Better genetics
for aquaculture.

AI-native, zero-shot target discovery.

Feed it a chromosome-scale genome — no phenotype data, no QTL mapping, no field trials — and it returns editable, mechanistically ranked disease-resistance targets.

The Interrogation Protocol

  1. 01

    Structural Census

  2. 02

    Regulatory Topology

  3. 03

    Bottleneck Identification

  4. 04

    Switch Characterization

  5. 05

    Edit Design

  6. 06

    Computational Proof

Aquaculture feeds the planet with a genetics stack built for the last century.

The current approach is slow, phenotype-reliant, and structurally incapable of matching the pace at which pathogens evolve. Multi-year breeding cycles chase traits that disease pressure has already moved past.

The result is a massive, under-tooled protein production system — one where the genetics layer has barely been touched by computation, even as disease losses climb and global demand accelerates.

01

Phenotype-dependent

Can't discover what you can't observe. Breeding programs require years of field data before selection even begins.

02

Slow cycle times

Selective breeding operates on generational timescales. Pathogens evolve orders of magnitude faster.

03

No mechanistic insight

Traditional approaches surface correlations, not causes. You get markers, not targets.

Oysters face acute, industry-defining disease pressure.

The commercial demand for resilience is immediate. And the industry is already primed for genetic solutions — making this the optimal wedge market before scaling across species.

Disease burden

Mass mortality events routinely wipe out 50–90% of production. The economic toll is existential for growers and the genetic bottleneck is only tightening.

Commercial pull

Genetic improvement is already a proven lever in oyster aquaculture. Growers pay premiums for resilient seed. The demand signal is established — the technology to serve it hasn't been.

Platform proof

What works in oysters transfers. The computational workflow, the target library, the validation framework — all of it is designed to port to shrimp, salmon, tilapia, and beyond.

The value is the engine, not any single edit.

OG is a computation-first biology company. Our enterprise value compounds in the discovery engine, the accumulating target library, the validation datasets, and a workflow that transfers across species.

How it scales

  1. 01

    Proprietary broodstock

  2. 02

    Target licensing

  3. 03

    Cross-species genetics platform

Deep tech meets food security

Aquaculture is the fastest-growing protein sector on Earth and its genetics layer is untouched. We're bringing the computational rigor that transformed row-crop agriculture into the last major food system that hasn't had it.