FAQ

Skimle is an AI-powered qualitative data analysis platform that structures unstructured documents (e.g., interview notes, data room files, customer comments, public consultation statements, legal documents and so on) into organized, analyzable datasets. Unlike chatbots that retrieve random passages, Skimle systematically processes your entire document set—analyzing each section, extracting insights, and organizing them into hierarchical theme taxonomies with full traceability.

Skimle is built for knowledge workers who analyze qualitative data: academic researchers conducting thematic analysis, management consultants synthesizing expert interviews, policy analysts processing public consultations, market researchers analyzing customer feedback, legal teams reviewing discovery documents, and anyone who needs systematic, defensible insights from unstructured data.

Most simple AI analysis tools use RAG (Retrieval-Augmented Generation) to retrieve relevant passages at query time—meaning you get different answers each time you ask the same question. Skimle processes your entire dataset systematically upfront, creating a stable structure where every theme links to verified quotes. You get comprehensive coverage, consistent results, and full traceability instead of black-box answers. Like with humans - just reading the documents once and having a chat based on those is not analysis!

No. Skimle is designed for researchers and analysts, not programmers. The interface is intuitive (think spreadsheet + document viewer), and all AI processing happens automatically. If you can use Google Docs and Excel, you can use Skimle.

Most users are productive within 15-30 minutes. Upload documents, review AI-generated themes, refine categories—the workflow is straightforward. Unlike traditional qualitative analysis software (NVivo, ATLAS.ti) which take weeks to master, Skimle's interface is designed for immediate productivity.

Skimle accepts: PDF, .docx, TXT, RTF, audio files (MP3, M4A, WAV), video files (MP4, MOV), CSV, and can import from URLs. For audio/video, Skimle transcribes automatically. Note: some format import features still under development. You can mix formats in a single project.

Up to 1,000 documents per project. Most use cases involve 10-200 documents (typical research studies, consulting projects, policy consultations), but Skimle scales to handle large document sets like data room reviews or comprehensive literature analyses.

100+ languages. Upload documents in any language—Skimle analyzes them in the original language while creating unified theme structures. Particularly useful for EU-wide consultations, global market research, or multilingual academic studies. No translation required.

Yes. Upload English interviews, German reports, and Spanish survey responses in the same project. Skimle creates cross-language theme categories while preserving original-language quotes for verification.

The Skimle table is an interactive spreadsheet where each row represents a document (e.g., one interview) and each column represents a theme category (e.g., "pricing concerns"). Cells contain extracted insights with direct links to source quotes. This structure makes it easy to see patterns across documents, compare segments, and drill into specific topics.

Absolutely. Skimle's AI suggests initial categories based on your data, but you have full control to merge, split, rename, delete, or create categories from scratch. You can also provide your own category structure upfront (e.g., based on interview guide or theoretical framework) and have Skimle extract insights accordingly.

Two-way transparency means you can navigate both directions: (1) Click a theme to see all supporting quotes across documents, and (2) Click a document to see all themes extracted from it. Every AI decision is traceable—no black boxes.

Skimle's chat interface has awareness of your structured data (themes, categories, metadata). You can ask questions like "What do enterprise customers say about pricing?" or "How do opinions differ by region?" The AI answers using both the structured theme data AND original documents—giving you more accurate, contextual responses than generic RAG chatbots.

Word (comprehensive reports with themes, quotes, and summaries), PowerPoint (executive summaries with theme breakdowns), Excel (data tables with coding matrices) and JSON (for developers/API access - coming soon!). All exports maintain source attribution and traceability.

Yes. Skimle is built by academics for academic use. Our co-founder Henri Schildt is a professor with 30+ publications using qualitative methods. Skimle's approach is inspired by established thematic analysis (Braun & Clarke) and grounded theory (Gioia) methodologies—systematic, transparent, and reproducible.

Increasingly yes, with proper disclosure. Disclose your methodology transparently: "AI-assisted qualitative coding using Skimle, with manual validation and refinement." Skimle's full audit trail lets you document exactly how themes were derived, which satisfies peer review requirements for methodological rigor.

We provide suggested citation language upon request.

Yes. Export your coding matrix, have a second coder review a sample of documents manually, and calculate agreement (Cohen's kappa, etc.). Researchers can use Skimle for initial coding (90% of work), then validate with manual review (10% quality check).

