Around the world there are many databases containing huge amounts of genetic information.

This information is provided by volunteers and is anonymised, with the goal of being able to help researchers discover the causes and cures for genetic conditions and diseases. 

Biobanks, as they are known, are quite a big deal in the "omics" (biology fields of studies like genomics, proteomics, metabolomics etc) and genome sequencing industries. Acting as a central repository of biological information from millions of individuals, they allow multiple research organisations to share and access anonymised data from a much larger set than they could on their own.

These databases hold enormous amounts of data, and whilst the data is technically available for public access, it can be really difficult to find the right data (think "can't see the wood for the trees") let alone organise the data in the right way to run the research.

Lifebit claim to make this much much simpler, stating that they provide a “radically new approach to bioinformatics".

They use Machine Learning (ML) and Artificial Intelligence (AI) in a variety of ways to not only make it easier to discover the pertinent data in these vast mountains of information, but then to recommend what kind of computing architecture should be used to analyse that data most effectively.

This means research can more quickly find the relevant data for their studies, and more quickly set up the right kind of analysis. One case study I read stated that it reduced the time to market by 6 months.

Lifebit provide collaborative solutions for tacking the biggest challenges in drug discovery and precision medicine. Their technology leverages AI to extract and organise the necessary information from vast quantities of genome data stored in biobanks.

In April 2020 Lifebit closed a $7.5M series A round to help them scale and deliver their solution globally.