GPU-Native Data Infrastructure
We solve the hardest data integration problems in life sciences — starting with neuroscience, where multimodal alignment across imaging, omics, and time-series is essential but broken.
The Problem
Modern biology generates extraordinary data — imaging, spatial transcriptomics, electrophysiology, behavior, longitudinal recordings — but integrating it remains manual, brittle, and irreproducible. AI models fail not because of architecture, but because datasets break upstream. We fix that.
Our Platform
Built from the ground up for multimodal complexity, GPU acceleration, and scientific reproducibility.
Natively handles imaging, spatial omics, electrophysiology, and behavioral data — aligned across space and time into unified representations.
Built on NVIDIA RAPIDS and Clara-compatible pipelines. Process terabyte-scale biological datasets in minutes, not days.
Every QC decision tracked. Every transformation versioned. Datasets that survive reuse across labs and time.
Not just "cleaned" but truly usable — standardized, harmonized, and ready for foundation model training.
Team
Chief Executive Officer
Neuroscientist and builder focused on scalable data infrastructure for multimodal biology, with experience across imaging, omics, and longitudinal neural datasets.
CSO / Developer
Computational neuroscientist and engineer specializing in multimodal data integration, alignment, and analysis for large-scale biological systems.
Early Access
Help us build the right tool — your answers shape our roadmap.