Data Annotation Services

Project Scalability

Employing a data annotation company can help you scale any AI or ML project you have, expanding it and advancing the power of your solution as your business grows. Annotation volume rarely stays the same through an AI or ML project. A pilot might need a few thousand labeled images, and production can need hundreds of thousands, along with fresh data every month to retrain the model as real-world inputs change. Hiring and training in-house annotators for each spike takes months, and the team sits idle between them. A data annotation company already has trained annotators, so it can add people when volume grows and scale back when it drops. At Alltegrio, we start with a pilot batch to agree on labeling guidelines and quality targets, then apply the same guidelines as volume grows, so the labels in your ten-thousandth batch match those in the first.

Data Security

Depending on your industry and the specifics of the data used, outsourcing annotation and labelling to a trustworthy company can help protect sensitive information and reduce risks. Healthcare records, insurance claims and financial documents often contain personal data that can't leave a controlled environment. Before sending these datasets to annotators, Alltegrio can anonymize or pseudonymize them, removing names, IDs and faces so annotators see only what the task requires. Every annotator signs an NDA, access is limited to the specific project, and files are transferred over encrypted channels. With an office in Warsaw, we can handle annotation and labeling under GDPR requirements for EU data. Outsourcing to a vendor with these controls in place reduces your legal and data breach risks compared to sharing datasets with freelance labelers.

Quality Assurance

The data quality used by AI and ML models is the most important factor in their training. Ensuring the data is clean, clear, safe, and in a uniform format will help you achieve the best results. Mislabeled examples, duplicates and inconsistent formats teach AI and ML models the wrong patterns, and no amount of tuning fixes that later. Before annotation starts, Alltegrio removes duplicate and corrupted files, strips personal data such as names and faces where required, and converts everything into a single format. During labeling, we check each batch against a gold-standard set and send a share of items to two annotators independently, so disagreements get caught and resolved. We also check the balance between classes, so rare cases aren't underrepresented in the training set. The finished dataset is delivered in the format your pipeline expects, such as COCO, YOLO, JSON or CSV.

Expanded Possibilities

Alltegrio data labeling and annotation services can help you grow by enhancing your AI model to learn from valuable data that can open opportunities in new markets or attract new audiences. When you outsource data annotation services, your model can learn from data your team doesn't have the time or expertise to label, such as new languages, dialects, product categories or medical imaging. Alltegrio's AI data annotation services include custom taxonomies for these domains, so you can extend an existing model to new markets and audiences instead of building one from scratch. Our data labeling and annotation services also cover mixed data types, which lets you add image, audio or video features to a product that started with text alone.