Optimizer for Pharma

Get assays to automation faster

Design more informative plate-based experiments, account for
execution constraints earlier, and use each result to guide the next.

Request Early Access

Generate the data your models need

Existing data is often too inconsistent, incomplete, or poorly matched to the model a team is building.

The Optimizer helps pharma teams develop reliable, high-throughput assays that generate fit-for-purpose experimental data, with every result connected to the parameters, controls, and conditions behind it.

Use your data to train your models. Your team can use its experimental data to develop and improve internal models.

Your data does not train ours. Potato does not use your protocols, results, or generated outputs to train or improve its own models.

Design and execution in one workflow

Assay and automation teams often optimize in sequence, forcing protocols to be rebuilt when execution constraints surface late. The Optimizer brings the assay

For assay scientists

Choose the parameters, ranges, controls, and conditions worth testing.

For automation teams

Generate plate maps, worklists, and run-ready outputs that account for labware, volumes, and liquid-handler constraints.

For R&D/CMC leaders

Reduce avoidable optimization cycles and move assays toward screening and scale with a clearer experimental record.

From assay goal to next run

Four-step experimental loop: Define, Design, Prepare, and Learn

Define

Bring in the protocol, performance targets, prior data, and known constraints.
Design

Select what to test and generate a structured experimental design.
Prepare

Create condition tables, plate maps, protocols, and automation-ready files.
Learn

Connect results to the conditions that produced them and decide what to test next.

Built for plate-based workflows

ELISA optimization · Cell-based assay development · Media optimization · Screening preparation · Assay development · Bench-to-automation transfer

Build a better optimization loop

Early access is open to selected pharma R&D, assay development, and CMC teams running plate-based endpoint analysis.

If your team is spending too many cycles on the handoff between assay scientist and automation engineer, or rebuilding protocols that should have been automation-ready from the start, we’d like to show you what a more structured approach looks like in practice.