Overwhelming Volume
50+ interviews, 500+ pages of transcripts. Where do you even start?
AI-powered QDA software that codes interviews, identifies themes, and
Code and analyze qualitative data 10x faster with AI assistance
Hundreds of hours of interviews and observations need systematic analysis
50+ interviews, 500+ pages of transcripts. Where do you even start?
Different researchers see different patterns. Inter-rater reliability is painful.
Code, recode, recode again. Themes emerge slowly through manual sifting.
Human cognitive limits prevent seeing subtle patterns across hundreds of pages.
Existing QDA software has steep learning curves and clunky interfaces.
Multiple coders working separately leads to inconsistent analysis.
Accelerate analysis while maintaining methodological rigor
AI suggests codes and themes while preserving human interpretation
Analyze interviews, observations, documents, and media simultaneously
Follow established qualitative methodologies with AI acceleration
AI flags potential researcher bias and suggests alternative interpretations
Upload transcripts, documents, and multimedia data
Collaborate with AI for initial coding and pattern identification
Develop and validate themes with AI pattern analysis
Cut my coding time in half while finding patterns I would have missed. The AI suggestions opened up new theoretical directions.
Finally, QDA software that actually understands how qualitative research works. The grounded theory support is incredible.
No. The AI assists with pattern detection and coding suggestions, but you maintain complete control over interpretation and meaning-making. It’s designed to enhance, not replace, qualitative researcher expertise.
The AI follows established qualitative methodologies like grounded theory, supports triangulation, provides audit trails, and includes reflexivity prompts to maintain methodological integrity.
Yes. Supports grounded theory, phenomenology, ethnography, case study research, and other qualitative approaches with methodology-specific guidance and analysis frameworks.
Enterprise-grade security with encryption and strict privacy controls. Data is never used for AI training, remains completely confidential, and meets IRB and ethical research requirements.
Supports 50+ languages for transcript analysis and coding. The AI understands cultural contexts and language nuances important for qualitative research across different populations.
Full collaboration features with individual coding, inter-rater reliability tracking, disagreement resolution, and shared interpretation development.
The AI flags potential confirmation bias, suggests alternative interpretations, and prompts reflexive thinking about researcher positionality and assumptions.
Accelerate discovery while maintaining research integrity