Lamin

Scaling AI for complex biological systems means scaling its iterative cycle: laboratory results inform models, models design the next round of experiments. While leading ML and Comp Bio teams in the industry we experienced firsthand that — beyond data generation itself — the primary bottleneck was closing the feedback loop across heterogeneous modalities at scale. We needed a way to query, trace, and validate complex biological datasets with context and memory to effectively learn from mistakes and train the next generation of models.