We use generative AI for custom Nanobody and Antibody design across various pathogen types.
Our AI models can find hidden patterns and make predictions for oncology and immunology studies.
We integrate biological, statistical and AI knowledge to create more reliable biological AI systems.
AI agents assist with real-time decision-making in complex working environments.
Automated processes allow for more consistent outcomes across various tasks.
Effective integration of AI enhances human skills and productivity.
Neural network-based predictive analytics that can generalize to various non-linear patterns in data.
Swarms of multiple AI models which make mass predictions to improve reliability.
Using techniques like retrieval-augmented generation to reduce false outputs from AI systems.
Generating synthetic data to help improve simulation accuracy.
Digital twins that can simulate real-world clinical scenarios.
Synthetic data can reduce the costs associated with real-world testing.
We emphasize ongoing education for AI practitioners.
We foster strong team dynamics that enhance knowledge sharing and growth.
Implementing real-world projects allows us to increase practical skills development.
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