RLHF, prompt engineering and synthetic data.
Overview
AI in Generative AI
Generative AI, from foundation models to LLMs and multimodal systems, needs the most sophisticated human feedback data in the industry. From preference ranking to red-teaming, feedback quality determines alignment.
Key challenges
- Calibrated preference data at scale across diverse task domains
- Red-teaming coverage for safety, bias and alignment failure modes
- Multilingual RLHF for cross-lingual foundation model alignment
- Synthetic data generation for rare scenarios and domain expansion
Our solutions
How we help
- 01Expert rater programs with domain calibration and IAA monitoring
- 02Structured red-teaming protocols with adversarial prompt libraries
- 03Cross-lingual RLHF pipelines with native-speaker rater pools
- 04Validated synthetic data augmentation frameworks
Results
2M+Preference pairs
−34%Hallucination rate
40+Languages for RLHF
Power yourgenerative AI
Start with a scoped pilot: results in weeks, not months.