FluxPilot · in production

Design better microbes in minutes.

Scientists reprogram microbes to make useful things: medicines, food ingredients, cleaner fuels. The hard part is deciding which genetic changes to make. FluxPilot uses a computer model of the cell's chemistry to rank the changes most likely to work, and flags the ones that would kill the cell, so a lab tests the right ideas first instead of a month of dead ends. Every number comes from the model, not the AI's imagination.

Request access › Explore the demo › ← Biotech
FluxPilot dashboard: the metabolic-engineering workspace, with quick actions to upload data, train a model, and design experiments.
FluxPilot · dashboard
What it does

From lab data to a ranked to-do list, in minutes.

The problem

Deciding how to re-engineer a microbe is slow and needs a specialist, often weeks of careful hand-work. Generic AI makes it worse: it confidently invents numbers a lab cannot trust, or suggests a change that would kill the organism. Both burn real lab time and money.

What FluxPilot gives you

A ranked shortlist in minutes. Each suggested change shows how confident the model is, and carries a clear flag: safe, risky, or fatal. Every number comes from a validated chemistry model of the cell, not guesswork, and the assistant explains each result or admits when it cannot.

Capabilities

What it does for a working scientist.

01

Reads all your lab data at once

It combines your different measurements of the cell into one working model, so the suggestions reflect what is actually happening inside, not one narrow slice of it.

02

Ranks the changes, flags the deadly ones

It sorts the possible genetic edits by how likely they are to work, and marks each one safe, risky, or fatal, so a design that cannot survive never reaches the bench.

03

Turns a design into a lab plan

It writes the chosen design up as a step-by-step protocol, down to the exact tray layout a lab robot can run. The tedious handoff scientists used to do by hand.

04

It never makes up numbers

Every number comes from the chemistry model. The AI explains and plans; it will not invent a value just to sound helpful.

A small demonstration

The difference, on one question.

Same question, two behaviors. Most AI answers confidently even when it is wrong. FluxPilot answers only if it can point to a real source, otherwise it says it does not know.

"Does compound CX-417 inhibit the enzyme KasA?"
Illustrative. When a source exists, the same answer ships with its citation and a confidence score.
Validation

Tested on real data, not a toy example.

The test

We gave FluxPilot a large table of real E. coli gene-activity data with no hints, and asked it to find the patterns on its own. It rediscovered a known control system, the set of genes the microbe switches on to digest a particular sugar, and matched the textbook answer.

Why it matters

It found real biology, checked against a standard reference, in about two seconds, and the same run reproduces every time. This is the kind of result a scientist or a reviewer can verify for themselves, not a cherry-picked demo.

55/60
known genes recovered
0
hints given
~2s
to run
100%
reproducible
Request access

Tell us about your strain-design problem.

FluxPilot is in production with a free academic tier and enterprise licensing. Tell us the organism and the objective, and we will get you set up.