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What is NeuralSpace?

NeuralSpace is a Software as a Service (SaaS) platform which offers developers a suite of APIs for Natural Language Processing (NLP) that you can use without having any Machine Learning (ML) or Data Science knowledge.

NeuralSpace Platform

Along with some of the common languages like English, German, French, etc., the platform supports various languages spoken across India, South East Asia, Africa, Middle East, Scandinavia and Eastern Europe, aka, low-resource languages. The primary goal is to democratize NLP and make sure any developer can create apps with advanced language processing in any language and not just English.

The platform comes with various language processing Apps that can help you classify long or short text into categories, identify speakers in a given audio file, transcribe speech into text, and much more. The AI models that power our platform are state-of-the-art, quality assured and ready for you to customize or consume out-of-the-box.

AutoNLP

All our apps can be trained/customized using AutoNLP, with which you can build your own language processing AI models, deploy them on our cloud infrastructure, and start using them through APIs with just a few clicks without having any ML or AI knowledge.

The NeuralSpace platform is designed as a no-code/low-code platform for you to go to market faster and focus on the business problems you are solving, while we take care of all your language processing needs.

apps Explore Our Apps

NeuraLingo


Whether you are using chatbots, voicebots, or process automation engines, they are all powered by Natural Language Understanding (NLU). It's main purpose is to understand the intent of the user, and extract relevant information (entities) from what they said (speech) or wrote (text) to perform a relevant action. Entities can be anything from names, addresses, account numbers to very domain specific terms like names of chemicals, medicines, etc. Sometimes it also predicts the sentiment of the user which helps the bot respond to the user in a more empathetic tone.

Named Entity Recognition (NER)


Text documents contain information that are useful to make them more searchable. Information such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. These are called entities. While indexing documents with such meta information makes them easily accessible, manually extracting them from documents for indexing is extremely laborious and expensive. Additionally, extracting domain specific terms and phrases requires expert knowledge.

We provide pre-trained entity extraction APIs for 57 languages.

Neural Machine Translation (NMT)


Whether we are talking about subtitles, government documents or question papers for exams, all of them need to be translated into multiple languages. Manually translating documents at such a scale is not only expensive but also an extremely time consuming process.

With the help of Neural Machine Translation (NMT) you can drastically reduce the amount of time it takes for manual translation of documents. Our translation models are all state-of-the-art Artificial Neural Networks which can translate text in over 100 languages.

Transliteration


For languages that don't use the latin script, e.g., Arabic, Hindi, Punjabi, Sinhala and many other spoken around the world, typing can be challenging as keyboards/keypads mostly default to latin characters. That makes creating content in vernacular languages difficult. With transliteration you can create content in these languages using your latin keypad.

For instance, you type a word on the latin keypad the way you would pronounce it in Punjabi, and using transliteration you can convert that into Punjabi. It transforms a word from one alphabet to the other phonetically.

NeuralAug


Any language processing task requires data, and we all wish we could generate data magically. That was exactly the idea behind building NeuralAug. Given a sentence NeuralAug can generate up to ten sentences keeping the intent of the original sentence intact. It can help in creating datasets faster and make language processing models more robust.

Language Detection


If the users of your application are multilingual, you naturally have the need to detect which language they are speaking in. This helps you improve user experience as well as pick language specific AI models to process what they are saying. E.g., a chatbot can detect the language the user is speaking in and speak the same language, or an email automation agent can detect the language of the sender and accordingly pick a language-specific AI model to process the rest of the conversation.

  • Language Support: Detect over 150 languages directly through our APIs
  • Supported Medium: Currently only text documents are supported

Speech To Text (STT) (Coming Soon)


Speech is the most natural choice of communication for humans. However, AI models cannot semantically understand speech nearly as well as text. What if, you could have a bridge that let's you use speech as an interface while also interpret the meaning behind, to react meaningfully. The Speech To Text app is that bridge for us. It is built with state-of-the-art speech to text AI models for providing accurate transcriptions to speech. Once, we have transcription, it is basically text that can then be meaningfully interpretted by the advanced and battle tested NS NLU apps.

With the help of Speech To Text (STT) you can exponentially reduce the amount of time it takes for manual transcription of speech audios.

Speaker Identification (coming soon)


Speaker Identification or Speech diarization is a process of identifying the speaker by an audio input. Our speaker identification app can help you to separate out different individuals by just uploading an audio file through our APIs.

Short Unlabelled Document Analysis (coming soon)


We consider any document with less than 300 word a short document. It is commonly known that AI models need labelled data, where labels are additional information about your data that defines it. E.g., news categories like sports, or politics can be labels for news articles; positive, negative, or neutral can be labels for comments or tweets for sentiment analysis; Intents like wants-to-buy, or looking-for-a-discount can be labels for a user messages to train a chatbot.

Lack of labelled data

It is a well known problem that labelled data is usually not available or is expensive to curate. Your customers would give you unlabelled emails, or user messages, or just documents and you have to start from scratch. Just finding all possible labels is by itself a daunting task.

Long Document Classification and Analysis (coming soon)


We consider any document with greater than 300 words a long document. Whether it be annual reports, research papers, technical documentations or pdf documents, if you are sourcing or generating such documents you have to have a way to organize and sort them into categories. You might even have to extract entities from these documents, which are specific information like names, addresses, dates, etc. Doing this manually can be quite expensive and time consuming. Doing the same for multiple languages can be even more testing.

Run on Postman


To kickstart your journey with NeuralSpace we have created a Postman collection that you can fork.

Run in Postman

👉 API Docs on Postman