MicroLub Raises $10.1M

AcouBatt

AcouBatt listens to batteries to improve formation, quality and safety. We’re building a “stethoscope for batteries”, combining passive acoustic emission sensors, data acquisition hardware and AI analysis software to reveal internal cell activity in real time. We listen to the signals generated as batteries form, age and degrade, helping teams understand what is happening inside their cells. Our initial focus is formation: the first charging and discharging steps that help determine a battery’s lifetime and performance. By synchronising acoustic activity with existing test data, we aim to help manufacturers shorten formation, reduce scrap and identify unusual cell behaviour earlier, while maintaining battery performance. Beyond manufacturing, we are developing tools for battery researchers and developers to compare cells, chemistries and test conditions. We are also developing applications for monitoring battery health and detecting early signs of failure during operation, including in battery energy storage systems. AcouBatt is a UCL spinout built on research supported by the Faraday Institution through the SafeBatt project. We are piloting our technology with the UK Battery Industrialisation Centre (UKBIC) and have raised £1.1m in pre-seed funding, led by Creator Fund with participation from Ada Ventures and angel investors. If you manufacture, develop or operate batteries, get in touch to discuss your cells, processes and monitoring needs. contact@acoubatt.ai

Quartz

The first wealth platform built to truly understand you. Quartz brings all your assets into one place, learns about your goals, and guides you with personalised, real-time intelligence. Get the experience of a personal banker that works with you 24/7, at a fraction of the cost.

Opio

We are one of the few machine learning agencies with deep product design experience. We approach a new technology project by first exploring the requirements of the end user; their current workflows, their goals, their idea of success. Using this knowledge we can design a system with just the right amount of machine learning, seamlessly integrated, to produce a product experience they love. Happy users lead to successful products.