Scalable Multimodal Data Labeling for Advanced GenAI Training
Creating 48,000 complex visual prompts across 7 scientific disciplines
Project Overview
The client aimed to develop robust, contextually aware Generative AI models capable of sophisticated reasoning and accurate visual comprehension across multiple scientific disciplines.
This case study demonstrates how Tbrain created 48,000 complex visual prompts tailored for advanced undergraduate-level understanding in Chemistry, Biology, Medical Sciences, Mathematics, Physics, Engineering, and Economics.
The Challenge
1Recruiting Specialized Workforce
Initial team of only 50 Makers and 5 QCs was insufficient. Required rapid scaling to 600 Makers and 20 QCs with postgraduate-level expertise.
2Complex Task Requirements
Each visual prompt required meeting 8 strict criteria, demanding multi-step conceptual reasoning that challenged learners to interpret visual content and apply abstract concepts.
3Maintaining Quality at Scale
Required sophisticated multi-stage review process: domain experts (Rv1), language verification (Rv2), and final QC - all while preventing bottlenecks and quality cascades.
Tbrain's Strategic!
Multi-Layer Quality Workflow
Talent Recruitment
Scaled from 50 to 600 Makers through academic partnerships with top-tier universities and AI research labs
Real-time Dashboard
Looker Data Studio for live progress tracking, bottleneck identification, and performance analytics
Quality Assurance
Multi-tiered review with performance-based management - underperformers retrained or removed
Outstanding Results
Before Optimization
After Optimization
Final Deliverables
- 35,401 prompts delivered and final-approved with high academic and linguistic precision
- 12x growth in team size without compromising quality standards
- Dramatic reduction in bottlenecks through real-time dashboards and daily sync-ups
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