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Agency
Barbados flagChrist Church, Barbados
mcqagency.com

Key Skills

Software

LabelboxLabelbox
LabelImgLabelImg
RoboflowRoboflow

Top Subject Matter

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Top Data Types

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TextText
VideoVideo

Top Task Types

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Company Overview

is a digitally native media, research, and AI-enabled content operations company focused on contextual analysis, structured data generation, and human-guided annotation workflows for modern AI systems. Originally launched as a premium editorial platform covering business, technology, hospitality, and culture, TBT has evolved into a broader intelligence and content infrastructure company that combines human editorial expertise with AI-assisted workflows. We specialize in long-context reasoning, conversational evaluation, tone and sentiment analysis, prompt-response ranking, structured labeling, synthetic data generation, and culturally aware content review across industries including business, consumer brands, hospitality, technology, and media. Our unique strength comes from blending editorial storytelling instincts with structured annotation processes, allowing us to generate nuanced, high-quality outputs that capture tone, context, emotional framing, and real-world communication patterns. Operating through a remote-first global workflow, TBT leverages AI-assisted research systems, editorial QA pipelines, and scalable digital collaboration methods to support evolving AI training and evaluation environments while prioritizing accuracy, consistency, professionalism, and contextual integrity.

Security

Security Overview

We maintain a remote-first operational structure with an emphasis on confidentiality, controlled access, and secure digital collaboration practices. Our security approach includes role-based workflow organization, restricted access to sensitive project materials, secure cloud-based communication and file management systems, password-protected operational environments, and structured editorial QA processes designed to reduce data handling risks and maintain content integrity. We prioritize confidentiality agreements where required, maintain internal review procedures for sensitive datasets and client information, and use compartmentalized workflows to limit unnecessary exposure to proprietary or private materials. Our operational model is designed to support professionalism, data sensitivity awareness, and secure handling practices across AI-assisted research, annotation, editorial, and content evaluation workflows.

Labeling Experience

Labelbox

Strategic Content & Narrative Systems Analyst

LabelboxLabelboxVideoVideoText GenerationText GenerationObject DetectionObject Detection

The project focused on delivering high-context AI annotation and conversational evaluation services designed to support modern generative AI systems and large language model training workflows. The work involved human-in-the-loop annotation, prompt-response evaluation, tone and sentiment classification, conversational quality assessment, long-context summarization, synthetic dialogue generation, narrative consistency review, and culturally aware content analysis across industries including business, technology, hospitality, consumer brands, media, and digital culture. The project emphasized editorial-level reasoning and contextual intelligence rather than basic data labeling, with workflows designed to improve model alignment, response quality, human preference understanding, and natural language consistency. Operating through a remote-first team structure, the project utilized AI-assisted research systems, structured QA pipelines, multi-stage review processes, and scalable annotation workflows to maintain high standards of accuracy, contextual integrity, and evaluation consistency across conversational and content-focused AI datasets.

2025 - Present
Labelbox

AI Annotation & Editorial Intelligence Specialist

LabelboxLabelboxImageImageBounding BoxBounding BoxObject DetectionObject Detection

The project scope focused on high-context AI annotation, conversational evaluation, and editorial intelligence workflows designed to support modern generative AI systems. Tasks included structured data labeling, prompt-response evaluation, conversational quality assessment, tone and sentiment classification, long-context summarization, synthetic dialogue generation, narrative consistency review, and culturally aware content evaluation across domains including business, technology, hospitality, media, consumer brands, and digital culture. The project operated through a remote-first team structure with scalable editorial and QA workflows capable of handling large conversational datasets and multi-turn reasoning tasks. Quality measures included multi-layer human review, editorial QA validation, consistency checks for tone and context alignment, structured evaluation rubrics, role-based workflow organization, and ongoing refinement processes designed to improve annotation accuracy, contextual integrity, and output reliability across AI training and evaluation environments.

2025 - Present