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Deepset raises $14M to help companies build NLP apps –

Native Language Processing (NLP), an AI site that involves text analysis of tasks including summarizing and generation, is a rapidly evolving technology. According to 2021 exploration According to John Snow Labs and Gradient Stream, 60% of technology leaders showed that their NLP budget grew by at least 10% compared to 2020, while a third said their spending had increased by more than 30%. Fortune Trading Perspective stuck NLP market for $ 16.53 by 2020.

In addition to this place, Deep, The start-up behind the open source NLP model Haystack, announced today that it has raised $ 14 million in GV-led Investment Series through its participation in Harpoon Ventures, System.One, Lunar Ventures, and Acequia Capital. Capital infusion came with Deepset Cloud, a new registration product for building NLP-based computers.

“It simply came to our notice then [our] Believing in open-source, the Deepset team… contributed to the models and results of NLP open-source community research [for years]”Rusic told TechCrunch by email.”Haystack, the company’s open source product, is a product of the experience, expertise, and knowledge gained while building NLP for large organizations and the need for an accurate framework for NLP applications driven by the API.

CEO Milos Rusic shared with Deepset Malte Pietsch and Timo Möller in 2018. Pietsch and Möller – who have a background in data science – came from Plista, an advertising startup, where they worked on products including AI creation tools. works.

Haystack allows developers to build pipelines for NLP use cases. Originally designed for search queries, the framework can enable engines that answer specific questions (e.g. “Why are they moving to Berlin?”) Or quickly navigate through documents.

Haystack can also go into “knowledge-based” searches that search for general information on websites with more data or internal wikis. Rusic says Haystack was used to streamline the workflow of managing financial services risk companies, reversing the results of questions such as “What is a business perspective?” and “How has revenue grown in recent years?” Other organizations, such as Alcatel-Lucent Enterprise, have empowered Haystack to launch technical assistants who recommend documents to on-site technicians.


Photographer for Haystack interface.

According to Rusic, Haystack’s goal was to enable developers and product segments to build a state-of-the-art NLP platform, driving API successfully – and quickly. He noted that, although it is often the case for the data science team to come up with a model, there can be challenges in moving from modeling to productivity. About 80% of AI projects – including NLP projects – have never been productive, according to 2019 Gartner exploration.

“[With Haystack,] development teams … are equipped with all components to build a full NLP application and are guided by the right workflow technology and real-time production technology through an open source, “Rusic said.”[Prebuilt NLP systems] is the basis [for Haystack] and they often provide good results on the pipeline without further training. Customization, if needed, happens to end users and experts who provide feedback by testing and using a new repetition of a [system] or hide. ”

But not every company chooses – or wants to – go the DIY route. For those who prefer a managed solution, there are the aforementioned Deepset Cloud, which supports customers across the NLP service life cycle. The service begins with testing – for example, testing and evaluating the app, correcting a used case, and building conceptual evidence – and ends with labeling and monitoring of the app.

“All NLP services have been developed [with Deepset Cloud] It can be used for any end-to-end application, simply by integrating an API, “said Rusic.

With new funding secured ($ 15.6 overall), Deepset wants to translate its success with an open eye – thousands of organizations now use Haystack – increased revenue. Rusic has 30 people, Berlin, the German-based company was shut down and broken even before the first round of financing in 2021, and now has large commercial customers including Airbus.

“[With the new funding,] We will continue to build an open source for the Haystack NLP project – adding additional features, making it even more direct to develop NLP-savvy backend NLP services, ”Rusic said. “[We’ll also] Make Deepset Cloud a fully functional business program for building language knowledge applications. This will include flexible workflow capability, large product daily guidance, and the provision of essential tools and supports, such as labeling and data integration.