Context
Qualitative researchers spend 40+ hours per project manually coding survey responses and interview transcripts. That means reading every quote, tagging themes, and reassembling insights into a synthesis report. It’s tedious, slow, and the most valuable part of the work (pattern detection) is the part humans are weakest at.
Every existing tool I evaluated solved this with keyword search or lexical tagging. But themes in qualitative data are semantic, not lexical. A respondent saying "I feel lost" and another saying "the navigation confused me" are expressing the same insight and share zero keywords. TF-IDF misses it. String matching misses it. Even "smart search" misses it, because it was never designed to detect meaning.
I built Evidnc as a live, usable product rather than a prototype, because researchers evaluate tools by running their real data through them, not by imagining what a tool could do.
The problemQualitative research synthesis is manual, slow, and blind to semantic similarity. Researchers need pattern detection, not better organization.
Where it hurt
- 40+ hours per synthesisManual coding of transcripts, theme-building, and report assembly dominates the researcher’s week.
- Keyword search misses meaningExisting tools rely on lexical match. Semantically identical quotes go uncounted because they share no words.
- Black-box AI is worseOff-the-shelf LLM summarizers produce confident answers with no traceable evidence. Researchers can’t defend findings to stakeholders.
- Context switching kills flowResearchers jump between transcript tool, spreadsheet, Miro board, and doc. Evidence lives in four places, synthesis lives nowhere.
Anirudh
