Zerve, a Dublin-based start-up specializing in data science and artificial intelligence, has raised $3.8 million in pre-seed funding to accelerate its research and development efforts. The company plans to use the funds to expand its team from 12 members to 24 over the next year, with a focus on engineering, cloud infrastructure, and research and development.
Founded by Phily Hayes, Jason Hillary, and Greg Michaelson in 2021, Zerve has developed a unique platform that allows collaboration between data science and AI development teams. This cloud-based, serverless environment enables real-time collaboration and the creation of stable code suitable for deployment. The company aims to transform data science into a collaborative experience where data scientists can work together and build on each other’s work.
The pre-seed funding round was led by Elkstone Ventures, with additional investment from angel investors such as Algolia CTO Sean Mullaney and Rob Hickey, former EVP of engineering at DataRobot. Niall McEvoy of Elkstone Ventures praised Zerve’s visionary technology, stating that it has the potential to revolutionize data science and AI development.
Zerve has also been recognized as one of the top 10 European startups for the Intel Ignite Accelerator program, focusing on deep-tech companies. This recognition further validates the potential impact of Zerve’s innovative approach to data science and AI development.
Phily Hayes highlighted the current limitations of existing tools for data scientists to share code and results with their colleagues. He emphasized the need for a more cohesive and collaborative environment that would allow data scientists to work together more effectively. With the funding secured by Zerve, the company will be able to capitalize on its vision and address some of these challenges faced by data scientists every day.
Zerve’s platform is designed to improve collaboration between data scientists who are currently working alone or in small teams without any shared infrastructure or communication tools. The company aims to provide a platform where data scientists can easily share code snippets, test their models locally before deploying them in production environments, get feedback from their colleagues in real time while they are building new features or models.
The funding raised by Zerve will enable it to continue developing its platform while expanding its team beyond engineering roles into cloud infrastructure management positions as well as adding expertise in research and development areas such as machine learning algorithms development.
Overall, Zerve’s innovative approach is set