Skimle is an AI-assisted qualitative analysis platform that automatically codes, categorizes, and summarizes unstructured data across hundreds of documents. The platform provides full two-way transparency with every code and category traceable to verbatim source text, and includes an AI interviewer for conducting interviews at scale. It supports 100+ languages, handles up to 1,000 documents per project, and offers MCP integration for connecting to tools like Claude Code and Cursor.
Funding
Funding not disclosed
Founders
Product
Problem
Qualitative researchers face a fundamental trade-off between the scale of quantitative research and the depth of insights from qualitative analysis. Manual coding of interviews, documents, and other unstructured data is extremely time-consuming, often taking weeks for large datasets, while existing AI tools are often shallow, unpredictable, and lack the transparency needed for rigorous academic or business analysis.
Solution
Skimle provides an AI-assisted qualitative analysis platform that automatically collects, codes, and categorizes qualitative data across hundreds of documents using a workflow distilled from 40+ years of academic and business qualitative analysis experience. The platform offers full two-way transparency, ensuring every code, category, and summary is editable and traceable to verbatim source text, with mechanical verification that quotes are 100% accurate. Skimle includes an AI-assisted interviewer that conducts interviews at scale, transcribes and anonymizes audio/video recordings in a GDPR-compliant EU-hosted environment, and produces structured theme hierarchies with verbatim quotes attached. The platform also supports mixed-methods analysis by attaching metadata to documents and segmenting themes by variables like cohort, role, or condition.
Target Audience
Primary users are academic researchers conducting literature reviews, thematic analysis, or grounded theory studies, as well as consultants, policy analysts, and market researchers who need rigorous qualitative analysis of interviews, documents, and open-ended responses.
Features
- Automated initial coding with every quote linked to its category and traceable to source text
- Full two-way audit trail for peer review and methods transparency, with mechanical verification of verbatim quotes
- AI-assisted interviewer (Skimle Ask) that drafts interview guides, conducts interviews at scale, and analyzes responses for emerging themes
- Built-in transcription and research-grade pseudonymisation with six identifier categories (names, titles, locations, organizations, dates, other) and cross-file consistency
- Works in 100+ languages without translation and handles up to 1,000 documents per project including interviews, audio/video, books, and archival materials
- Metadata attachment and cross-tabulation for mixed-methods analysis, quantifying theme prevalence across sample segments
- MCP (Model Context Protocol) integration allowing connection to Claude Code, Cursor, Windsurf, and other AI tools with read and write access to project data
- Agentic Chat panel within the platform for AI-assisted exploration, category merging, and pattern discovery with full traceability