Lionbridge Acquires Gengo, a Tokyo-Based Translation Company

Two language services providing giants unite

A swarm of letters in black-and-white

Last week, on 16 January 2019, Lionbridge has announced its acquisition of Gengo for undisclosed sum. While Gengo is still sometimes referred to as startup, this Tokyo-based company has firmly established itself of the language service market several years ago, and can name AirBnb, Amazon, Youtube, and Rakuten as their customers along with other well-known corporations. In this article we would like to track the history of Gengo from the early days to its acquisition by Lionbridge.

The emergence of Gengo gives us a great insight into the current language situation in Japan. One of its creators and CEOs, Matthew Romaine, used to work for Sony Corporation. And while he was primary employed as an audio research engineer, his bilingual abilities in English and Japanese were put into use for translations inside the company.

Having the insight into the translation and localization process, Matthew Romain and Riber Laing started the translation company in 2008 and called it myGengo. The principle behind their business model is quite simple. The platform recruited translators around the world. By assessing their skills during the recruitments process myGengo created a pool of qualified professionals to deliver high-quality translations that was later also rated and commented on by the clients.

Gengo has grown its network of certified translators to over 21,000 people who work with 70 language pairs.

While automation is growing in many areas of production, and AI is sophisticated enough to recreate a meaningful communication with a human, we are still far away from fully outsourcing the translation and localization process to machines. And we do still need professionals to go over automatically translated texts to edit and verify the correctness of the final product.

Thus, myGengo was answering the demand of landing highly-skilled translators for companies that could not afford or did not need an in-house translator on a full-time basis. In 2010 Gengo launched API that made it possible for developers to integrate the translation platform into websites, applications, and widgets. Later on, in 2012, rebranded itself into Gengo.

It has grown its network of certified translators to over 21,000 people that are working across the globe and time zones. The company provides translation for over 70 language pairs and claims to process more than 5 million words weekly while sustaining a competitive price per word.

Before the acquisition by Lionbridge, Gengo has raised about USD 25.6 million in funding, including major contributions of USD 5.3 million from 500 Startups and Atomico in 2011 in series A, USD 12 million in Series B from Intel Capital in 2013, and USD 6.8 million in 2015 during Series C from Recruit Co. Ltd. In 2018, these investments allowed Gengo to engage with the machine learning introducing a platform that supplies a multilingual training data on demand for machine learning developers. This data helps to improve chatbots, text categorization, data annotation, and the machine translation quality.

“Gengo is a key acquisition for us that immediately complements our core strategic initiatives,” states John Fennelly, Lionbridge CEO in the announcement of acquisition. “It will accelerate our ability to penetrate new markets in the artificial intelligence space; it bolsters our human capital pool by bringing an extremely talented team assembled by Matthew Romaine into our company; and Gengo’s advanced technology platform will become a key part of our localization delivery system. In addition to those very tangible benefits, we’ll add to our already formidable capabilities in Asia, and this will give us a deeper set of technology tools to increase our market share in the local games and life sciences markets.”

Matthew Romaine and other team members of Gengo will take the key leadership roles in Lionbridge after the transaction.

Today’s “otsumami” – a bite size snack:

Translation is still a challenge to AI and needs a human expertise to be done properly.

What do you think?

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