Add text to get the translation that 22 AI models agree on
We're building an AI that picks our translators, here's how it works
When a new translation project lands at Tomedes, a project manager has to answer one question before anything else: who is the right translator for this job? It sounds simple. It is not. That single decision draws on years of accumulated knowledge about thousands of vendors — who delivers on time, who struggles with legal content, who has handled this client before, who is already at capacity, and who showed up at midnight when we needed them to.
Industry analysis suggests that project managers at translation companies typically spend 10–20% of their time on manual coordination tasks that a proper system would automate. At Tomedes, we decided to build that system ourselves — not an off-the-shelf TMS vendor picker, but an AI tool trained to replicate the judgment of an experienced project manager. This post is the honest account of what we have built so far, what we have deliberately not automated, and where we are going next.
Why vendor assignment is harder than it looks
Tomedes works with translators and linguistic specialists across more than 270 languages. For any given project, there may be dozens of vendors who are technically qualified. The real question is not who can do this job, but who should do this job right now, for this client, given everything we know.
A CRM record tells you the basics: language pairs, specializations, rates, last active date. What it does not tell you is whether this vendor pushed back on a tight deadline last month, whether they have delivered for this specific client before and built a rapport, whether they consistently deliver clean files that go straight to QA or files that require a second pass, or whether they are currently mid-project and unlikely to take on new work.
That gap between what the system knows and what an experienced project manager knows is exactly the gap we are trying to close. Without robust vendor intelligence, project managers have no standard way of evaluating translators beyond what's in the system — leading to repeated manual coordination by email and instant message, and an increased risk of losing critical context.
What data a project manager actually uses to decide
Before building anything, we spent time mapping out the actual decision process a Tomedes project manager follows when assigning a vendor. The list was longer than expected.
The quantitative signals are the easier ones: total projects completed, delivery on-time rate, last delivery date, active project count, language pair, subject matter specialization, client history, and rate. These live in the CRM and are relatively straightforward to surface.
The qualitative signals are where institutional knowledge matters most. These include how a vendor communicates when there is a problem, do they flag issues early or go quiet? How they negotiate when scope expands mid-project. Whether they have relevant certifications such as ISO 17100 or domain credentials for legal, medical, or technical content. Whether they have worked for this particular client before and whether that relationship is positive. Whether they are responsive on weekends for urgent projects.
These signals do not live in a CRM field. They live in email threads, in the notes a project manager writes after a difficult project, in the pattern of a vendor's replies over three years of working together. This is what we needed the AI to read.