NeuralSpace secures £1.2 million Seed investment led by Merus Capital

NeuralSpace raises $1.7 million seed to bring NLP to local languages

NeuralSpace is a Natural Language Processing (NLP) company specializing in local, or low-resource languages. The NeuralSpace Platform is developers’ one-stop solution for any NLP features, does not require any machine learning expertise, and all that is needed is a handful of data to train your custom model with a click of a button. The NeuralSpace Platform is a no-code, modular developer toolkit and each of its Apps, from its Natural Language Understanding (NLU) engine called NeuraLingo, to Speech-to-Text, Machine Translation and Transliteration can be taken as a standalone product, even installed on a private cloud if required. NeuralSpace’s co-founder and CEO Felix Laumann points out that

“training latest deep learning models accurately on very small data sets, which any local, low-resource language usually suffers from, is a very challenging task because models are usually designed to perform well in English with terabytes of available text and speech data but not in languages with a few hundred megabytes of data.” 

Investment 

The $1.7 million seed round is led by Merus Capital and its General Partner, David Rangel, joins the Board at NeuralSpace. Further investors are APX (the joint venture arm of Porsche and Axel Springer), Techstars, Verissimo and a few impactful angels. The investment will help NeuralSpace to scale its no-code platform that went live as a self-serve toolkit on 31 January 2022 with NeuraLingo, the NLU app in 87 languages, and multiple auxiliary functionalities such as automatic data augmentation, automatic data set conversion (from Rasa, Google Dialogflow and Microsoft LUIS), language-detection to capture the language preference of a user automatically, and translation to translate a user’s input whenever needed for additional context. NeuralSpace plans to add state-of-the-art speech models (both Speech-to-Text and Text-to-Speech) for the same 87 languages to its Platform in the first quarter of 2022 and make developers trade their domain-specific pre-trained NLP models on a marketplace. 

David Rangel says:

“As more and more people around the globe get access to online services, everything from e-commerce to financial services, they will want to be served and supported in their native languages. NeuralSpace is in a position to be the key provider of NLP technologies for all these underserved languages, services and populations.”

Why NeuralSpace

Private Cloud Solution

NeuralSpace offers to deploy the NeuralSpace Platform on a private cloud. It has already successfully done this with Hello Ebbot, a Swedish chatbot development company operating in Scandinavian and other European languages, where the NeuralSpace Platform is hosted on infrastructure arranged and provided by Hello Ebbot. It allows Hello Ebbot to comply with GDPR requirements as customer-owned data are not shared or processed outside of the private cloud that is owned by Hello Ebbot and hosted in the European Union. 

State-of-the-art NLP Models

NeuralSpace always makes sure that its proprietary NLP models behind any of its Apps on the NeuralSpace Platform are state-of-the-art and comparable offerings by Google Cloud, Microsoft Azure or AWS do not achieve higher accuracies. Additionally, scalability or load handling capabilities are automatically integrated using a proprietary AutoMLOps module so users of the NeuralSpace Platform do not only get the most accurate models but can also use them for thousands of requests per minute. The NLP models behind the NeuralSpace Apps are specialized in low-resource languages, which are languages with small data sets that are predominantly spoken in Scandinavia, Africa, and Asia. To make the life of the software developers who use the NeuralSpace Platform as easy as possible, the Platform takes care of training models without specifying any model parameters by using a language-agnostic AutoNLP that NeuralSpace has developed over the last 12 months. 

Ease of Integration and No-Code Platform

The NeuralSpace Platform is developed keeping in mind ease of use as well as ease of switching from any other solution to NeuralSpace. The Platform can be accessed from the CLI for developers, by REST APIs for an easy web or online product integration, and even by a no-code GUI so that training, testing, and deploying a model in production can be done without writing one line of code. The NeuralSpace Platform includes auxiliary functionalities like multiple output formats corresponding to Google Dialogflow, IBM Watson, or Rasa in order to make it easy to switch live products to the NeuralSpace Platform with minimal changes in customers’ own codebase.

One-Stop Solution

One of NeuralSpace’s most important propositions for any developer is that all NLP requirements can be fulfilled with one single platform and jingling between multiple vendors with different data formats is not required anymore. The NeuralSpace Platform provides various NLP-specific Apps like NeuralLingo (an NLU engine), Neural Machine Transliteration, Transliteration, NeuralAug (automated dataset augmentation), Language Detection and many more in one place so that developers do not need to think about handling different services,  payments, and contracts.


Scalability

One of the key advantages of the NeuralSpace Platform is its scalability. It is designed in such a way that it can linearly scale to millions of requests every hour, which is achieved by combining technologies like Kubernetes and Container Instances with a proprietary compute-load watchdog, data allocator, and training scheduler. The scalability is extensively evaluated through load testing techniques by simulating a large number of requests the NeuralSpace Platform receives at the same time, providing statistics around the limits for a given number of model replicas, and providing an estimated cost for the infrastructure required for the given load quantities. Thus, it makes it easy for any developer to assess the economic feasibility of using NeuralSpace without having to go through a lengthy process of experimentation first.

All of these points make the NeuralSpace Platform a must-try for any team of developers that is using a collection of other solutions to provide language services to their customers. The ease of trying and integrating the NeuralSpace Platform should help developers make quick decisions about how the NeuralSpace Platform can help them best.

Safiya Marzook

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