The control system for breeding.
BreedOS helps breeders and bioengineers choose what to cross, what to edit, and what to keep — while preserving long-term genetic diversity.
Breeding is becoming data-rich but decision-poor.
Breeding teams now generate genomic data, phenotypes, field trials, pedigree records, environment measurements, and increasingly gene-editing data. But the core decision is still hard: what should we cross, what should we edit, what should we keep, and what happens over the next 10 or 20 generations?
BreedOS is the decision layer.
Selection Digital Twin
Simulate genetic gain, allele-frequency drift, diversity loss, inbreeding, bottlenecks, and future selection optionality.
Genomic Selection Planner (roadmap)
Designed to rank candidate parents, compare crosses, and choose selection strategies under long-term constraints. Production integration scheduled for the program build.
CRISPR Strategy Layer
Prioritize candidate edits and test whether an edit, a cross, or a combined strategy creates the best population trajectory.
CRISPR gives breeders a limited but powerful write function. BreedOS decides what to write.
BreedOS does not design guide RNAs, score off-targets, or provide wet-lab protocols — that space is covered by Benchling, Synthego, CRISPResso, and downstream tools. BreedOS instead demonstrates the product layer above them: edit-aware selection planning. Candidate edits are ranked by expected trait value, current allele frequency, and population-risk logic; selected edits can be seeded into a simulated population, then propagated through balanced selection.
Genotypes, phenotypes, pedigree, environment, candidate variants.
Cross, edit, keep, reject, or preserve as diversity reserve.
Simulate the population trajectory before committing resources.
One-line pitch
BreedOS is the decision engine for artificial selection, helping breeding teams optimize genetic gain across generations without losing the diversity that makes future improvement possible.
Why now
- Sequencing made biological populations readable.
- CRISPR and precision breeding made biology writable.
- AI and cheap compute make multi-generation biological decision systems possible.
- The bottleneck is moving from data generation to decision intelligence.
Initial beachhead
Crop and agricultural biotech breeding teams working on climate-resilient traits: drought tolerance, heat tolerance, disease resistance, salinity tolerance, yield stability, and resource efficiency.