Yes. Skimle supports inductive coding where themes emerge from data rather than being predefined. Start with open coding (Skimle suggests initial categories), then iteratively refine through axial coding (merge/reorganize categories), and develop theoretical frameworks. The process mirrors manual grounded theory but accelerates the mechanical aspects.

Every quote generated by AI is verified against source documents. If the AI creates text that doesn't exist verbatim in your documents, our system flags it and requests re-processing until verified quotes are provided. Additionally, the systematic structure means you can spot-check any theme by reviewing its supporting quotes.

Skimle's systematic processing analyzes every section of every document, reducing the risk of missed themes compared to manual coding (where fatigue and bias affect coverage). However, we recommend reviewing AI-generated themes critically. AI suggests, you validate.

Yes. The free tier lets you analyze a limited number of documents (up to 600 pages) to test the platform. Perfect for trying Skimle with a pilot project before committing to a paid plan. The reason for charging for larger sets of data is to pay for the AI processing capacity.

Document analysis consumes credits based on document length and complexity. One credit allows you to upload around 2,000 characters of text, roughly one page. Subscriptions include a monthly/annual credit allocation, with options to purchase top-ups if needed.

Pricing starts with a free tier for trial use. For the price of paid plans, check the Pricing page. Enterprise and institutional licenses available with custom pricing.

At the moment yes. We offer discount codes for students and academic researchers. A proof of academic status is needed.

Yes, anytime from your account settings. When upgrading, you're charged the prorated difference. When downgrading, the change takes effect at your next billing cycle.

You can purchase credit top-ups at any time, or upgrade to a higher tier. Your existing projects remain accessible, you just can't analyze new documents until you add credits.

Yes. We provide site licenses for universities, research institutes, consulting firms, and government agencies. Team licenses include collaborative features, centralized billing, data processing agreements, and volume discounts. Contact us for institutional pricing.

Yes. All paid plans generate invoices automatically. For institutional purchases requiring purchase orders or specific billing arrangements, contact our sales team.

Yes. Access is authenticated and logged. We follow industry-standard security practices and are happy to complete security questionnaires for enterprise customers.

All data is stored in the European Union (cloud servers in EU regions). Your data never leaves the EU, ensuring GDPR compliance and data sovereignty for European customers.

Yes, fully. We comply with all GDPR requirements: lawful processing, data minimization, purpose limitation, storage limitation, and data subject rights. We provide Data Processing Agreements (DPAs) for institutional customers.

No. Your documents and data are never used to train AI models. Your data remains private and is used solely for your analysis purposes.

Yes. You can delete individual projects or your entire account at any time from your settings. Deletion is permanent and immediate. We retain no copies after deletion (except for legal/accounting records like invoices).

Yes, for enterprise customers with specific security or compliance requirements. We can deploy Skimle in a dedicated cloud environment or discuss on-premises options. Contact us for details.

Researchers can include Skimle in their IRB/ethics applications under "data analysis tools" similar to how they'd mention SPSS or NVivo. We can provide documentation about our data handling, security, and AI processes to support your ethics applications.

Traditional tools require manual coding of every passage—systematic but time-intensive (weeks to months). Skimle uses AI to automate the coding process while maintaining the same systematic, transparent methodology. You get NVivo-quality rigor at ChatGPT-level speed. Additionally, Skimle's interface is more intuitive (no weeks of training needed) and cloud-native (no software installation).

Dovetail is designed for product teams doing lightweight UX research with AI features added on. Skimle is built from the ground up for rigorous qualitative analysis with academic-grade methodology. If you need to defend your findings in peer review, present to skeptical executives, or handle confidential data with GDPR compliance, Skimle provides the structure and transparency those situations demand.

You can, but you'll hit limitations quickly: (1) Inconsistent answers—ask the same question twice, get different responses; (2) No systematic coverage—might miss insights that don't semantically match your query; (3) No traceability—can't show where conclusions came from; (4) Limited context window—struggles with 20+ documents. For quick summaries of individual / a few documents, ChatGPT works. For systematic analysis, use Skimle.

For small projects (under 10 interviews), manual reading is perfectly fine. But as scale increases: (1) Time explodes—20 interviews = 40+ hours of manual coding; (2) Consistency drops—fatigue and bias affect later documents differently than early ones; (3) Pattern recognition suffers—hard to hold 50 interviews in your head simultaneously. Skimle maintains quality at scale while freeing your time for actual analysis instead of mechanical coding.