Own intelligence you can understand and trust to make the next leap.

Tavana is building the infrastructure and technology to make this possible. We bring together models, data, scientific knowledge, and real-world feedback to create intelligence that organizations can own, understand, and continuously improve. For example, when a drug candidate, biological target, or new material proposed by an AI system fails experimental testing, a team could investigate what the model learned and missed, use those insights to form a new hypothesis, and test it using their own experimental infrastructure. The resulting evidence feeds back into the system, improving the model and informing future research.

We are starting with science, where predictions are becoming abundant, but discovery still depends on understanding what they mean. Organizations need to understand what a model has learned, why it produces a result, and what new evidence might change. Mechanistic interpretability helps them investigate the model’s inner workings and form hypotheses. Teams test those hypotheses using their own data, models, and experimental infrastructure, then feed the results back into an AI system they control.

Our goal is to make intelligence compound over time. Every model, experiment, result, and discovery will make the system more capable than before.

Organizations should understand and control the intelligence they build. They can keep their data and model weights private while having the option to learn with trusted partners across different scientific fields and data types when they share a research goal.