We're building integrated hardware-software solutions for the next decade of genomic analysis. Our tools are designed to work anywhere—from the cloud to the field—and scale from single samples to population-level studies.
Most genomics tools are software-only and rely on generic compute. We're taking a different approach: purpose-built hardware paired with optimized software for genome and pangenome analysis. The result is faster processing, lower costs, and deployments that actually make sense for your workflow. Whether you need a cloud instance or a standalone box, we're building solutions that fit how you work.
Speed matters. Our tools are built for accelerated computing from the ground up. We're obsessed with making things fast—not just on benchmarks, but in real production environments where time is money.
Not everyone has access to big cloud infrastructure. Field research, remote clinics, and resource-limited settings need genomic analysis too. Our edge solutions are small, efficient, and capable of real analysis, not just data collection. Your whole workflow should be able to run on one box, anywhere.
Cloud computing is supposed to save time and money. Too often it does neither. We optimize for both: faster processing means less compute time, which means lower bills. Our tools integrate with AWS, GCP, Azure, and OCI. Deploy once, scale to thousands of samples. We aim to remove any surprises in your cloud invoices.
Our software scales. One sample or ten thousand samples—same tools, same workflow. We've designed everything to handle population-scale studies without requiring you to rewrite your pipeline or learn new frameworks.
Illumina, PacBio, Oxford Nanopore—we support them all. Short reads, long reads, whatever you're generating. Our tools work across sequencing technologies because genomics isn't a one-platform world. P.S.: If you're a sequencing device company and we don't yet support your platform, get in touch so we can fix that.
Linear reference genomes are useful but limited. Pangenomes capture the full diversity of human genetic variation and improve analysis accuracy, especially for recurrent variation. We're building tools that treat pangenomes as first-class citizens, not afterthoughts. Better references mean better variant calling, better disease detection, and better outcomes. Historically, pangenome analysis has been slower than approaches using a linear reference. We aim to make tools that shatter that expectation.
We're building tools for how genomics will actually be used over the next decade: diverse deployment scenarios, massive scale, multiple data types, and pangenome-based analysis. If you need genomic analysis that's fast, accurate, and works in the real world, we're building it.