Data annotation services your models can learn from Agents label the volume, and a trained person checks every edge case

A supervised model learns only what its labels teach it. If two annotators box the same car differently, the model learns that both are right, and its predictions drift.

Annotated data is the ground truth your team measures the model against. It decides whether search results are relevant, a voice assistant hears the right word, or a claims model reads the right field.

We pick the technique from what the model has to do, not from what the tool offers. A detector needs boxes, a driving model needs exact edges, and a support bot needs intents and entities.

• Bounding boxes to locate objects
• Semantic segmentation to classify every pixel
• Polygons for irregular shapes such as trees and signs
• Named entity recognition for names, places, and dates in text
• Sentiment tags for opinions in reviews and chat
• Audio transcription with speaker and sound tags

Agents now draw most first-pass labels well, and they're fast. They still miss the item that sits outside the guide, such as a half-hidden pedestrian, a sarcastic review, or a form with two names on it.

So every item an agent is unsure of goes to a trained reviewer, who decides it and writes down the reason. One named batch owner on our team checks the audit sample and answers for the batch's accuracy.

We annotate data for teams building models in automotive and robotics, retail and ecommerce, healthcare, finance, insurance, legal work, and GIS mapping. Each has its own edge cases, so each guide is written with your domain experts.

• Traffic signals, lanes, and pedestrians for driver assistance
• Products and attributes for retail search and recommendations
• Fields in claims and policies for insurance models
• Clauses and parties in contracts for legal review
• Transactions tagged for fraud and credit risk models
• Roads, buildings, and land use in aerial images for GIS maps

An in-house annotation team means hiring, training, and managing people for work that rises and falls with each model release. Outsourcing turns that into a price per label and a delivery date.

You keep control through the guide and the audit. Your engineers spend their week on the model, and the labeled data arrives in the format your pipeline reads, such as COCO JSON, YOLO, or CSV.

We agree with you where the data is stored, who may see it, and when it's deleted. Access is limited to the people working on your batch, and you can ask for the access log at any time.

It depends on the volume, the technique, and how many edge cases your data has. We agree the date for a pilot batch before we start, and a weekly volume once you've checked the pilot.

Yes. We write a guide for your domain with your experts, and the reviewers who decide edge cases are trained on it. Insurance claims, retail catalogs, and driving scenes each get their own guide.

Yes. Agents carry most of the volume, so a larger project mostly adds reviewers for the items they flag. We split the work into batches, and each batch has one owner who answers for it.

We price per label, per item, or per hour for expert review, depending on the technique. The price comes from your pilot batch, so it reflects how hard your data really is.

The terms overlap. Labeling usually means giving an item a class or a tag, and annotation also covers drawing boxes, masks, and points or marking spans of text. We do both, and our data labeling page covers classification work in more depth.