We use AI to enable

Our story

With a founding team whose experience includes mathematics, music, software engineering, arts, science and business, we know that creativity and diversity aren’t just buzzwords, they’re our recipe for making good things happen.

We started FeedForward because we see many amazing businesses and creators in the creative industry who have great industry knowledge and want to work with AI, but don’t have access to the expertise. We are your bridge between the frontier of machine learning and its practical applications.

FeedForward's story

Our philosophy


We don’t believe in hype.

With all the noise around AI, it can be hard to know what’s relevant. We think the science is exciting enough in itself.


We believe in human
creativity & curiosity…

….and that both are required for scientific discovery. Innovation is the combination of creativity, knowledge and skill.


We believe AI needs intelligent & responsible implementation.

This requires experts from academia and industry to work together.

Meet the team

Kevin Webster
Kevin Webster

Kevin is a Senior Teaching Fellow in Mathematics at Imperial College London, where he specialises in Deep Learning. He completed his PhD in dynamical systems at Imperial College London and his research experience includes postdoctoral positions at Imperial College, and a Marie-Curie Fellowship. He is currently teaching the Deep Learning course on the statistics MSc in the Mathematics department at Imperial College London and three Coursera courses on TensorFlow and Deep Learning.

Prior to founding FeedForward, he was Head of Research at Jukedeck where he led the team applying machine learning to music composition. He also spent 9 years as a professional musician, during which time he was Head of Keyboard & Applied Musicianship at BIMM (then Tech Music Schools). He was also a pianist, organist, composer & arranger, performing with his own trio and a range of artists for concerts & recording sessions.

Kevin has a particular interest in generative modelling using modern deep learning algorithms, especially applied to sequence modelling tasks such as music generation and audio synthesis. He also has expertise in using machine learning and deep learning models as part of music recommendation systems. His passion is in applying machine learning to the creative sphere.

Kevin Webster
Lydia Gregory
Jason Storey
William Meleyal
Jeremy Silver
Jonathan Finn
Martin Gould
Owen Smith
Matthew Hawn