AI research platform for University of Amsterdam
Dual AI agents converting qualitative data to quantitative analysis, compressing months of academic research into minutes.
University of Amsterdam (UvA)
The University of Amsterdam needed an AI platform to handle large-scale qualitative research data across multiple languages and cultures. Two specialised agents were required: one for data conversion and another for complex analysis.
Vast qualitative datasets spanning multiple languages and cultural contexts made manual coding impractical. Researchers could not systematically compare findings across studies, and the sheer volume of data meant months of analysis before any insights emerged. Traditional methods could not scale to the ambitions of the research program.
What SevenLab built
SevenLab built a dual-agent AI platform. Agent 1 converts qualitative data into structured quantitative formats. Agent 2 enables natural language querying and complex cross-dataset analysis.
Qualitative-to-quantitative agent
AI agent that codes and converts qualitative research data into structured quantitative formats.
Natural language analysis
Second agent enables researchers to query datasets using plain language questions.
Cross-cultural comparison
Analyses data across languages and cultural contexts with consistent methodology.
Novel insight discovery
Identifies patterns and connections that manual analysis would likely miss.
Measurable business impact
Analysis that previously took months now completes in minutes. Researchers discovered novel insights that manual analysis would have missed, and the dual-agent approach established a new methodological standard for qualitative-quantitative research.
The richness of our qualitative data always presented a methodological challenge. The dual-agent platform turned that challenge into our biggest advantage.
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