OctaiPipe, the end-to-end Edge AI platform, has announced raising £3 million in pre-Series A funding and a £500,000 non-equity grant. The pre-Series A round was led by SuperSeed with Forward Partners, D2, Atlas Ventures, Martlet Capital, Gelecek Etki VC and Arm-backed Deeptech Labs also participating.
The money will allow OctaiPipe to further develop its proprietary Federated Learning technology and scale availability of the OctaiPipe platform for Internet of Things (IoT) dependent critical industries including Energy, Utilities, Telecoms, Manufacturing and connected device OEMs.
In addition to the funding, OctaiPipe has also announced the appointment of Arnaud Lagarde as Chief Revenue Officer. Mr. Lagarde will lead OctaiPipe’s commercial development. Prior to joining OctaiPipe, Mr Lagarde was the Vice President of Sales at Humanising Autonomy where he led the global sales efforts and go-to-market initiatives across Automotive, Autonomous Vehicles and smart city solution providers.
Critical Infrastructure is typified by data-rich, highly demanding environments where data security is often paramount. The application of AI on data collected by IoT devices has tremendous potential in Critical Infrastructure to increase productivity, improve sustainability and monitor asset health and performance. In the UK’s energy sector alone, the UK government’s digitisation strategy estimates AI, if fully harnessed, could reduce energy system costs by up to £70 billion by 2050. However, due to security concerns regarding data processing in the Cloud and rising Cloud AI costs, the utilisation of connected devices and AI in Critical Infrastructure has, until now, been limited.
OctaiPipe Federated Learning is a new decentralised approach to training AI models which does not require the exchange of data between IoT devices and Cloud servers. In Federated Learning, the data on IoT devices is used to train the AI model locally at the Edge, maximising performance and system resilience, increasing data security and radically reducing Cloud data costs.