Data annotation services by people who hold the credential.
Some labels can be judged by anyone. The ones that decide whether your model is safe to ship cannot: a finding on a scan, a clause that shifts liability, a proof step, a line of code that leaks a secret. eeko's data annotation services route that work to credential-checked domain experts, matched by field and seniority, with a second expert checking the work and the annotator, source, and review outcome recorded on every label.
What are data annotation services?
Data annotation services turn raw material into training data by attaching the structure a model learns from: a class for each image, a box around each object, a tag on each clause, a transcript for each recording, a score or a written critique for each model answer. A provider brings the people, the tooling, the written guidelines, and the quality process, and hands back labels in your schema. The phrase covers everything from a crowd clicking on traffic lights to a radiologist outlining a lesion.
That range is the problem. A general labeling workforce is fine for tasks anyone can judge, and most annotation vendors are built for exactly that. Specialist tasks fail quietly in the same pipeline: a wrong label on a radiograph or a mis-tagged indemnity clause looks perfectly reasonable to a generalist reviewer, passes the spot check, and compounds through training until the model confidently reproduces the error.
eeko's data practice exists for the specialist end of that range. Annotation tasks go to experts whose credentials were checked when they joined the eeko expert network, routed by specialty and level of experience, working to guidelines agreed with you. A second expert reviews an agreed share, disagreements are adjudicated rather than averaged, and every label records who made it, whether it began as an AI suggestion, and what the reviewer decided. It is the same standard we hold ourselves to when we build and evaluate enterprise AI agents for regulated clients, and it sits inside the wider eeko AI training data practice, alongside the data we source and verify ourselves.
Expert data annotation and labeling services, across every modality.
Eight services, one bench of experts, one quality process. Each is scoped in a written specification and proven on a pilot batch before volume.
- Matched by specialty and seniority
- Qualification set per project
- Annotator recorded on every label
- 2D, 3D, and temporal annotation
- Radiology and pathology by clinicians
- Pixel-level masks with class hierarchies
- Legal, financial, and clinical text
- Layout-aware document labels
- Multilingual where staffed
- Verbatim and clean transcripts
- Diarization and event labels
- Dialect and accent metadata
- Rubrics drafted with your team
- Written rationale per judgment
- Code, math, and clinical reasoning
- Suggestions marked as AI-sourced
- Corrections logged per field
- Optional no-pre-label mode
- Review share set per project
- Adjudication, not averaging
- Agreement reported per batch
- Written with domain experts
- Versioned rulings log
- Version recorded on each label
Domain expert labeling, by a bench you can audit.
The difference between a label and an expert label is the person. Here is how we know who that person is.
How an expert reaches your project
- Credential check — qualifications relevant to the field are verified when an expert joins the eeko expert network, not self-declared on a profile.
- Identity and tax verification — completed before any payment, which also means the person labeling is the person we checked.
- Matching — tasks are routed by specialty and seniority: a board-certified radiologist for chest CT, a transactional attorney for indemnities, a CPA for reconciliations.
- Project qualification — before production, each expert clears a qualification round drawn from your guidelines, and the pass is recorded.
- Paid on acceptance — experts are paid automatically when work is accepted, which keeps a stable bench available for standing projects rather than a churn of new faces.
The bench includes radiologists, physicians, attorneys, accountants, engineers, mathematicians, chemists, and linguists. Browse by field on the expertise pages, or send us a task and we will name who would do it.
What an annotation batch looks like with the record switched on.
An illustrative batch of chest CT studies for a segmentation model, run on the buyer's own data under confidentiality.
Annotation quality as numbers, not adjectives.
"High quality" is a claim. Agreement per class, gold-item scores, and review outcomes per batch are measurements. We deliver the measurements.
The four controls on every project
- Qualification sets — built from your guidelines and passed by each expert before production. A failed set means no production items for that person until they re-qualify.
- Gold items — items with known answers mixed into production work, scored continuously, so a drifting annotator is caught in hours rather than at delivery.
- Second-expert review — an independent expert reviews an agreed share. Disagreements go to adjudication by a senior expert, and the ruling is recorded against both labels.
- Inter-annotator agreement — overlapping items are labeled by more than one expert so agreement can be calculated per class and per batch. Low agreement usually means the guideline is ambiguous, and we fix the guideline.
Acceptance criteria for all four go into the specification and get tested on the pilot batch, so "accepted" has a definition before the first production item is labeled.
Annotation on your data, or on data we source for you.
Most projects label material the buyer already holds. Where you have no data yet, we source it under license and label it in the same pipeline.
