versioning, reviewer access control, and audit exports
Support and maintenance
We keep the handbook and correction history together, and realign reviewers when labels or source material change.
Common questions
Can AI assist with labeling?
Yes. Model suggestions can reduce some preparation work. We record which labels a model suggested, measure the effect on review quality, and agree on which labels need independent human checks.
Do you provide domain experts?
Yes, within the agreed scope. Your experts can make the domain decisions, and we can arrange additional specialist reviewers. We confirm reviewer qualifications and availability for each project.