Question 1

Which annotation modalities do you support?

Accepted Answer

Image, video, LiDAR and 3D point cloud, text, audio, and multimodal, all delivered at 97%+ accuracy through the three-tier QA cascade. Within those, Corpshore handles bounding boxes, polygons, semantic and instance segmentation, keypoints, object tracking, cuboids, NER, classification, and relationship labeling.

Question 2

Can you work in our tooling?

Accepted Answer

Yes. Teams work in your annotation platform or in Corpshore's, matching your taxonomy, label schema, and export format. Working in your tooling keeps annotations flowing straight into your existing pipeline with no re-mapping step.

Question 3

How do you guarantee label quality?

Accepted Answer

Every unit passes a three-tier QA cascade: annotator plus peer review, an expert QA-lead audit, then programmatic consensus and gold-set checks that gate the batch before delivery. Inter-annotator agreement is tracked and fed back into annotator guidance, so quality is measured rather than assumed.

Question 4

How much does data annotation cost?

Accepted Answer

Annotation is priced by modality, volume, label complexity, languages, and turnaround, and typically runs 50 to 70% below US-domestic providers. A simple 2D bounding-box task costs far less per unit than dense 3D segmentation or expert medical labeling, so the fairest number comes from scoping a pilot against a real sample of your data.

Question 5

Can you annotate specialized or expert domains?

Accepted Answer

Yes. For domains like medical imaging, financial documents, or legal text, credentialed or domain-trained reviewers annotate under a documented protocol with provenance capture and tiered sign-off. This produces auditable, high-accuracy datasets that stand up to regulatory scrutiny.

Question 6

How do you handle large or ongoing annotation volumes?

Accepted Answer

Corpshore staffs dedicated pods rather than per-task marketplace bidding, so a program can scale to steady weekly throughput and hold a consistent taxonomy over time. The same team learns your edge cases across batches, which keeps agreement high as volume grows.

Question 7

What accuracy can we expect on our data?

Accepted Answer

The benchmark is 97%+ measured against gold sets, and a pilot establishes the realistic figure for your specific taxonomy and edge cases before you commit to volume. Difficult or ambiguous label schemas are tightened during the pilot so the production run starts from a proven rubric.

Annotation & Labeling

Scoped, staffed, and QA-gated

Every unit passes a three-tier QA cascade

Annotator + peer review

Expert QA lead

Programmatic + consensus

Annotation & Labeling, answered

Ready to scope a pilot?