Our Vision
We envision a future where AI systems are truly aligned with human values and capable of reliably assisting across all domains of human endeavor. We’re building the infrastructure of human feedback that makes this future possible.
Our Services
Rubric Creation
Creating effective evaluation criteria is both an art and a science. Our rubric development process involves:
- Domain expert consultation to identify key quality dimensions
- Iterative refinement based on annotator feedback and inter-rater reliability metrics
- Comprehensive documentation for consistent application at scale
- Regular calibration sessions to maintain annotation quality
Our rubrics have been used to evaluate outputs across coding, mathematics, scientific reasoning, creative writing, and many other domains.
RLHF (Reinforcement Learning from Human Feedback)
We provide end-to-end RLHF data services:
- Preference data collection - Side-by-side comparisons with detailed reasoning
- Reward model training data - Scalar ratings with calibrated annotators
- Red teaming - Adversarial testing to improve model safety
- Constitutional AI data - Critiques and revisions for self-improvement
Our RLHF data has helped train models that are more helpful, harmless, and honest.
SOTA Failure Analysis
Understanding where state-of-the-art models fail is crucial for improvement. We specialize in:
- Systematic failure categorization - Taxonomy development for common failure modes
- Edge case discovery - Identifying inputs that expose model limitations
- Benchmark development - Creating evaluation sets focused on known weaknesses
- Regression testing - Ensuring new models don’t reintroduce old failures
We document not just what fails, but why it fails and how to fix it.
Evaluation & Ranking
Rigorous evaluation is the foundation of model improvement:
- Blind evaluation protocols - Unbiased assessment of model outputs
- Multi-dimensional scoring - Capturing accuracy, helpfulness, safety, and style
- Statistical analysis - Confidence intervals and significance testing
- Leaderboard management - Fair comparison across models and versions
STEM Domain Expertise
Our annotator network includes specialists in:
- Mathematics - From arithmetic to abstract algebra
- Computer Science - Algorithms, systems, and software engineering
- Physics - Classical mechanics to quantum field theory
- Chemistry - Organic, inorganic, and biochemistry
- Biology - Molecular biology to ecology
- Engineering - Electrical, mechanical, and civil
This expertise enables accurate annotation of technical content that general annotators would struggle with.
Our Mission
To advance the frontier of artificial intelligence by providing the highest quality human feedback and evaluation data, enabling the development of AI systems that are more capable, reliable, and aligned with human values.
We believe that the future of AI depends not just on algorithmic advances, but on the quality of human feedback that guides model development. Every annotation we produce is an investment in AI systems that better serve humanity.