Open ends, transcripts & qualitative coding

Code open ends without losing rigour.

Code open ends, interviews, and transcripts into themes, then review the assignments before using them in the wider research workflow. Start from your own codebook or ask AI to propose one with custom instructions; generated codes include descriptions and verbatim examples you can edit before applying.

Keep the researcher in control of the codebook.

The core loop is Code → review → trace to verbatims. Review individual records, correct mis-codes, merge or split themes, and re-apply the codebook across the dataset. Optional topic-sentiment adds polarity at the theme level.

Keep every theme connected to what people said.

Every theme remains linked to the source phrases that support it. The reviewed themes can then become variables for quantitative analysis, preserving an evidence path from codebook to verbatim rather than treating AI coding as a black box.

Scale the repetitive part without outsourcing the judgement.

Manual coding gives researchers control but becomes difficult to sustain across large or recurring text volumes. Blind automation is faster but leaves the codebook and its decisions hard to review. AddMaple puts the researcher between those extremes: AI accelerates application across the dataset, while the team decides what a theme means, which records belong to it, and whether the evidence supports the interpretation.