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Butera Mugisha

Butera Mugisha

Agency
Rwanda flagKigali, Rwanda
$5.00/hrIntermediate53+

Key Skills

Software

LabelboxLabelbox
CVATCVAT
LabelImgLabelImg
SamaSama
V7 LabsV7 Labs

Top Subject Matter

No subject matter listed

Top Data Types

ImageImage
TextText
DocumentDocument

Top Task Types

Bounding Box
Polygon
Segmentation
Classification
Entity Ner Classification

Company Overview

Fortis BPO is a Rwanda-based Business Process Outsourcing company specializing in AI data annotation and training data services. We provide high-quality data labeling solutions to support machine learning and artificial intelligence development. Our team consists of trained bilingual professionals (English and French) capable of performing tasks such as image annotation, bounding box labeling, segmentation, NLP annotation, document classification, transcription, and dataset validation. With reliable infrastructure and a scalable workforce, Fortis BPO delivers accurate, secure, and cost-efficient data services for AI companies and technology organizations worldwide. Based in Kigali, Rwanda, we leverage a young, educated, and motivated workforce to support large-scale data annotation projects while maintaining strong quality control standards.

IntermediateFrenchGermanChinese Mandarin

Security

Security Overview

Fortis BPO implements strict data protection and confidentiality practices to ensure the security of client datasets. All team members sign non-disclosure agreements (NDAs) and follow internal data protection policies. Access to project data is restricted through role-based permissions, and workstations are monitored to ensure compliance with client security requirements. Data is processed in a controlled environment with secure internet access, restricted external storage, and internal quality control procedures. Our team follows best practices for handling sensitive information and maintaining the confidentiality and integrity of AI training data.

Labeling Experience

CVAT

Computer Vision Image Annotation for Object Detection

CVATImageBounding Box
This project involves image annotation for computer vision model training. Our team performs bounding box annotation to identify and label objects within image datasets used for object detection tasks. The workflow includes dataset preparation, annotation using industry-standard labeling tools, and multi-step quality control to ensure labeling accuracy and consistency. Each annotated dataset is reviewed through internal QA processes to maintain high-quality training data for machine learning models. The project demonstrates our ability to manage scalable image annotation workflows while maintaining strict quality assurance standards.

This project involves image annotation for computer vision model training. Our team performs bounding box annotation to identify and label objects within image datasets used for object detection tasks. The workflow includes dataset preparation, annotation using industry-standard labeling tools, and multi-step quality control to ensure labeling accuracy and consistency. Each annotated dataset is reviewed through internal QA processes to maintain high-quality training data for machine learning models. The project demonstrates our ability to manage scalable image annotation workflows while maintaining strict quality assurance standards.

Present