Low-resource language API platform NeuralSpace raises $1.7 million in seed round


NeuralSpace, a SaaS platform offering natural language processing (NLP) APIs to engineers, raised $1.7 million in a funding round led by Merus Capital.

CEO Felix Laumann told Slator, “We aspire to establish ourselves as the go-to provider for NLP services in low-resource languages.” The platform is designed for software and mobile application developers to implement NLP functionality into their products, even with minimal knowledge of machine learning and data science.

This funding round valued NeuralSpace at $11 million. Other investors include APX (the joint venture of Porsche and Axel Springer), Verissimo and several influential angels. NeuralSpace initially connected with investor Techstars by participating in its acceleration program in 2021.


The company currently generates $100,000 ARR and has a tiered subscription model. Its team of 19 people, scattered around the world, is headquartered in London.

Founded in 2019 and started for the first 10 months, NeuralSpace had its first client in November 2019. Revenue from this client was used to hire a small team that laid the foundation for the platform.

“Now we’re at a stage where product development and marketing efforts need a lot more support and it was a good time to raise the first investment,” Laumann said.

NeuralSpace is looking for UX researchers and developers who can help users integrate a voice command system, automated summary, or one-click sentiment analysis.

Focus “niche” that could reach millions

Bridging language gaps is the language industry’s raison d’être, a mission shared by NeuralSpace. The company decided that the most financially viable strategy would be to work directly with software developers, whose products will reach end users who speak low-resource languages. In particular, the company’s primary customer base is chatbot and conversational AI development companies.

“If customers want to offer voice chat or bots in low-resource languages, our competitors often don’t support those languages ​​— or only with low accuracy,” Laumann explained. “We’ve developed proprietary algorithms that are very data-efficient, as low-resource languages ​​often only have 1% of the available data that English, French, or Spanish have.”

According to Laumann, Hugging Face, an AI platform backed by a large open-source community, is a strong contender due to its huge volume of models and datasets. But only a few of these models and datasets are available in low-resource languages.

They are also difficult for developers to use without a solid understanding of NLP – a likely scenario for many software developers in developing countries, where low-resource languages ​​are spoken.

2021 M&A and Funding Report Product

2021 Slator Report on Language Industry M&A and Financing

Data and Research, Slator Reports

46 pages on language industry mergers and acquisitions and venture capital funding. Includes financial investments, mergers, acquisitions and IPOs.

NeuralSpace combines several methods to circumvent the lack of data for low-resource languages. These include transfer learning and combining the datasets of several related languages. The platform currently supports 87 languages. It also offers language-neutral templates that developers can use for languages ​​that are not yet supported.

Voice to text

For general use cases – building a chatbot in English and offering it to users in 108 languages ​​- NeuralSpace’s machine translation (MT) API works well, but the company sees its TM as an ancillary service to the instant.

“Customers looking for machine translation only without using any of our other services on the NeuralSpace platform will likely choose another provider,” Laumann pointed out.

Beyond translation and transliteration, NeuralSpace advocates a “voice-first” approach for client products aimed at low-resource language speakers in developing countries, where lower literacy rates may be a postman.

“Simply put, talking is friendlier and in many situations also safer than typing, for example while driving a car,” Laumann added.


Comments are closed.