Deepset.ai stands at the forefront of NLP innovations, transforming textual data into actionable insights. Join us as we delve into the workings and offerings of this leading-edge tech powerhouse.
Background Story
Deepset is an enterprise software vendor that provides developers with the tools to build production-ready natural language processing (NLP) systems. The company was founded in 2018 in Berlin, Germany by Milos Rusic, Malte Pietsch, and Timo Möller. The founders had a vision to make NLP accessible to everyone, regardless of their technical expertise. They wanted to create a platform that would enable developers to build NLP systems quickly and easily, without having to worry about the underlying infrastructure.
Target Customers
Deepset’s target customers are developers and data scientists who want to build NLP systems for their organizations. The company’s platform is designed to be easy to use, even for those who are not experts in NLP. Deepset’s customers come from a variety of industries, including finance, healthcare, and e-commerce.
Deepset has served a number of customers since its inception. Some of its featured customers include Deutsche Bahn, Germany’s national railway company, and the German Federal Ministry of Education and Research. Deepset has also worked with a number of startups, including Flink, a German grocery delivery startup, and Hasty.ai, a platform for building computer vision models. The industries it majorly focuses on include:
- Artificial Intelligence
- Machine Learning
- Natural Language Processing
- Open Source
- Search Engine
- Semantic Search
What is Roboflow, Uses, Alternatives, Features, and Everything You Need to Know
Funding and Capital Raised
Deepset has raised a total of $45.6 million in funding to date. The company’s most recent funding round was a Series B investment round of $30 million led by Balderton Capital, with participation from existing investors GV, System.One, Lunar Ventures, and Harpoon Ventures. In April 2022, the company announced a Series A investment round of $14 million led by GV, with participation from Harpoon Ventures, Acequia Capital, and a team of experienced commercial open source software and machine learning founders. Deepset’s previous pre-seed investment round of $1.6 million was led by System.One and Lunar Ventures, who also participated in the subsequent Series A round.
Products and Services
Deepset offers two main products: Haystack and deepset Cloud.
Haystack is an open source software that enables developers to build NLP systems quickly and easily. It includes a number of pre-built components, such as document stores, retrievers, and readers, that can be combined to create custom NLP pipelines. Haystack is designed to be modular and flexible, so developers can easily swap out components as needed.
Deepset Cloud is a commercial SaaS offering that provides a managed version of Haystack. It includes additional features, such as automatic scaling, monitoring, and support. Deepset Cloud is designed to be easy to use, even for those who are not experts in NLP. It allows developers to focus on building their NLP applications, rather than worrying about the underlying infrastructure.
Competitors
- SAP Fiori
- ProductPlan
- Saturn Cloud
- Termius
- Epsilon3
- SAS Studio
- Jina AI
- Apache Airflow
- Ask Codi
- Amazon Comprehend
Pros and Cons of Deepset
Pros
- Focus on Production-Ready NLP Systems: One of the main advantages of Deepset is its focus on building production-ready NLP systems. The company’s platform is designed to be easy to use, even for those who are not experts in NLP. This makes it accessible to a wide range of developers and data scientists.
- Cutting-edge Technologies: Deepset focuses on the latest technologies, especially in NLP.
- Open-Source Focus: The company is rooted in open-source software and has an active community on GitHub
- Research-Driven: There’s a strong emphasis on ML/NLP research, with opportunities for attending conferences and publishing papers.
- Modular and Flexible Platform: Deepset’s platform is designed to be modular and flexible, so developers can easily customize it to meet their specific needs.
Cons
- Evolving Internal Processes: Processes are still taking shape, which may mean they change frequently or are not fully established.
- Less Structure: Being a start-up, deepset may lack the structured environment seen in larger companies.
- Cost: Deepset’s commercial SaaS offering, Deepset Cloud, may be more expensive than other NLP tools and services.
