Our AI data annotation service labels your data with trained reviewers and checks every batch for quality, so your labels are consistent instead of a gamble.
We label and QA your data. We do not train models on it.
AI data annotation is the work of labeling raw data so models can learn from it or be tested against it: categories, text spans, scores, rankings, or bounding boxes. It matters because model quality follows label quality; bad labels teach bad behavior, and bad test labels hide it. Good annotation means clear guidelines, trained labelers, and quality checks on every batch, not just the first one. When the job is grading model outputs rather than labeling raw data, that is our human evaluation service.
Most annotation problems are process problems, not people problems. We fix the process.
Unclear guidelines mean every labeler invents their own standard, and your dataset becomes three datasets in a trench coat. We write guidelines with worked examples, calibrate labelers on them, and measure agreement before production labeling starts.
Many labeling pipelines have no quality step at all: labels go straight from the labeler into the dataset. We QA every batch with spot checks and agreement scores, and the QA numbers ship with the data.
Every dataset has edge cases the guidelines never imagined. Instead of letting labelers guess, we document the edge cases, resolve them with you, and update the guidelines so the next batch is cleaner than the last.
Annotation queues that take weeks stall the teams waiting on them. Pilot batches come back in days, and production timelines are agreed up front with the QA steps included, not added as an afterthought.
You send the raw data and tell us what labels you need, or we help you define the label schema.
Trained labelers annotate every item against your guidelines, with QA checks on each batch.
Labeled data in your format, with QA stats, guideline notes, and a walkthrough call.
Training data, test sets, and evaluation benchmarks are all built on labels. When the labels are wrong, everything downstream inherits the error: models learn the wrong thing, test sets measure the wrong thing, and teams make decisions on bad evidence.
Treating annotation as infrastructure means giving it infrastructure-grade process: defined schemas, trained labelers, and quality checks that run on every batch. That is the difference between a dataset you can build on and one you have to redo.
If two trained labelers cannot agree on the labels, the labels are not ready, no matter how many items are done. Inter-labeler agreement is the honest signal of annotation quality, and it should ship with the data, not live in someone's head.
We measure agreement on overlapping items in every batch and include the numbers with the delivery. When agreement dips, we find out why: usually a guideline gap or an edge case cluster, both fixable, both worth knowing about.
The easy items label themselves. The value of a professional annotation process is entirely in the hard ten percent: the ambiguous cases, the boundary examples, the inputs the guideline never imagined. How those get resolved determines the dataset's quality.
We log every edge case, resolve it against the guideline or escalate it to you, and update the guideline so the next batch is cleaner. Over a full project, that log becomes one of the most valuable artifacts you own.
Text classification, span and entity labeling, quality scores, rankings and pairwise preferences, and similar text-focused label types. If your project is mostly text, we can likely handle it; ask us about your specific schema.
Every batch gets spot checks by a senior reviewer plus inter-labeler agreement measurement on overlapping items. Batches that miss the agreed quality bar get reworked, not shipped. The QA numbers come with the data.
Pilot batches first, so we can prove the guideline and the QA numbers on your real data. Production volume gets scoped after the pilot, with timelines agreed up front.
We draft them with you. You bring the domain knowledge of what the labels mean; we bring the structure that makes guidelines labelable: definitions, worked examples, and edge case rules. You approve the final version.
Your data stays inside the engagement. We do not train models on client data, we do not reuse it for other clients, and we work under NDA when you need one. Put it in writing with us before you send anything sensitive.
Yes, and test sets deserve the strictest QA of all, since every model decision gets measured against them. Tell us it is a test set and we apply tighter agreement bars and document every judgment call.
Annotation labels raw data so models can learn from it or be tested against it. Evaluation grades what a model produced against a rubric. They are complementary: you annotate to build datasets, you evaluate to judge outputs. Our comparison page walks through when you need which.
Send a sample of your data and get a labeled pilot batch with QA stats.
Not sure where to start? Talk to us and we will point you at the right evaluation.