Our users enjoy access to all the key, live tender opportunities from around the world.
We have hundreds of thousands of global, live contracts and tenders across over 100 languages at any one time.
It comes as little surprise then, that a common discussion we have with our users centres around language and translation.
This conversation typically begins with a question of whether all the data can be translated.
That’s certainly an option, but it does not scale too well: machine learning translation tools, whilst effective, are expensive.
Moreover, the issue is compounded for countries with several official languages, such as Belgium.
Nor does this option really work for snippets of the data. Many tender notices lack titles, and a snippet often loses context, especially for larger projects.
It was these conversations that prompted us to look for a better way.
We created a machine learning script that can take a proven classification (such as UNSPSC, SIC, or CPV) and automatically apply labels for any tender or contract, regardless of language across hundreds of thousands of global notices.
This means that users can search across the classification first in their native language (such as ‘car’, ‘macchina’, ‘voiture’, ‘машина’, ‘汽车’, or ‘Wagen’) and they will receive all the notices that fit their chosen category group across all languages. From there, translation options can be deployed, whether it’s a snippet of data or the full notice.
This can even be automated, with alerts sent whenever new alerts from a chosen category such as ‘software services’ or ‘clean energy’ are published.
The end result is that our users enjoy a significant, competitive advantage for global bidding at minimal cost.
If you would like to know more or are interested in a demo, please get in touch