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Owerri, Nigeria
CVAT
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Label StudioNo subject matter listed
Audio
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TextAtelis Technologies is a campus-based technology company and innovation hub focused on training, mentoring, and deploying young tech talents for real-world digital work. We operate dedicated hub spaces where students and early-career professionals receive hands-on training, structured supervision, and access to paid digital work opportunities. Our model combines workforce development with quality delivery, allowing organizations to access well-managed, scalable teams while empowering students with income, experience, and career growth.
Atelis Technologies Ltd. operates from a supervised, physical hub environment with controlled access to workstations. Our facilities are monitored to ensure only authorized personnel can access active projects and devices. From a cybersecurity perspective, all workstations operate on secured networks with firewall protection, updated antivirus software, and access controls. Project data is accessed strictly on a need-to-know basis, and external storage or unauthorized data transfer is restricted. All annotators and supervisors are required to sign non-disclosure agreements (NDAs) and receive onboarding training on data privacy, confidentiality, and responsible data handling practices. Supervisors oversee daily operations and perform quality and compliance checks on ongoing tasks. We maintain internal operational guidelines to ensure adherence to client requirements and periodically review workflows to improve data security, quality assurance, and compliance with applicable data protection standards.
Performed English-language audio transcription and labeling tasks, focusing on transcription accuracy, timestamp alignment, and quality validation under centralized supervision.
Labeled and classified text datasets for natural language processing tasks, including entity recognition and content categorization. Annotators followed defined guidelines with supervisor-led quality assurance reviews to ensure consistency.
Annotated image datasets for object detection and classification tasks using bounding boxes. Work was conducted in a supervised hub environment with structured quality checks and review workflows to ensure labeling accuracy and consistency.