Research initiatives
Pushing the boundaries of AI with specialized reasoning, privacy preservation, and autonomous customization to solve tomorrow's challenges today.
Academic partnerships
Privacy
TrustML privacy research
Our TrustML research focuses on developing frameworks that convert transformer-based architectures into their private counterparts. These frameworks enable inference and fine-tuning while preserving privacy for data and model owners.
Novel permutation and factorization techniques for privacy-preserving AI with minimal computational overhead.
Secure multi-party computation for highest-level privacy guarantees.
Differential privacy integration for enhanced data protection.
Customizable privacy solutions with trade-offs between privacy and computation.
Research
Novel Post-Training Techniques
Our Autonomous Finetuning research focuses on creating systems that enable developers to build custom AI models without machine learning expertise through sophisticated data augmentation and automated optimization.
Multi-agent orchestration for high-quality synthetic data generation.
Tunable training parameters balancing depth and resource efficiency.
Autonomous evaluation and model selection through parallel training.
Domain-specific model customization with configurable creativity controls.
Research initiative
Collaboration
We collaborate with leading universities and research institutions on cutting-edge AI research.
Open source is at the heart of our mission, sharing knowledge and code to advance AI for everyone.
Let’s collaborate
Interested in collaborating with our research team or learning more about our initiatives?