Akwayi Mpah

Key Skills

Software

Data Annotation Tech

CVAT

Other

Top Subject Matter

Image Annotation for Computer Vision

OCR and Video Content Evaluation

Legal Services & Contract Review

Top Data Types

Image

Video

Text

Audio

Document

Top Task Types

Bounding Box

Segmentation

Classification

Transcription

Freelancer Overview

Image Annotation and Quality Control. Core strengths include CVAT and Other. AI-training focus includes data types such as Image and Video and labeling workflows including Bounding Box and Transcription.

Labeling Experience

Atlas Capture

Data Annotation TechVideoSegmentationTranscription

Atlas Capture is a data collection and annotation platform focused on training high-performance AI models through structured human feedback. Projects typically involve image, video, and text annotation tasks such as object detection, bounding box labeling, segmentation, classification, and content validation. Contributors follow detailed guidelines to ensure precision, consistency, and dataset reliability. The platform emphasizes quality control through multi-level reviews and performance scoring. Atlas Capture supports AI development across industries including computer vision, automation, and digital platforms. Projects are task-based, requiring attention to detail, accuracy, and timely delivery to maintain high dataset standards for machine learning training.

Atlas Capture is a data collection and annotation platform focused on training high-performance AI models through structured human feedback. Projects typically involve image, video, and text annotation tasks such as object detection, bounding box labeling, segmentation, classification, and content validation. Contributors follow detailed guidelines to ensure precision, consistency, and dataset reliability. The platform emphasizes quality control through multi-level reviews and performance scoring. Atlas Capture supports AI development across industries including computer vision, automation, and digital platforms. Projects are task-based, requiring attention to detail, accuracy, and timely delivery to maintain high dataset standards for machine learning training.

2025 - 2025

Perle Labs

Data Annotation TechImageBounding Box

Perle Labs delivers high-quality data and image annotation projects that power AI and computer vision systems. Projects involve labeling images using bounding boxes, polygons, and keypoints, as well as text classification, entity tagging, and data validation. Annotators follow detailed guidelines, complete qualification tests, and work within structured QA workflows to maintain high accuracy standards. Tasks require precision, consistency, and strong attention to detail. The annotated datasets support applications in healthcare, retail analytics, autonomous systems, and smart surveillance. Performance is measured through accuracy scores and reviews, ensuring reliable datasets for training scalable machine learning models.

Perle Labs delivers high-quality data and image annotation projects that power AI and computer vision systems. Projects involve labeling images using bounding boxes, polygons, and keypoints, as well as text classification, entity tagging, and data validation. Annotators follow detailed guidelines, complete qualification tests, and work within structured QA workflows to maintain high accuracy standards. Tasks require precision, consistency, and strong attention to detail. The annotated datasets support applications in healthcare, retail analytics, autonomous systems, and smart surveillance. Performance is measured through accuracy scores and reviews, ensuring reliable datasets for training scalable machine learning models.

2025 - 2025

LLM Data Annotation – Voice & Audio-Visual Commands – Silencio

OtherAudioClassification

Annotated and evaluated voice-command datasets for large language model (LLM) training and validation. Assessed alignment between spoken commands and corresponding system or visual outputs. Used structured scoring frameworks to ensure dataset quality and training compliance. • Enhanced clarity and logical consistency in voice-command labels • Followed AI training quality standards and protocols • Supported robust LLM speech understanding • Improved reliability of multimodal training data

Annotated and evaluated voice-command datasets for large language model (LLM) training and validation. Assessed alignment between spoken commands and corresponding system or visual outputs. Used structured scoring frameworks to ensure dataset quality and training compliance. • Enhanced clarity and logical consistency in voice-command labels • Followed AI training quality standards and protocols • Supported robust LLM speech understanding • Improved reliability of multimodal training data

2026 - Present

AI Response Evaluation (Therapeutic Dialogue Project) – PerleLabs

OtherText

Evaluated and compared AI-generated responses for therapeutic-style conversations. Used structured criteria to assess outputs for empathy, emotional intelligence, appropriateness, and communication clarity. Generated high-quality rewrites to improve clarity, safety, and supportive tone. • Applied evaluation rubrics for consistent, guideline-aligned assessment • Focused on mental health-oriented AI guidelines • Supported research to strengthen human-aligned AI behavior • Delivered comprehensive feedback to improve AI responses

Evaluated and compared AI-generated responses for therapeutic-style conversations. Used structured criteria to assess outputs for empathy, emotional intelligence, appropriateness, and communication clarity. Generated high-quality rewrites to improve clarity, safety, and supportive tone. • Applied evaluation rubrics for consistent, guideline-aligned assessment • Focused on mental health-oriented AI guidelines • Supported research to strengthen human-aligned AI behavior • Delivered comprehensive feedback to improve AI responses

2025 - Present

Education

Work History