Open Data: Gengo by the numbers

Back in 2008, we were a much smaller company. We rang in 2009 just shy of 50 translators who had completed 3,405 words total. Almost six years later, we’re at an incredible 10,800+ translators and over 200 million words translated. We’re charging forward and we have all of you to thank.

To show how the world is communicating freely through Gengo, we’re introducing our Open Data initiative, which will give users more insight into our translation speed, scale and quality. We strongly believe in the power of our platform and want for you to, too.

Our first step in this direction is sharing core stats on our home page, including:

  • Words translated: over 210 million
  • Qualified translators: 10,822
  • Rate of translation: 4 words per second—six times faster than the average person can type

In the near future we’re sharing more information across the site to show how things are working behind the scenes. Stay tuned!


Jay Trinidad

The author

Jay Trinidad

Jay is Director of Business Operations at Gengo. Previously, Jay was Head of Consumer Marketing, Strategy and Operations for Google Asia Pacific and has worked around the globe in cities like New York, Singapore, Hyderabad, Manila, Seoul and Beijing. He currently resides in Tokyo.

  • Daniel Harper

    Great to see more Gengo data being shared. Having done some work with UK government on Open Data, the public sector benefits are 1) greater accountability and 2) driving growth by releasing more ‘raw material’ data to businesses. There’s definite value in making Gengo more transparent and open to customers, and I’m intrigued to see how/if 2) can apply to us as well!

  • Henry Bedenian

    Dear Jay,
    I’m so proud to be in Gengo global family and would like to know any new information of the future development.
    I wish you and Gengo all the success and prosperity.
    Henry G. Bedenian

  • Great to hear you’re willing to share data with your customers and the community! It’d be really useful for customers, developers and the community to get some insight into average turnaround times (and standard deviation) for the individual language pairs. You currently show a rough estimation in the order form, but it’d be a great feature enhancement if you would refine this to include factors like time of day and how many open jobs are in the system. Also, that’d be valuable information for your API.

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