Data annotation by people who know the field.
Some labels need a specialist. Our data annotation and labeling services match each task to credential-checked experts and record exactly who labeled what.
Data annotation, with proof.
General data labeling workforces are well suited to tasks anyone can judge. Many valuable annotation tasks are not like that: reading a radiograph, deciding whether a contract clause shifts liability, checking a proof, judging whether code is idiomatic and secure. Errors there are hard to see without expertise, and they compound in training. HUMXN routes annotation to experts whose credentials are checked when they join, matched to the task by field and seniority.
Experts work to written guidelines agreed with you, and complete a qualification set before labeling production data. Labels are marked by source, whether creator, AI-suggested or expert-reviewed, so you can see where a pre-label was accepted unchanged and where an expert intervened. A second expert reviews an agreed share of items, disagreements go to adjudication, and agreement is measured on overlapping items and reported with each batch.
Annotation can run on data you supply or on data we source for you. Data you send stays under your confidentiality terms and is not added to the catalogue. Supported work spans text, image, audio, video, 3D and documents: classification, extraction, segmentation, transcription, grading, critique and free-form review. Experts are paid automatically after identity and tax verification, which keeps the network stable for long and standing projects.
Built to your specification.
Every engagement starts from a written spec and a pilot batch. These are the most common requests we source for data annotation.
Domain labeling
Classification, extraction and tagging by specialists in medicine, law, finance, engineering and science.
Image and video annotation
Boxes, polygons, masks, keypoints and frame-level labels where the classes require expert recognition.
Text and document annotation
Entity, clause and span labeling, classification and rubric grading of model outputs by specialists in the field.
Pre-label review
Experts correct AI-suggested labels, with every change recorded.
Second-expert review
Independent review of an agreed share, with adjudication of disagreements.
Guideline development
Annotation guidelines and edge-case rulings drafted with your team and versioned.
01Invisible errors
A wrong label in a specialist domain looks right to a generalist reviewer. Expert review catches what spot checks miss.
02Pre-labels anchor annotators
Recording label source shows where an AI suggestion was accepted unchanged, so you can measure that effect.
03Agreement as a quality signal
Measured agreement on overlapping items shows which classes or guidelines need work.
Data annotation: asked often.
What is the difference between data annotation and data labeling?
In practice the terms are used interchangeably: both mean adding the tags, boxes, transcripts, scores or written judgments a model learns from. Some teams use labeling for simple classes and annotation for richer markup such as segmentation or critique. We do both, and the difference that matters more is who does the work: for specialist data, a domain expert rather than a general crowd.
How do you verify annotator credentials?
Experts are credential-checked when they join the network, against the qualifications relevant to their field, and complete identity and tax verification before being paid. For each project they also pass a qualification set built from your guidelines. Each label records which expert produced it, so you can trace work to a verified person.
Can you annotate data we provide?
Yes. Data you send is handled under your confidentiality terms, is visible only to the experts assigned, and is not added to the catalogue. It passes safety screening, and personal data handling is agreed in the specification. Labels are delivered in your schema with source, annotator and review status for each.
How is annotation quality measured?
Through qualification sets before production, gold items mixed into production work, a second-expert review of an agreed share, and inter-annotator agreement on overlapping items, reported per batch. Disagreements go to adjudication rather than being averaged. Acceptance criteria are written into the specification and tested on the pilot.
Do you use AI pre-labeling?
Where it helps and you approve it. AI-suggested labels are always marked as such, and expert corrections are recorded, so you can tell an expert-confirmed label from an unchanged suggestion. If your spec requires labels created without pre-labels, we run the task without them.
How is expert annotation priced?
Pricing depends on the expertise required, task complexity and time per item, review and overlap requirements, volume and turnaround. Radiologist segmentation or attorney clause review costs more than general tagging. We quote per item or per hour after reviewing samples, with volume pricing for standing projects.
How quickly can an annotation project start?
Start time depends mainly on how quickly the right experts can be qualified on your guidelines. Fields with many experts in the network can begin soon after scoping; rarer specialties take longer to staff. We run a pilot batch first so guidelines are tested before volume, and agree the schedule in the specification.
Tell us what your model needs to learn.
Send a brief in five minutes. A data lead replies within one business day with questions and a first sourcing plan.