What We Offer

Services for AI Data Annotation

Zenetra offers end-to-end data annotation services designed to scale with your AI initiatives. We tailor labeling workflows to your data types, model requirements, and security needs.

Text Annotation and Labeling

Accurate labeling of documents, transcripts, sentiment, entities, and metadata with customizable taxonomies; includes quality assurance and dataset versioning.

Image Annotation and Computer Vision Labeling

Bounding boxes, polygons, semantic segmentation, and image-level labels with strict QA, enabling reliable training for object detection and vision tasks.

Audio Transcription and Labeling

High-precision transcription, speaker labeling, punctuation, and metadata tagging for ASR, voice analytics, and multimedia applications.

Data Curation and Quality Assurance

Data cleaning, deduplication, normalization, and rigorous QA workflows to improve dataset reliability and model performance.

Annotation for Model Evaluation and Benchmarking

Create gold-standard evaluation sets, blinded labeling, inter-annotator agreement metrics, and robust baselines for model assessment.

Secure Data Handling and Compliance

End-to-end data security, encryption, access controls, and compliance with privacy standards (GDPR, HIPAA where applicable) to protect sensitive information.

Frequently Asked Questions

Answers to common questions about Zenetra’s data annotation services, security, and workflows.

What types of data can Zenetra annotate?

We annotate text, images, and audio, including transcription, bounding boxes, segmentation, and metadata tagging.

We enforce encryption in transit and at rest, strict access controls, NDA agreements, and compliance with data privacy standards.

We serve technology, healthcare, retail, autonomous systems, finance, and media—tailoring labeling workflows to domain-specific needs.

Turnaround depends on data volume and complexity, but we offer scalable SLAs, pilot projects, and iterative labeling with rapid QA feedback.

Yes. We can start with a pilot to validate labeling quality, tooling compatibility, and integration with your ML pipeline.