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D
David

David

Agency

CEO

Nigeria flagLagos, Nigeria
raterx.ai

Key Skills

Software

LabelboxLabelbox
AppenAppen
TelusTelus
TolokaToloka
OneFormaOneForma
MercorMercor
Internal/Proprietary Tooling
CVATCVAT
iMeritiMerit
MindriftMindrift
Data Annotation TechData Annotation Tech
Other

Top Subject Matter

No subject matter listed

Top Data Types

ImageImage
TextText
AudioAudio

Top Task Types

RLHFRLHF
Evaluation/RatingEvaluation/Rating
Prompt + Response Writing (SFT)Prompt + Response Writing (SFT)
Red TeamingRed Teaming
Function CallingFunction Calling

Company Overview

Rater-X is an AI workforce and evaluation company helping global AI teams improve model quality, reliability, alignment, and cultural intelligence through trained human judgment. We specialize in AI model evaluation, multilingual annotation, search and content quality review, evaluator calibration, quality assurance, and human-in-the-loop feedback operations. Our work supports AI companies that need consistent, context-aware evaluation for high-judgment tasks where accuracy, nuance, and reliability matter. Our mission is to build scalable AI talent infrastructure from Africa and other emerging regions while helping global AI companies solve challenges around model performance, localization, policy alignment, and evaluation consistency. Rater-X operates through a distributed remote workforce model supported by structured training, project-specific guidelines, continuous calibration, and quality control systems. Our evaluators are screened, trained, and assigned based on task complexity, with NDA-bound participation, controlled access, and project-isolated workflows to protect client data and maintain confidentiality. Our team has hands-on experience across AI content analysis, search relevance evaluation, annotation programs, multilingual AI operations, and human feedback workflows. Rater-X is currently strengthening its evaluation infrastructure and positioning itself as a trusted global partner for AI model evaluation, data quality, and human-in-the-loop AI operations.

Security

Security Overview

Rater-X operates as a distributed remote AI training and evaluation workforce with a strong focus on confidentiality, controlled access, and responsible data handling. While our contributors may work remotely, project access is not open or casual. Team members are onboarded per project, and access is restricted to authorized contributors based on role, task need, and project requirements. Because client projects may involve proprietary guidelines, datasets, model outputs, evaluation rubrics, or sensitive workflow information, Rater-X uses permission-based systems, secured communication channels, controlled file access, and project-specific onboarding procedures. Sensitive materials are shared only through approved work channels and controlled environments. Contributors are vetted before participation and are expected to follow strict confidentiality requirements, including NDA-bound engagement where applicable. We also use project-isolated workflows, role-based access, structured quality review layers, reviewer calibration, and operational oversight to reduce unauthorized exposure and maintain consistency across AI evaluation and annotation projects. Higher-risk or policy-sensitive tasks are assigned to trained contributors and reviewed through QA and calibration processes. As we scale, Rater-X is actively strengthening its operational, privacy, and security infrastructure to align with global AI data service standards and industry best practices.

Labeling Experience

Audio Query Evaluation and Answer Relevance Review

Internal/Proprietary ToolingAudioAudioQuestion AnsweringQuestion AnsweringEvaluation/RatingEvaluation/Rating

Rater-X supported audio-based AI evaluation workflows involving human audio queries, AI-generated responses, and search-generated answers. The project focused on assessing whether responses accurately addressed the user’s spoken query, matched user intent, and provided relevant, complete, and reliable information. Evaluators reviewed audio segments, categorized queries as fact-check or non-fact-check, and assessed answer quality, factual accuracy, usefulness, and guideline alignment. Where required, contributors conducted additional web research to verify claims, identify unsupported information, and ensure accurate evaluation decisions. The projects span across question-answering review, evaluation rating, RLHF-style human feedback, and audio segmentation support. Quality measures included evaluator onboarding, guideline review, calibration, structured QA checks, consistency monitoring, and escalation of unclear or borderline cases.

2025 - 2025

LLM Response Evaluation and Human Feedback Review

Internal/Proprietary ToolingTextTextEvaluation/RatingEvaluation/RatingRLHFRLHF

Rater-X supported text-based AI evaluation workflows involving LLM response review, output rating, instruction-following assessment, preference judgment, and human feedback operations. The project focused on evaluating AI-generated responses for accuracy, relevance, completeness, safety, tone, clarity, factual consistency, and alignment with project-specific guidelines. Quality measures included evaluator onboarding, guideline review, reviewer calibration, structured QA checks, consistency monitoring, and escalation of unclear, borderline, or policy-sensitive cases. The work required careful judgment, attention to detail, and consistent application of evaluation rubrics across AI-generated content.

2024 - 2025