| Training models on any data, anywhere, using decentralized machine learning nodes.

If the next AI winter comes, it will be because we couldn’t feed our models with enough data.

Data exists but it’s siloed and unreachable. This ruins models, particularly in banks. Anti-Money-Laundering false positive rates average 95%, fuelling $3Tn of financial crime a year.

Data scientists use Gensyn’s differentially private federated learning infrastructure to train models over networks of nodes: taking models to data, not data to models.

Gensyn’s uses transcend banks, for example in Reinsurance, Material Science and Health. We have pilots with a tier 1 bank and a global chemical firm, plus a diverse pipeline.

Dr. Ben Fielding (left) and Harry Grieve (right)

Dr. Ben Fielding (left) and Harry Grieve (right)

Harry Grieve, CEO and co-founder

Harry is a full stack data scientist and previously led the data team from seed to series B at a high growth insurance risk prediction start-up. Harry has sold enterprise AI solutions to some of the world’s largest financial institutions and holds a masters degree from Brown University.

Dr. Ben Fielding, CTO and co-founder

Ben is a Computer Scientist, holding a PhD and with publications in Evolutionary Optimisation for Neural Architecture Search. He was the founding CEO of Fair Custodian, a B2B privacy automation start-up, and has experience building state-of-the-art AI systems for a variety of enterprises.