Who are BLUESKEYE AI? 

We are a VC funded spin out from the University of Nottingham set up in April 2019 to commercialise 18 years of academic research undertaken by our  founder, Professor Michel Valstar and his team  in the Affective Computing (AC) and Social Signal Processing (SSP) field. 

What do we do?

We are committed to improving tomorrow’s quality of life, today. We care deeply about developing advanced, affordable and accessible technology that creates products for our customers and end-users that are highly relevant, usable and have real world impact.

Our mission is to improve health and wellbeing by building objective, accessible, scalable and affordable mobile device applications for assessing health, mood and mental state and putting them in the hands of the public as well as developers.

How do we do it?

We use machine learning to objectively and automatically analyse face and voice data and interpret medically relevant expressed behaviour and assist in the assessment, treatment and monitoring of health, mood and mental state. 

Our Offer

B-Social – makes it easy to design a more engaging, empathetic and responsive virtual assistant or social robot for use in store or online. 

B-Healthy – makes it easy to both design a new health and well being app from scratch or customise our unique measurement technology into an existing branded companion app.

B-Automotive – will revolutionise the passenger experience making the car of the future safer, smarter and more responsive to passenger mood, wellbeing and health.

Our values

A strong ethical framework  is central to everything we do. 

Our AI models are designed to be interpretable and transparent, with predictions based on readily verifiable data. Resulting in an AI system where outputs can be checked independently. 

We create our technology with privacy included by design. Data collection and storage is minimised wherever practical, and we process all data on people’s own devices, without using the cloud. Users choose who they share their data with, and when and always with end-to-end encryption.