Reliabl
Reliabl provides an integrated annotation platform that turns organizational and community knowledge into training-ready datasets with context-specific labels. The platform offers custom ontology and taxonomy builders, collaborative annotation tools, and in-platform review workflows that measure label consensus rather than assuming it. Teams can invite domain experts, annotators, and reviewers to work together in one shared project, with label consensus visibility built in.
- Artificial Intelligence
- Software Only
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
Generic data labeling tools use predefined labels that fail to capture domain-specific nuance, cultural context, and subjective interpretation, leading to inaccurate AI models. Teams lack an integrated workflow to structure their organizational and community knowledge into high-quality training data that reflects how people in their domain actually speak, express, and interact.
Solution
Reliabl provides an integrated annotation platform that turns organizational and community knowledge into training-ready datasets with context-specific labels. The platform offers custom ontology and taxonomy builders, collaborative annotation tools, and in-platform review workflows that measure label consensus rather than assuming it. Teams can invite domain experts, annotators, and reviewers to work together in one shared project, with label consensus visibility built in. Reliabl also supports fair compensation for external annotators and facilitators to help source experts and community members for AI training needs. The platform streamlines the entire annotation workflow—from data collection to preparation for model training—through one integrated, research-backed system, exporting structured, pipeline-ready datasets using the team's own schema with no cleanup required.
Target Audience
Primary users are AI teams at organizations building complex, culturally inclusive AI systems—including machine learning engineers, data scientists, product managers, and domain experts—who need research-grade, contextually accurate training data.
Features
- Custom ontology and taxonomy builder that lets teams build or upload annotation schemas reflecting domain-specific language and cultural nuance
- Collaborative annotation workspace where team members, customers, and domain experts annotate together with label consensus visibility and discussion threads
- Reviewer workflows with label consensus tracking to ensure quality before export
- Export of structured ML training data and human AI evaluation datasets using the team's schema with no cleanup required
- Facilitator support from Reliabl's team for feedback sessions with domain experts
- Expert and community sourcing with fair compensation for workers, researchers, and customers