UX & Product Design
AI-Driven UX Research - Automating multi-language process analysis
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ABOUT THE PROJECT
We developed a custom AI GPT solution to automate and analyze multi-language UX research processes for a global corporation.
We supported international design research for a global partner using a dedicated artificial intelligence assistant. The project involved processing data from a UX interview series conducted across 7 different countries in 7 different languages. The developed GPT was capable of directly handling and structurally interpreting transcripts of spoken interviews as well as written feedback in various languages. The goal of this software solution was to create accurate, realistic, and immediately actionable summaries for product development teams from a heterogeneous, multi-language database on a single, unified platform.
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THE PROBLEM
Manually analyzing UX interviews across 7 countries and languages would have been extremely time-consuming, tying up expert resources for weeks.
During user research covering an international market, language barriers and massive data volumes present the greatest challenges. According to traditional methodology, it would have taken a UX designer at least two weeks of intensive manual work to review, organize, and synthesize spoken and written interviews collected in 7 different languages. This slow process would have significantly delayed product development. Furthermore, manual processing carried the risk that important customer experience data or hidden insights would be lost among unorganized notes due to cultural and linguistic nuances.
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THE SOLUTION
The custom AI GPT synthesized multi-language data instantly, replacing two weeks of manual work for the design team.
The deployed AI assistant immediately analyzed spoken interview transcripts and written feedback across 7 languages. The GPT performed complex process analysis in moments, pinpointing critical pain points in user journeys with high accuracy. The system generated a unified, realistic, and easily digestible synthesis of the feedback. With this technology, we effectively replaced at least two weeks of monotonous, manual data analysis by a UX designer. The team saved valuable time, allowing them to shift focus away from unnecessary administration and immediately toward solving validated problems and driving product development.
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THE RESULTS
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