Microbial & Fermentation
Turn promising strains into scalable processes.
In industrial biotech and precision fermentation, competitive advantage comes from how quickly every run becomes a better next experiment — and how confidently your team can carry that learning from bench to pilot, CDMO, and production. Invert helps keep the full process history organized, so promising biology has a better path to real-world scale.
The problem
Every run, a better next experiment.
Microbial and fermentation teams move quickly. Whether you are working with E. coli, yeast, Pichia, or other engineered strains, each run creates another opportunity to improve titer, yield, productivity, and cost of goods. But when runs are happening constantly across strains, media, carbon sources, conditions, and scales, the data can pile up faster than teams can make sense of it.
That makes process learning harder than it should be. Cross-run comparisons often need to be rebuilt manually, scale-up decisions depend on context scattered across files and people, and valuable optimization history can disappear during tech transfer or CDMO handoff. When process knowledge lives mostly in individual scientists’ heads, every departure, transition, or new program creates risk.
Invert gives fermentation teams a digital foundation where run data, process conditions, offline measurements, strain context, and scale-up history stay connected. Your team can compare runs in minutes, start analysis as soon as a run completes, and build institutional knowledge that compounds over time instead of getting trapped in spreadsheets, notebooks, or memory.
How we deliver
We help every run become a better next experiment or scale-up campaign.
Start learning right away
Invert helps close the gap between a run finishing and the analysis starting. Your team can move quickly from completed fermentation data to useful insights, without waiting on manual cleanup, spreadsheet rebuilds, or one-off analysis requests.
Compare anything in minutes
Fermentation teams need to compare across strains, conditions, scales, and process strategies constantly. Invert makes those comparisons easy to run and repeat, so scientists can spend less time rebuilding analysis and more time deciding what to try next.
Keep the learning in the system
Process knowledge should not live only in people’s heads. Invert keeps optimization history, run context, and scale-up decisions organized in one place, so hard-earned learning carries forward through tech transfer, CDMO handoffs, and team changes.
Every run, a better next experiment.
Bring a strain comparison, a scale-up decision, or a tech transfer that’s been stalled on data work. In 30 minutes we’ll show you what your fermentation history looks like when every run stays connected.