GUIDE · FEATURED
Planning a multilingual data collection project
Key considerations for language, recruitment, quality and delivery when your project spans regions, dialects and participant profiles.
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RESOURCES
Practical insights for AI data teams.
Guides, project insights and clear explanations around human data, multilingual operations and AI evaluation.
FEATURED READING
Useful thinking for teams building AI with people.
Start with the fundamentals, then go deeper into language data, recruitment, quality and evaluation.
GUIDE
What makes a strong AI data workflow?
From requirements and screening to QA and final delivery, understand the pieces that make an operation repeatable.
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INSIGHT
Working with regional Indian language data
Why local context, dialect and participant selection matter when building human language datasets.
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EXPLORE BY TOPIC
From collection to evaluation.
Use these themes to find the part of the AI data lifecycle most relevant to your current project.
Data Collection
Participant recruitment, modality planning and field operations.
Language & Localization
Regional language, dialect, translation and localization considerations.
Quality Assurance
Screening, validation, sampling and layered review.
AI Evaluation
Human judgments, ranking, relevance and evaluation workflows.
Operations
Project management, mobilization and delivery processes.
Emerging AI
New modalities, products and data requirements.
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