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Most people who use AI-powered translation tools do so for commonplace, relatively unimportant tasks like understanding a single phrase or quote. Those basic services won’t do for an enterprise offering technical documents in 15 languages — but Lengoo 20m’s custom machine translation models might just do the trick. And with a new $20 million B round, they may be able to build a considerable lead.
The translation business is a big one, in the billions, and isn’t going anywhere. It’s simply too common a task to need to release a document, piece of software or live website in multiple languages — perhaps dozens.
These days that work is done by translation agencies, which employ expert speakers to provide translation on demand at a high level of quality. The rise of machine translation as an everyday tool hasn’t affected them as much as you might think, since the occasional Portuguese user using Google’s built-in webpage translation on a Korean website is very much a niche case, and things like translating social media posts or individual sentences isn’t really something you could or would farm out to professionals.
In these familiar cases, “good enough” is the rule, since the bare meaning is all anyone really wants or needs. But if you’re releasing a product in 10 different markets speaking 10 different languages, it won’t do to have the instructions, warnings, legal agreements or technical documentation perfect in one language and merely fine in the other nine.
Lengoo 20m started from a team working on automating that workflow between translators and companies.
“The next step to take obviously was automating the translation itself,” said CEO and founder Christopher Kränzler. “We’ll still need humans in the loop for a long time — the goal is to get the models to the level where’s they’re actually usable and the human has fewer translations to make.”
With machine learning capabilities constantly being improved, that’s not an unrealistic goal at all. Other companies have started down that road — DeepL and Lilt, for instance, which made their cases by showing major improvements over Google and Microsoft frameworks, but never claiming to remove humans from the process.
Lengoo raises USD 20m Series B from Inkef on AI Agency Investment Thesis
Lengoo 20m iterates on their work by focusing on speed and specificity — that is, making a language model that integrates all the jargon, stylistic preferences and formatting requirements of a given client. To do this they make a custom language model by training it not just with the customer’s own documents and websites, but by continually adding in feedback from the translation process itself.
“We have an automated training pipeline for the models,” said Kränzler. The more people contribute to the correction process, the faster the process gets. Eventually we get to be about three times faster than Google or DeepL.”
A new client may start with a model customized on a few thousand documents from the last couple years. But whenever the model produces text that needs to be corrected, it remembers that particular correction and integrates it with the rest of its training.
Today, this work is done by translation companies, who use professional speakers to provide high-quality, on-demand translations. The rise of machine translation as an everyday tool doesn’t affect them as much as you might think, because Portuguese users don’t often use Google’s embedded website translation on a Korean website. a niche and things like social media translations. individual messages or phrases are not really something you can or want to outsource to professionals.
The improvements have won over customers that were leery of over-automation in the past.
Kränzler admits: “At first there was resistance. “People are turning to Google Translate for translation every day, and they see the quality improve – they and DeepL have really educated the market. People now understand that if you do it right, machine translation will work. In case of business use, a large client may have 30, 40, or 50 translators and each has its own style… You can say we are faster and cheaper, but at the same time quality, in terms of consistency, also increased.
Although customizing a model with a client’s data is hardly a unique approach, Lengoo 20m seems to have built a lead over rivals and slower large companies that can’t improve their products quickly enough to keep up. And they intend to solidify that lead by revamping their tech stack.
The issue is that the translator-key AI’s feedback loop is constrained by the usage of more or less conventional machine learning techniques. The frequency of updates for a template depends on its use, but you won’t repurpose a massive template to add a few hundred words to the content. Retraining can only be carried out seldom because it is computationally costly.
Lengoo 20m plans, neural machine translation framework
But Lengoo 20m plans to build its own, more responsive neural machine translation framework that integrates the various pipelines and processes involved. The result wouldn’t improve in real time, exactly, but would include the newest information in a much quicker and less involved way.
“Think of it as a segment by segment improvement,” said applied research lead Ahmad Taie (segments vary in size but generally are logical “chunks” of text). “You translate one segment, and by the next one, you already have the improvements made to the model.”
Making that key product feature better, faster and easier to implement customer by customer is key to keeping clients on the hook, of course. And while there will likely be intense competition in this space, Kränzler doesn’t expect it to come from Google or any existing large companies, which tend to pursue an acquire-and-integrate approach rather than an agile development one.
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For professional human translators, this field will not replace them, but can increase their efficiency to a great extent, which can reduce the manpower there. But if the international market continues to grow and with it the demand for professional translations, they may well be able to keep up.
The $20M seed round, led by Inkef Capital, will allow Lengoo 20M to go to market in North American and other European markets and integrate into multiple business stacks. Joining the round were existing investors Redalpine, Creathor Ventures, Techstars (whose program stems from the company) and angel investors Matthias Hilpert and Michael Schmitt, and new investors Polipo Ventures and Volker Pyrtek.
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