Skimle for academic researchers
Academic rigour at the speed of AI
Skimle was built by academics for academics. Co-founder Professor Henri Schildt has decades of experience in qualitative research and articles published in leading management journals including Academy of Management Journal and Organization Science. He got tired of NVivo and MAXQDA's slow, clumsy workflows, and was worried by the way ChatGPT is being used for analysis that lacks transparency and comprehensiveness. So he built Skimle as the platform that replicates rigorous, transparent qualitative methodology, but with AI at the core.
Initial coding of interviews can take weeks manually. Skimle handles it without sacrificing methodological rigour, often catching quotes a human would miss. Every theme links to a verified verbatim quote. The full audit trail satisfies peer reviewers. Note: academic discounted plans available.
Rigorous analysis that lets you focus on your theoretical contribution
Skimle's approach is inspired by grounded theory (Glaser & Strauss; Strauss & Corbin) and Gioia's method. It produces the systematic, transparent analysis that peer review demands, without the weeks of mechanical coding that consume time better spent on theory development. Works in 100+ languages, handles up to 1,000 documents per project.
- Automated initial coding — every quote linked to its category
- Full two-way audit trail for peer review and methods transparency
- Codebook you control — merge, split, rename, reorganise categories freely
- Works in 100+ languages without translation
- Handles interviews, audio/video, books, documents, archival materials
- Skimle Academic - discounted plan - available
FROM WEEKS OF MANUAL CODING TO HOURS OF ANALYSIS
Conduct AI-assisted qualitative analysis of interviews
Research based on 40+ interviews should produce a rich theoretical contribution (for example for your PhD), but manual coding — highlighting, organising, re-coding, building trackers — consumes most of the available time and might still miss important patterns. Skimle reads every interview systematically and gives you a structured theme hierarchy with verbatim quotes attached, so you can spend your time on the theoretical interpretation that only you can provide.
How it works
- 1. Upload your transcripts
Drag in interview transcripts as PDFs, Word files, or plain text. Skimle accepts documents in any format and handles 100+ languages natively.
- 2. Describe your research question
Set your analytical focus, broadly or specifically. Skimle structures the initial coding around what you are trying to understand theoretically.
- 3. Review the initial code structure
Skimle produces a hierarchy of themes, each linked to verbatim quotes from the source transcripts. Nothing is invented. Every code traces to text.
- 4. Refine to your theoretical framework
Merge, split, rename, and reorganise categories to match your emerging theoretical model. Two-way transparency lets you verify which quotes support which codes at any point.
- 5. Export for your methods appendix
Export your codebook and supporting quotes as text or as open source REFI-QDA (.qdpx) format supported by legacy CASQDA programs like Nvivo, Atlas.TI or MAXQDA.
PROTECT PARTICIPANT PRIVACY BEFORE ANALYSIS BEGINS
Anonymise transcripts automatically
Interview transcripts contain names, affiliations, locations, and other identifiers that create GDPR and ethics compliance obligations before you can share data with collaborators, archive it, or include quotes in publications. Manual redaction is slow and inconsistent. Skimle Anonymise is a research-grade pseudonymisation and anonymisation tool — three compliance levels, six identifier categories, per-entity rules, cross-file consistency, and a full audit trail. Your ethics board gets the documentation it needs; your analysis can begin the same day.
How it works
- 1. Upload your transcripts
Upload PDF, Word, or plain text files. Skimle detects identifiers across six categories — names, titles and roles, locations, organisations, dates, and other — and highlights them across every document in your corpus.
- 2. Choose your anonymisation level
Three preset levels: light pseudonymisation (direct identifiers replaced, key retained), strong pseudonymisation (indirect identifiers also covered), or strong anonymisation (key destroyed — intended for HIPAA compliance). Each category can be tuned independently.
- 3. Review and refine
Review flagged passages, adjust per-entity rules, merge duplicate entities, and add custom redaction rules in plain language. Cross-file consistency ensures the same person always gets the same pseudonym across your full corpus.
