What if we do not have a clean TM or term base?

That is not a problem at all. The workflow can start completely from scratch. If you don’t have existing translation assets, we can easily bootstrap the process using two highly effective methods.

First, we can use the “gold standard” sample approach, where your native linguist translates a small, initial section of the project. I then use that approved human sample to fine-tune the prompt, aligning the AI’s style, tone, and terminology choices with your translator’s preferences for the remaining files. Alternatively, we can use an iterative feedback loop for multi-file or rolling projects, where the workflow learns as it goes. Once your translators post-edit the first batch of files, we feed those corrections back into the prompt to improve the quality of the next delivery. Furthermore, for smaller projects without rigid pre-defined specifications, we can simply let the frontier model run without heavy customization and still deliver an excellent, internally consistent draft that is immediately ready for post-editing.

In other words, you don’t need legacy data to get started. This is a highly adaptable pipeline designed to build your linguistic assets from day one, rather than requiring you to bring them to the table.