Your Gateway to Skilled AI Data Specialists

Your trusted team for accurate data labelling and responsible AI.

Featured Data & Annotation Experts

Meet Top-Rated Annotation, RLHF, and Data Audit Professionals

Sandra Lu

Toronto, Canada | 7+ Years 

Experience

$85/hr

Curated high-signal preference datasets for LLM alignment at enterprise scale

Neeraj Patel

Bangalore, India | 10+ Years 

Experience

$95/hr

Conducted full-scale audits of training datasets for copyright, bias, and source transparency

Chloe Martinez

Austin, USA | 6+ Years 

Experience

$75/hr

Led QA across 25+ annotation projects spanning healthcare, finance, and automotive domains

Top Featured Data & Annotation Projects

Training Data Audit for Open-Domain LLM 

Category: Data Provenance & Compliance

A U.S.-based AI lab hired a Provenance Auditor to assess dataset sources and reduce copyright risk ahead of regulatory audits. 

Budget: $18,000

Duration: 6 weeks

Remote (USA) 

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RLHF Workflow for Chatbot Behavior Tuning 

Category: RLHF Curation

 

A conversational AI startup engaged an RLHF Curator to collect and rank human preference data to refine chatbot tone and helpfulness. 

Budget: $25,000

Duration: 2 months

Remote (UK) 

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Annotation QA for Retail Object Detection Model 

Category: Annotation Quality & Computer Vision

A computer vision platform brought in an Annotation Lead to restructure QA workflows, cutting error rates by 37%.
 

Budget: $15,000

Duration: 1.5 months

Berlin, Germany 

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You’ve Got Questions, We’ve Got Answers

Our provenance auditors specialize in identifying risky or unverifiable data and can help you build transparent, compliant datasets.
Yes —we have curators who design feedback pipelines and manage high-volume, high-variance data for human-in-the-loop alignment.
Absolutely. Our experts are tool-agnostic and familiar with platforms like Labelbox, CVAT, SuperAnnotate, and more.
Yes —many Annotations Quality Leads and CV Leads have experience managing internal or outsourced labelling teams.

Your AI starts with quality data.

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