Many methods study microbes one at a time—a poor proxy for how they operate in the presence of other microbes.
Other methods take a census of entire microbial ecosystems at once, which cannot teach us how the parts give rise to the whole.
Concerto learns how interactions within communities give rise to microbial synergies and desirable properties. We translate this knowledge directly into products.
Even a small bank of microbes (hundreds) creates a combinatorial explosion of possible community compositions (millions or billions)—far too big for automated microbiology methods to generate. kChip solves this experimental bottleneck.
To design high-performing products, we measure what matters. kChip captures direct readouts of microbial function—tracking pathogen suppression, metabolite production or degradation, robustness to environment, and other key performance indicators critical to rigorous product design.
kChip generates high-quality, high-volume datasets—over 40 million measured combinations and counting. These measurements form of the basis of interaction networks and represent ideal datasets to train an AI to understand how microbes influence each other.
The trained kAI model can expand the microbial network, predicting how new, untested combinations behave. The filled-in network let's us see far beyond what’s experimentally measured.
With a kChip-seeded, kAI-expanded network, we identify synergistic, robust combinations that perform their functions when embedded into real communities.
Amidst the billions of possibilities, kChip and kAI pinpoint precise microbial combinations that deliver superior function—in therapeutics, consumer products, food applications, and beyond.
We build the solution that best solves the problem: the microbes themselves, prebiotic nutrients, or postbiotic ferments.
Our products are greater than the sum of their microbes. Synergistic combinations power performance.
By measuring performance across millions of contexts, we choose microbes that maintain their performance no matter what (or who!) they run into.
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