// CASE 06 · TRANSLATED · MATESUB

Before the AI writes the subtitle, watch who writes it today.

Translated serves 180,000 clients in 177 languages. Matesub was going to automate the slowest parts of subtitling and dubbing with AI. Before designing a single screen, we went to see how subtitlers in more than ten countries actually work.

180K

clients Translated serves

177

languages in its catalogue

10+

countries of the professionals we interviewed and shadowed

web-based subtitling tool informed by the research

01

Executive summary

AI could cut the time and cost of multilingual subtitles, which means more video content available in more languages. But subtitling is a craft with its own rhythm, tools and pain, and a tool that ignores it gets ignored. We mapped the industry as a system, followed professionals through their real workflow, interviewed them, and turned what we saw into a set of recommendations, journeys and UX prototypes that shaped the first web-based version of Matesub.

02

Four phases

➔ System map of the subtitling industry: actors, tools, handoffs

➔ User interviews and shadowing with subtitlers in more than ten countries

➔ Customer journey mapping of the as-is workflow, with the pain points

➔ User flows and wireframes for the prototype of the web tool

Tools & tactics

System mapping

Remote interviews and shadowing

Journey mapping

Wireframing and prototyping

Automate the wait, not the judgement.

The slow parts of subtitling are the ones nobody wanted to keep. The judgement calls are the ones nobody wanted to lose.

03

Issues discovered

➔ The workflow was split across several tools, with manual handoffs between them.

➔ Timing and segmentation ate most of the hours and none of the skill.

➔ Quality checks lived in people's heads, not in the tool.

➔ Multilingual jobs multiplied the same manual steps per language.

➔ Professionals distrusted automation that hid what it had changed.

Four principles for AI tools

Automate the repetitive, keep the call

Show what the machine changed

One workspace, no handoffs

Design for the professional first

04

Results

Translated got a full research report, journey maps and UX prototypes, and used them to inform the development of its web-based subtitling tool; Matesub then shipped as a product. What I cannot claim: I do not have adoption numbers, and the product evolved past our prototypes. What stays: this was 2020, and the question of how people trust an AI that rewrites their work was already the design problem. It is the same question I now put to every AI feature, whether it writes a subtitle, a listing or a page.

05

Lessons learned

➔ Research in more than one country prevents designing for the one workflow you know

➔ Professionals accept automation when it shows its work

➔ A system map finds the handoff nobody had noticed

➔ AI products are still products: the craft around them is the design

// Building an AI tool

for professionals?

Let's go see how they work before we decide how the machine should. Reach out for UX research, or book an Ekitai Sprint to map the workflow in five hours.