Learning systems for
precision fermentation

We're building experimental programs that get smarter with every run. Each experiment updates what the system knows. Every recommendation explains what it learned from and why.

What we believe

The bottleneck isn't generating data

It's turning that data into decisions that compound. Most teams ask "what does this data say?" We think the better question is "what experiment would most improve our ability to act?"

Our north star

Learning loops, not dashboards

Not summaries of what happened. Not models trained once on historical data. Systems where each experiment updates what the model knows, and every recommendation comes with full lineage.

Where we're starting

A unified data layer with AI built in

Before you can learn from experiments, you need to find them. We're starting with the foundation: bringing all your fermentation data into one place, with full traceability and AI that understands context.

Import anything

CSV, Excel, instrument exports. Map once, import forever. Your data becomes browsable.

Trace everything

Every chart links back to source. Click any datapoint, see exactly where it came from.

AI with context

The model sees what you see. It surfaces patterns and explains its reasoning.

Search everything

Ask questions in plain language. Find any experiment, any measurement, instantly.

What would teach us the most?

Every recommendation is designed to reduce uncertainty, not just optimize a number.

Nothing gets lost

Every experiment, outcome, and condition — searchable, connected, always there.

Full lineage

Know what it learned from, why it thinks what it thinks, how confident it is.

We're working with a small number of teams

If this is your problem, we'd love to talk.