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Research participant marketplace that gives AI teams and academics fast access to verified, screened human data and feedback.
Outlier is a platform where experts earn freelance income training and evaluating AI by writing prompts, rubrics and rating answers.
Enterprise data-annotation and evaluation platform pairing a labeling tool with a managed expert annotator workforce.
Open-source data-labeling and AI-evaluation platform for image, text, audio, video and LLM workflows, with a paid enterprise tier.
No public pricing
No public pricing
No public pricing
Free trial available
- ✦300,000+ verified, screened participants
- ✦300+ audience targeting filters
- ✦Representative and quota-based sampling
- ✦API and no-code survey tool integrations
- ✦AI-powered participant quality monitoring (Protocol)
- ✦Managed services with dedicated project teams
- ✦Access to vetted domain experts
- ✦Remote AI-training gig work
- ✦Tasks: prompt writing, rubrics, rating answers
- ✦Flexible hours, work from anywhere
- ✦Weekly, quality-based pay
- ✦Roles across coding, STEM and languages
- ✦Free access to paid AI models
- ✦Customizable multimodal annotation editors for image, video, text and audio
- ✦Support for RLHF preference data, SFT datasets, RAG and agent evaluation workflows
- ✦Managed expert annotator workforce option
- ✦Data curation, exploration and analytics tools
- ✦Team and project management with SSO on higher tiers
- ✦Integrations with AWS, GCP, Databricks, Snowflake and others
- ✦Open-source multi-type labeling
- ✦Programmable, customizable interfaces
- ✦API, SDK and webhooks
- ✦ML backend for pre-labeling and active learning
- ✦LLM evaluation and RLHF workflows
- ✦Enterprise QA, SSO and analytics
- →Collecting human preference data for RLHF or model evaluation
- →Running academic behavioral or market research studies
- →Sourcing domain-expert data for specialized AI benchmarks
- →Earning side income as an AI trainer
- →Contributing expertise to improve AI
- →Flexible remote work for grads and experts
- →Gaining hands-on AI and prompt experience
- →Building large-scale labeled datasets to train computer vision or NLP models
- →Running human evaluation and RLHF pipelines for LLM fine-tuning
- →Auditing and scoring AI agent decisions with human review
- →Labeling training data across modalities
- →Human-in-the-loop AI evaluation
- →RLHF and fine-tuning data collection
- →RAG and LLM benchmarking