Research

Data quality makes
all the difference.

We're driven by the conviction that model performance is fundamentally bounded by training data quality. Through expert collaboration, rigorous curation methodologies, and deep domain expertise, we research datasets that power tomorrow's models.

Paper 1: FinanceQA

Research Paper 1 - Botanical Illustration

Paper 2: VADER

Research Paper 2 - Botanical Illustration

Core Research Areas

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AI Safety and Security

Our research focuses on developing novel approaches to AI training that help models understand and respect human values without sacrificing capability.

2

÷Multimodal Learning

We advance AI's ability to understand and reason across visual, audio, and textual modalities simultaneously.

3

>Computer Use & Automation

We've created training data that teaches AI agents to understand context, anticipate user needs, and execute complex multi-step workflows across diverse software environments.

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Data Quality & Curation

We've developed rigorous methodologies for identifying, filtering, and enhancing training data quality to drive superior model performance.

5

Model Evaluation

Our evaluation frameworks go beyond traditional benchmarks to rigorously assess real-world AI performance across diverse real-world scenarios.

Ready to build better AI?