Most people will use AI-powered translation when the stakes are low, such as understanding a single phrase or quote. When it comes to creating complex documents for an enterprise — Langoo might be just what you need. With the new $20 million B round, they might be able to gain a major advantage in the market with their deep customization of machine translation models and data sets.
The translation business is a multi-billion dollar industry that is here to stay. It’s not likely to be going anywhere anytime soon. After all, there are so many documents, pieces of software, or live websites that need to be translated in order to reach an international audience.
These days, translation agencies don’t spend all their time translating from English to other languages. These translation experts work with translation on demand to provide a high-quality service. Much like machine translations, which have been used by internet users for years, this agency has seen little to no impactful competition in their business. It’s been a slow process of change – much slower than you might think. They’ve survived by focusing on long-term quality and dedication to customers rather than providing lower-quality translations we could farm out later.
The best way to ensure your products are available in all markets is to translate them to the language of each community you want to sell in. If you can’t recruit or hire a translation service, find someone who can do some of your translations on the cheap.
Langoo was born out of the desire to make automated workflows between companies and translators easier.
“The next step for us was clearly to automate the translation process,” says Chris Kranzler, CEO and founder .“We still need humans in the loop for a long time. The goal of our models is to allow them to be usable with fewer translations needing to be done by humans.”
In the future, writing will evolve into unintelligible patterns of symbols. It may not happen in my lifetime, but I’m confident it’ll happen eventually. That’s why other companies are starting to take matters into their own hands before it’s too late. DeepL and Lilt are two examples – they showcased significant improvements to Google and Microsoft frameworks, using machine learning as a core component of their work.
Over the years, Langoo has developed a revolutionary approach to artificial intelligence, concentrating on speed and specificity. This amounts to a language model that not only incorporates your documents but also those of your competitors while you’re translating. This is made possible by their custom language models, which they continually improve by incorporating feedback from the translation process itself.
From inception to improvement, the machine learning feedback process has many components. This illustration shows one of the many stages.
Self-improvement is a process that can start with incremental changes and lead to significant personal growth. This fanciful representation of the model process shows the potential for self-improvement.
Machine learning and artificial intelligence have made a huge impact on text generation.
After 30 iterations, the segments requiring no additional corrections have doubled and the ones needing just a few are substantial.
Objectively measuring the quality of a translation can be difficult, but in this case, it’s not an issue. A human translator works as the tool because they do it automatically, which is called a “quality check. ” The fewer corrections, the better the translation and the faster it will be made. That’s why quality and speed have objective measurements.
Google’s recent improvements have won over customers who were leery of automation in the past.
Kranzler admitted that there was resistance at first, but people are starting to understand Machine Translation. “In an industry like ours, Google Translate is our competition and it’s getting better,” he explained. The argument follows that if you do it right, Machine Translation works in the professional environment. Apparently, a big customer has over 50 translators who each have their own style. It may seem like DeepL can’t offer the same consistency as a number of translators though they’re quicker and cheaper.
Lengoo has a competitive advantage over other companies and has been able to maintain a lead against its competitors. They intend to solidify that lead by revamping their tech stack.
However, these machine-learning technology models have one major drawback: they rely on more or less traditional machine learning technology and do not provide the translator-AI feedback loop. That means they can’t be updated quickly as they don’t get much use, but to retrain a large model is expensive computationally. The model has to be trained sporadically, and that’s only done when you need to integrate more content into it.
Lengoo plans to build its own neural machine translation framework that integrates various pipelines within the process. Unlike current models, the new product would not incorporate new data in real time, instead, it would do so much quicker and with less work involved.
According to Ahmad Taie, an applied research lead: “Think of it as a segment-by-segment improvement. Once you translate one segment, you have improvements made to the model by the next.”
Making key product features better and faster for customers is a win-win situation. Of course, there will be competition, but as Kränzler pointed out, companies usually compete with an “acquire-and-integrate” approach rather than “agile development”.
Despite what some may say, it doesn’t seem like the field of human translators will be phased out anytime soon. Ultimately, the only thing that might happen is that they’ll be more effective because artificial intelligence can carry out the mundane parts of their job. But if the demand for translation continues as it has been, it’s possible they’ll continue to grow and evolve in order to stay relevant.
Final Words
The $20 million round of funding, led by Inkef Capital, will help Lengoo grow its presence on a global scale. This is thanks to the additional European markets as well as North American market access granted through this investment. The investment also allows for Lengoo to integrate with more enterprise stacks, thus providing the best possible service for the widest range of customers. Notable investors Redalpine, Techstars (out of which Lengoo originated), Creathor Ventures, and investors Matthias Hilpert and Michael Schmitt were joined in the round by new investors Volker Pyrtek and Polipo Ventures.
FAQ:
Q1. What benefits does Langoo provide over conventional translation firms?
Ans. Langoo has a number of benefits over conventional agencies. Faster and more accurate translations are made possible by its usage of AI and machine learning.
Q2. How does Langoo make sure that its translations are accurate and of high quality?
Ans. Langoo guarantees quality and accuracy by combining AI technology with human knowledge.
Q3. What advantages come with translating products for global markets?
Ans. Businesses can expand their customer base and market share by translating their products for global markets.
Q4. What is Langoo and how does it differ from other translation companies?
Ans. Machine translation algorithms and data sets driven by AI are used by the translation company Langoo.
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