Services / Data Annotation

Data Annotation

Text, document and image labeling for analytics and AI teams, with written quality checks.

What we solve

Model quality depends on label quality. We provide consistent annotation with review and measurable accuracy.

Classification, entity and extraction tasks.

Boxes, segmentation and attributes.

Written rules and test sets for consistency.

Review sampling and accuracy reporting.

Quality funnel

Each stage checks the one before it, and reviewer findings flow back into the guidelines.

Guidelines and test set

Stage 1Written rules and sample answers agreed with you.

Pilot and calibration

Stage 2A small batch aligns labelers with your expectations.

Production labeling

Stage 3Text, document and image tasks at your volume.

Review sampling

Stage 4Reviewers check a sample against the test set.

Accuracy report

Stage 5Measured accuracy shared on a regular cadence.

Feedback loop: reviewer findings update the guidelines, so the next batch starts from a better baseline.

Can you follow our guidelines?

Yes. We calibrate against your guidelines and sample sets before scaling.

What kinds of data can you label?

Text, documents and images, including classification, entity tagging, extraction, bounding boxes and segmentation.

How do you measure label accuracy?

Reviewers check a sample of each batch against a test set, and we report the accuracy to you on a regular schedule.

Describe the problem and we will reply with a proposed scope, timeline and cost, usually within two working days.