General labeling workforces versus expert annotation.
Anonymous accounts work through a queue. A generalist QA team samples a few percent, finds nothing wrong because nothing looks wrong, and signs off. The errors that matter are the ones no one on the team was qualified to see.
- Unknown annotators No credential, no name on the label
- Invisible errors Specialist mistakes pass generalist review
- Averaged disagreements Majority vote hides the hard cases
- Hidden pre-labels No way to tell confirmed from accepted
Credential-checked experts in the field, a qualification set per project, a second expert on an agreed share, adjudication of every disagreement, and agreement figures per class in every batch report. The label and its history arrive together.
- Named experts Credential and identity verified
- Expert review Peers who recognize a wrong answer
- Adjudicated Hard cases ruled on and written into the guide
- Pre-labels marked Acceptance rate measured and reported
From a sample of your data to a standing project.
Fixed scope and fixed price per batch, agreed after the pilot. Standing projects keep the same qualified bench and the same terms.
Data annotation services questions.
What are data annotation services?
Data annotation services add the structure a model learns from to raw data: class labels, bounding boxes and masks, entity and clause tags, transcripts, scores, rankings, and written critiques. A provider supplies the people, the tooling, the guidelines, and the quality process, and returns labels in your schema. eeko's version differs in who does the work and what comes back with it. Labels are produced by credential-checked domain experts, reviewed by a second expert, and delivered with the annotator, the source, and the review outcome recorded on every one.
Is data annotation the same thing as data labeling?
Mostly vocabulary. Both describe attaching the information a model trains on, and most teams use the words interchangeably. When a distinction is drawn, labeling usually means assigning a class or a value, and annotation covers richer work such as segmentation, span tagging, transcription, or a written critique of a model's answer. We do both. The question that changes the result is not which word you use but whether the person doing it would recognize a wrong answer, which for specialist data means a domain expert rather than an anonymous crowd.
How do you verify who is doing the annotation?
Experts are credential-checked against the qualifications of their field when they join the eeko expert network, and they complete identity and tax verification before they can be paid. For each project they clear a qualification round drawn from your guidelines before touching production items. Every label records which verified person produced it and who reviewed it, so any item in the delivery can be traced back to a named, checked individual rather than an anonymous account.
Can you annotate data we send you?
Yes, and most annotation projects run on the buyer's own data. Material you send is held under your confidentiality agreement, seen only by the experts assigned to the project, and never enters the eeko catalogue. It goes through safety screening, and the handling of any personal data is agreed in the specification before work starts. Labels come back in your schema with the source, annotator, and review status recorded per item, and you keep both the data and the labels.
How is annotation quality measured?
Four ways, all reported per batch. A qualification set before production confirms each expert can apply your guidelines. Gold items with known answers are mixed into production work. A second expert independently reviews an agreed share of items, and disagreements are adjudicated rather than averaged away. Overlapping items labeled by more than one expert give an inter-annotator agreement figure per class, which shows where the guidelines need work. Acceptance criteria go into the specification and are proven on the pilot batch.
Do you use AI pre-labeling?
Only when it helps and you approve it. Where pre-labels are used, each one is marked as AI-suggested, and every expert correction is recorded, so you can distinguish an expert-confirmed label from a suggestion that was accepted without change and measure how often that happens. If your specification calls for labels made without any pre-labeling, we run the task that way. The choice is yours and is written into the specification.
How much do data annotation services cost?
Price is set by the expertise required, the complexity and time per item, the share of items sent for second-expert review, the overlap needed for agreement measurement, volume, and turnaround. A radiologist segmenting a scan or an attorney tagging clauses costs more per item than general tagging. Quotes are per item or per expert hour, issued after we review a sample of your data, offer volume pricing for standing projects, and price the pilot batch on the same terms so there is no surprise at scale.
How fast can an annotation project start?
The schedule is set by how fast we can qualify the right experts on your guidelines, and fields with a deep bench in the eeko network can start shortly after the specification is agreed; rarer specialties take longer to staff, and we say so up front. Every project begins with a pilot batch so the guidelines are tested on real items before volume, and the production schedule is written into the specification once the pilot is accepted. A data lead replies to any brief inside one business day.
Related data solutions and expert fields.
Send us the labels your current vendor keeps getting wrong.
A sample of the data, the schema, and the volume. A data lead replies inside one business day with the experts who would do it, the review plan, and a pilot price. No deck. A named bench and a number.
Experienced within
Markets served.
As an enterprise AI agency, eeko systems delivers production AI systems remote-first across the United States and internationally — including these markets: