Inephany raises £1.8m in pre-seed funding led by Amadeus Capital Partners to advance its AI optimisation platform.
The London-based deep tech startup secured backing from Amadeus Capital Partners, Sure Valley Ventures, and Professor Steve Young to expand its engineering team and refine its neural network training technology.
Share allotment filed at Companies House
Inephany builds intelligent optimisation software to make the training and deployment of large language models and neural networks more efficient.
Inephany has raised £1.8m in a pre-seed funding round led by Amadeus Capital Partners. The round also saw participation from Sure Valley Ventures and Professor Steve Young. The London-based startup will direct the capital towards expanding its engineering and research team, developing its AI optimisation engine, and supporting early enterprise users as it prepares to launch its initial products later this year.
Founded in 2024 by veterans from Apple Siri and Wluper, Inephany addresses the rising computational costs associated with training advanced artificial intelligence models. Its platform provides a model-agnostic optimisation system designed to reduce compute expenses and environmental footprints significantly.
From the founder
"We are thrilled to be backed by such experienced investors, and having a seasoned entrepreneur and AI pioneer like Professor Steve Young as our chair is a true privilege. Current approaches to training LLMs and other neural networks are extremely wasteful across multiple dimensions. Our unique solution tackles this inefficiency head-on, with the potential to radically reduce both the cost and time required to train and optimise state-of-the-art models. As we prepare to deliver our first products later this year, we are incredibly excited to embark on the next chapter of our journey—and to help shape the ongoing AI revolution by transforming AI optimisation."
John Torr
CEO at Inephany
Investor perspective
"We very much look forward to backing John, Hami, and Maurice as they tackle key efficiency challenges in current AI training. Their innovative approach to automating and optimising neural network training has the potential to reduce costs by an order of magnitude and accelerate advancements across AI applications. If rolled out at scale, the impact of this on what models can deliver will be very substantial."
Amelia Armour
Partner, Amadeus
Use of funds
- 01Grow the team
- 02Continue product development
- 03Accelerate go-to-market
