helps devs add language processing smarts to their apps – TechCrunch


While visual ‘no code‘ tools are helping businesses get more out of computing without the need for armies of in-house techies to configure software on behalf of other staff, access to the most powerful tech tools — at the ‘deep tech’ AI coal face — still requires some expert help (and/or costly in-house expertise). This is where bootstrapping French startup, is plying a trade-in MLOps/AIOps — or ‘compute platform as a service (being as it runs the queries on its own servers) — with a focus on natural language processing (NLP), as its name suggests. Developments in artificial intelligence have, in recent years, led to impressive advances in the field of NLP.

Technology that can help businesses scale their capacity intelligently grapple with all sorts of communications by automating tasks like Named Entity Recognition, sentiment-analysis, text classification, summarization, question answering, and Part-Of-Speech tagging, freeing up (human) staff to focus on more complex/nuanced work. (Although it’s worth emphasizing that the bulk of NLP research has focused on the English language — meaning that’s where this tech is most mature, so associated AI advances are not universally distributed.)

Production-ready (pre-trained) NLP models for English are readily available ‘out of the box. There are also dedicated open-source frameworks offering help with training models. But businesses wanting to tap into NLP still need to have the DevOps resource and chops to implement NLP models. is catering to companies that don’t feel up to the implementation challenge themselves — offering “production-ready NLP API” with the promise of “no DevOps required”. Its API is based on Hugging Face.

SpaCy open-source models. Customers can either choose to use ready-to-use pre-trained models (it selects the “best” open-source models; it does not build its own); or they can upload custom models developed internally by their own data scientists — which it says is a point of differentiation vs. SaaS services such as Google Natural Language (which uses Google’s ML models) or Amazon Comprehend and Monkey Learn. says it wants to democratize NLP by helping developers and data scientists deliver these projects “in no time and at a fair price”. (It has a tiered pricing model based on requests per minute, which starts at $39pm and ranges up to $1,199pm, at the enterprise end, for one custom model running on a GPU. It also offers a free tier so users can test models at low request velocity without incurring a charge.)

“The idea came from the fact that, as a software engineer, I saw many AI projects fail because of the deployment to the production phase,” says sole founder and CTO Julien Salinas. “Companies often focus on building accurate and fast

AI models but today, more and more excellent open-source models are available and are doing a fantastic job… so the most formidable challenge now is being able to efficiently use these models in production. It takes AI skills, DevOps skills, programming skills… which is why it’s a challenge for so many companies, so I decided to launch


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