If your company deals with text of any kind (customer feedbacks, internal documents, code, etc.) chances are that you would gain to understand and process it automatically. We use Large Language Models (LLMs) - and a new learning paradigm that we call interactive AI development - so that data scientists, data analysts, and software developers can tackle these tasks efficiently.
Ok, before diving further into what we do, we would like to give a big thanks to our investors. Flybridge for leading this round, Big Bets for putting the first institutional check, Carya, Pioneer, Velocity, Sharpstone, all our business angels for believing in us, and of course YCombinator for kickstarting everything. We look forward to working with all of you and make NuMind a leading AI company!
And now a bit more about NuMindā¦
Unless you have been living under a rock these last months, you heard about the rise of LLMs and their most famous representatives: ChatGPT and GPT-4. These models - trained on a big chunk of the web - are able to generate text at a level that was unthinkable a few years ago. This opens up plenty of new applications, notably in text generation, natural language interfaces, but also in text understanding.
Text understanding is needed for a wide range of applications: content moderation, topic classification, customer feedback analytics, sentiment analysis, but also search and chatbots. Currently, tackling these applications requires machine learning experts, an intense labeling effort, and the outcome is often disappointing.
NuMind is born out of our own frustration with this situation - it was clear that things should be done differently in a post GPT-3 world already, so we decided to do something about it. I (Etienne, CEO) left my position of head of Machine Learning at Wolfram Research, Samuel (CTO) left his previous startup Make.org, and we teamed up to build NuMind - a tool to create custom NLP models efficiently, that can be used by both experts and non-experts.
Note the importance of the ācustomā aspect here. It is our experience that most NLP tasks are unique - even for something as standard as sentiment analysis - which makes off-the-shelf models not so useful.
The long-term idea of NuMind is that we should teach computers in the same way as we would teach humans. Think of how you would proceed with an intern whose job is to classify your emails for example. You would first give instructions and examples, and then a conversation would start: your intern would ask you questions, and in return you would test your intern to identify and correct their knowledge gaps. We call this approach interactive AI development to stress the importance of the human-AI interaction in order to define/teach a task.
Interactive AI development is both natural and extremely efficient, and this paradigm can now be put into action thanks to recent large language models. Of course the road will be long - a lot of R&D is needed - but we started to implement it for text understanding applications and it works!
We released a private beta a few months ago and have a dozen customers, with use cases such as sentiment analysis, job offer classification, or legal document analysis. We also currently offer a founder-level support to help you succeed your NLP project. So if you think you might have a need for automatic text understanding, do not hesitate to book a demo.
