An enterprise research team needed scalable tools to analyze thousands of survey responses. We used LLMs to automate summarization, anonymization, and insights extraction—saving thousands of hours of manual UX research time.
UX Research, Survey Intelligence, AI Automation
LLM Applications, NLP, Research Workflow Optimization
An enterprise research team needed scalable tools to analyze thousands of survey responses. We used LLMs to automate summarization, anonymization, and insights extraction—saving thousands of hours of manual UX research time.
The client required automation to process, anonymize, and analyze large-scale survey responses.
Researchers spent extensive time reading and interpreting individual answers.
Sensitive data had to be removed while preserving context and quality.
Thousands of responses had to be converted into structured summaries for reporting.
We built an LLM-powered pipeline to extract structured, anonymized insights from unstructured survey data.
LLMs generated structured summaries from survey text with minimal human input.
Anonymization layer scrubbed names, emails, and other identifiers from responses.
Summaries were linked back to original responses for validation and context preservation.
Our solution drastically reduced the time to insight and enabled more accurate reporting for product teams.
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