• What is Skimle?
  • Releases
  • Pricing
  • Contact
Sign In

AI-Powered Document Analysis

© Copyright 2025 Skimle. All Rights Reserved.

Skimle
  • About
  • Contact
  • Terms of Service
  • Privacy Policy

What is Skimle?

What does Skimle do?

Skimle draws inspiration from social science methodology to leverage AI in an entirely new way to supercharge your knowledge work. Our iterative process turns your body of documents, such as interview transcripts, call audio files, group interview video tapes, reports, statements, data room presentations, or contracts into structured knowledge, helping you identify the insights that matter the most.

Think of Skimle as your tireless research assistant. You define what types of insight you want to extract from your document, or let the AI suggest based on the contents of the documents themselves. Insight types could follow your interview guide, your desired output topics (for example, market characteristics, competitors, key purchasing criteria and so in a Due Diligence) or more loosely specified criteria such as pro's and con's mentioned. When you upload the documents, the Skimle AI workflow processes your documents thoroughly to identify relevant insights and categorize them into a parsimonious and comprehensive category scheme from your data. The categories and subcategories essentially synthesize observations across your documents, helping you move from individual documents, such as what one interviewee told you, to more abstract questions, such as what all interviewees say about a specific issue.

Process depiction of Skimle: moving from raw document text to insight with quotes to an abstract category structure.

Skimle's main view is the interactive "spreadsheet" that allows quick navigation from broad categories to individual insights and the content of documents. This Skimle table represents each document as a row, and each category as a column. This type of data structure is familiar and easy to work with for humans and allows zooming into individual categories (including the underlying quotes) as well as showing what all insights were extracted from each document. You can sort, filter, highlight, change columns and perform other intuitive tasks.

Display of Skimle spreadsheet view

Clicking the magnifying class icon on any quote immediately takes you to the context of original document, helping you zoom into the raw materials. Even though AI is fairly good at analyzing and grouping content, the design philosophy of Skimle is always to keep the expert in the driver's seat!

Document view

Skimle features a highly customized chat interface that leverages the abstract knowledge structure and behind-the-scenes reasoning process to answer your queries. Our chat does not only draw on the original document materials like all widely available "RAG Chatbots" (Retrieval Augmented Generation), but also utilizes the insights and the categorization scheme to serve you a superior, structured response. The Skimle table also makes it easy and fast to export the insights into a structured report - be it a Word document showing what has been said about each theme, or a Powerpoint presentation with an executive summary and selected quotes per theme. Our unique Tree view allows you to dig deep into categories for analysis. We are working on API and MCP access as well to allow agents and other software to tap into the powerful structure provided by Skimle.

Try it free →

How do we compare?

If you are an expert needing to analyze and synthesise large sets of qualitative information, you have essentially four different approaches to choose from

Ad HocRigorousAd Hoc + AISkimle
Cursory analysis based on reading through materialsIdentify categories and tag text manually or with NVivo, MAXQDA etc.Feed documents directly or via RAG to an LLMAI-assisted workflow combining academic rigour with speed
Rigorous−✓−✓
Transparent−✓−✓
Fast✓−✓✓
Versatile✓−−✓

"Ad hoc" is when you glance through the reports to check for anything surprising, write a brief summary and maybe throw a word cloud to impress. There is nothing wrong with the approach and due to time constraints and cost pressures, many business professionals had to take this route before AI tools were available. As a result, qualitative analysis is often seen as a second class citizen compared to crunching the numbers.

"Rigorous" is what academic researchers and serious analysts do when facing large sets of qualitative data. It is based on using previous generation tools like NVivo, MAXQDA, ATLAS.ti or bespoke coding systems to manually go through each interview to create the transcript, analyze themes, categorize each quote and adjust the categories iteratively. This results into publishable quality research, but often takes months or even years for any sizeable dataset.

"Ad hoc + AI" is the new kid on the block, used by tools like NotebookLM, many domain specific workflow tools (e.g., Harvey, myjunior, Ailuyze and Dovetail) and in custom RAG (Retrieval Augmented Generation) workflows, and users who upload all the documents to the prompt of a standard tool like ChatGPT or Claude. The documents are either fed directly to a Large Language Model, or stored in a searchable vector database from which the right ones are selected for each query. This approach works nice in demos, but developers and users are increasingly seeing its limitations: Expert level insights depends on analysing and structuring the data, not just trying to search embeddings at runtime.

Why do simplistic one-shot and RAG based approaches fail? Think of them as having an analyst who has all their notes scattered in little post-it notes around the office. Even though they are great in finding the right notes depending on your question (for example, using an effective embedding and retrieval system), the answer you get always depends on which notes they happened to pull for your query. Ask it differently, and you get different answers from the black box. You can never know if answer was comprehensive or if something was hallucinated. In short: RAGs to riches doesn't work!

Skimle takes a completely different approach: analysing and structuring the documents on upload enables robust and stable analysis. Skimle uses atomic LLM calls to understand the meaning of each sentence and create relevant categories across documents. The editable and clear Skimle table allows humans and AI to make sense of the data and enables tailored transparent reports. By rigorously digesting the content up front, it is possible to identify common patterns, conflicting views and changes over time.

Keen to understand more?

Have a read through our application areas, and watch our YouTube channel for more in-depth information on how Skimle can be used in practice. We are also ready to demo our product or explore together how Skimle could fit your needs. Get in touch with us!

Contact us →