- 4. Export with audit documentation
Export anonymised files with a PDF audit report and Excel translation table — the documentation ethics boards and peer reviewers ask for. Anonymised transcripts feed directly into Skimle's thematic analysis workflow.
FROM AUDIO FILE TO CODED TRANSCRIPT IN MINUTES
Transcribe and analyse interview audio securely
Converting field recordings into usable transcripts is either expensive and slow through a third-party service, or prohibitively time-consuming to do manually — both with real confidentiality risks. Skimle transcribes audio and video directly within its secure, EU-hosted environment, so you get a coded transcript ready to review the same day without the data ever leaving GDPR-compliant infrastructure.
How it works
- 1. Upload the audio or video file
Upload MP3, M4A, WAV, MP4, or MOV files directly. No external transcription service required. Everything stays within Skimle's secure environment.
- 2. Automatic speaker identification
Skimle identifies speaker changes throughout the recording and labels them in the transcript. No manual formatting required.
- 3. Review and correct the transcript
Skimle's transcription handles accents and varying audio quality. Review the output and make corrections before analysis begins.
- 4. Analyse alongside other sources
Once transcribed, the interview sits in the same project as your other documents and is analysed using the same systematic process.
- 5. Secure deletion of audio files
After transcription, audio files are securely deleted. Only the transcript remains on EU-hosted infrastructure — fully GDPR-compliant.
FAQ
Frequently asked questions
- Can I cite Skimle in my research?
- Yes. Describe 'AI-assisted qualitative coding software' in your methods section, similar to how you would cite NVivo or MAXQDA. We have a methods article in peer review that will provide a formal citation. In the meantime, we can provide suggested citation language on request.
- Will peer reviewers accept AI-assisted analysis?
- Increasingly, yes, with proper documentation. The key is transparency about your process. Skimle's audit trail gives you exactly what reviewers expect: a documented, systematic coding process where every theme traces to specific quotes. The methods section writes itself.
- How does Skimle compare to NVivo or MAXQDA?
- NVivo and MAXQDA are excellent tools for researchers who have weeks for analysis and institutional licences that cover the cost. Skimle automates the mechanical coding phase, reducing what takes weeks to hours, while maintaining the same systematic and transparent methodology. The learning curve is minutes rather than weeks. Many researchers use both, Skimle for the initial coding pass, NVivo for mixed-methods integration.
- Does Skimle work for grounded theory?
- Yes. Skimle supports inductive coding where themes emerge from the data rather than being imposed in advance. You can start with a broad analytical focus and let the category structure develop iteratively, which maps closely to open coding in grounded theory approaches.
- Is my research data secure?
- Your transcripts are processed securely on EU-hosted infrastructure and never used to train AI models. We provide Data Processing Agreements for institutional ethics requirements and can accommodate most IT security requirements. Contact us if you have specific security requirements.
- Can I get a discount as an academic?
- Yes. Academics with a valid institutional email address and affiliation page can buy discounted Skimle Academic package. Contact us for institutional licensing if you need team access for a research group or lab.
- How many documents can I analyse in one project?
- Skimle supports up to 1,000 documents per project, comfortably covering interview-based studies with 50 to 100+ participants, and archival studies with large document corpora. For studies requiring more capacity, contact us to discuss institutional arrangements.
About Skimle
Built by academics for academics
Skimle is built by academics for academics, and by business professionals for business professionals. Co-founder Professor Henri Schildt has published in Academy of Management Journal, Organization Science, and Strategic Management Journal, and has spent two decades doing qualitative research the hard way. Co-founder Olli Salo is a former McKinsey Partner who conducted over 1,000 client interviews. We built Skimle because we needed it ourselves.
We are trusted by Finnish government ministries, over 30 universities, dozens of consulting and market research firms, and large companies. All data is stored within the EU and processed according to our strict GDPR policy and terms of service.
Want to learn more? Explore our Signal & Noise blog, our FAQ, and use cases by sector. We are also happy to demo the product or explore how Skimle could fit your needs.