Skimle
Qualitative data analysis with editable, source-traceable coding and mechanically verified verbatim quotes
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skimle.comAbout Skimle
Skimle is a qualitative-analysis workspace built to be defensible, which is a different design goal from most AI research tools. It collects, codes and categorises qualitative data across hundreds of documents using a workflow the vendor says distils 40+ years of academic and business qualitative-analysis practice, and every code, category and summary it produces remains editable and traceable back to the source text. Critically, it mechanically verifies that each quote it surfaces is 100% verbatim, which is the check that makes a finding defensible in a paper or a client deliverable rather than something you have to spot-check by hand. Inputs can be audio, transcripts, documents, open-text survey responses, or interviews collected through its own Skimle Ask AI-interview surveys; it transcribes, anonymises, combines sources and visualises patterns with mixed-methods support. Data is stored in the EU. Output exports to Word, PowerPoint, Excel and REFI-QDA, and connects to other AI tools over MCP. Published users span academic research, market research, and consulting, with case studies from the Finnish Academy of Science and the consultancy Noren, and the vendor reports 500+ researchers, consultants and analysts. A free masterclass and webinar series runs alongside the product, including a session with Professor Henri Schildt on using AI in qualitative research responsibly, which is consistent with the traceability-first design of the tool itself.
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