Berlin-based deepset, a company that offers enterprise developer tools to build applications with LLMs, announced on Wednesday, August 9, that it has secured $30M (approximately €27.3M) in a funding round led by Balderton Capital.
Existing investors GV (Google Ventures) and Harpoon Ventures also participated in the round.
deepset has also secured backing from experienced founders in open source, including Alex Ratner (Snorkel AI), Mustafa Suleyman (Deepmind), Spencer Kimball (Cockroach Labs), Jeff Hammerbacher (Cloudera), and Emil Eifrem (Neo4j).
The German company says it will deploy the funds to continue developing the Haystack framework and the deepset Cloud product alongside the company’s international expansion.
This latest funding round takes the total funding raised by the Berlin-based firm to $46M (approximately €41.8M).
Milos Rusic, co-founder of deepset, says, “Enterprises can see huge benefits from leveraging LLM technology. At deepset, we’re providing a platform that helps to bridge the decades of research in machine learning and computer science into production-ready applications. In the same way, you don’t need to know much about microchip architecture to write software, you don’t need to be an NLP or LLM scientific researcher to use our Haystack framework and deepset Cloud.”
deepset: Helping companies embed LLMs into application
Founded in 2018 by Milos Rusic, Malte Pietsch, and Timo Möller, deepset solves one of the greatest challenges facing enterprises today – how to exploit their data more quickly, more easily, and at scale.
The company does this by helping organisations tap into the potential of large language models (LLMs).
In December 2019, the German company launched the Haystack framework, followed by the introduction of deepset Cloud, a cloud-based LLM developer platform, in April of the very next year.
For enterprises lacking the full expertise or time to build their own LLM platform, deepset Cloud offers a solution to harness the power of natural language processing (NLP) and construct applications enhanced by LLMs.
deepset Cloud streamlines the process of creating tailored LLM-driven applications by assisting AI engineers, software developers, and product managers in adapting rapid iteration methodology to NLP and, consequently, developing applications in a standardised, predictable manner.
By establishing a solid software development life cycle, deepset Cloud significantly reduces time-to-market and enables organisations to remain adaptable to different models.
deepset Cloud has helped European legal publishing house Manz, for example, to access a new and fast-growing category of LLM-enabled products for legal professionals, without having ML specialists.
The legal team can now instantly find precedents, relevant regulations, templates, cross-reference data, summarise findings, and more from millions of documents.
Likewise, Airbus’ R&D team utilises deepset’s open-source technology, Haystack, to create application that helps pilots discover and use the most relevant aircraft operation guidelines from the cockpit where time-to-information is of the absolute essence.
The company works with enterprises across the UK, Europe, and the US and its team now consists of 50 people.
The Investor
Balderton Capital is a multistage venture firm with more than two decades of experience supporting Europe’s best founders from Seed to IPO.
The VC invests early and growth funds across the technology sector, with a proven track record backing fintech, B2B SaaS, digital health, mobility, gaming, and marketplace companies.
Balderton’s current portfolio includes Aircall, Beauty Pie, Contentful, Dream Games, GoCardless, JOKR, Lendable, Matillon, Merama, Revolut, Tibber, Vestiaire Collective, Voi and Zego.
James Wise, partner at Balderton, says, “The pace of change in the power of LLMs is remarkable, but to date there has been a lot of testing without the tools needed to get applications built of these new models into production. deepset Cloud and Haystack provide the tools and platform for enterprises to develop powerful new AI applications transparently and securely.”
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