ATLAS.ti serves as a sophisticated workbench for qualitative data analysis (QDA), helping researchers and professionals uncover meaningful insights from unstructured information. By integrating advanced machine learning with traditional coding tools, this software transforms raw text, audio, video, and image files into navigable networks of evidence. It is particularly adept at handling complex projects where manual sorting would be impractical, allowing users to focus on interpretation rather than administrative organization.
Key Features
- Intentional AI Coding: Accelerates the initial analysis phase by allowing users to guide the AI with specific research questions. Instead of generic auto-coding, the system applies codes that directly address your inquiry, ensuring the results are relevant to your study goals.
- Conversational AI: Enables a direct dialogue with your documents through an interactive chat interface. You can ask questions about your data set, and the software provides answers backed by verifiable citations from your source documents, making it easier to validate findings.
- Native Auto-Transcription: Converts audio and video recordings into text directly within the Windows application. This built-in tool supports over 30 languages and includes automatic speaker detection, eliminating the need for third-party transcription services.
- Co-occurrence Analysis: Visualizes relationships between different data segments using advanced graphical tools like Sankey diagrams and heat maps. This feature helps identify where specific codes overlap, revealing hidden patterns and correlations in survey responses or interview transcripts.
- Team Collaboration & Project Merge: Facilitates joint research by allowing multiple users to work on the same project file. You can split work among team members and later merge their contributions into a master project, with conflict resolution tools to handle discrepancies.
- Dynamic Network Views: Transforms coded data into interactive conceptual maps. Users can visually link codes, memos, and quotations to build theory-driven networks that illustrate the underlying structure of their research topic.
Use Cases
PhD candidates and academic researchers utilize ATLAS.ti to perform rigorous Grounded Theory analysis on extensive interview transcripts and literature reviews. It is also essential for market research teams analyzing thousands of open-ended customer feedback responses to identify emerging sentiment trends and product issues.
By balancing automated AI suggestions with precise human control, ATLAS.ti offers a reliable environment for converting messy data into defensible research outcomes on Windows 10 and Windows 11 systems.
Version Latest — 2025
- Added a new AI-powered Auto Transcription tool that automatically converts audio and video files into text, featuring speaker detection and support for over 30 languages.
- Improved the Co-occurrence Analysis feature to allow overlapping themes to be directly converted into codes for deeper integration into the research workflow.
- Added a playback speed control button to the transcript view, allowing users to adjust the tempo of audio and video recordings during review.
- Improved the overall application performance and user interface, delivering faster response times and smoother visual navigation.
- Fixed an issue where the "Redo" function was not operating correctly.
- Fixed a bug that caused the feedback prompt to appear more frequently than intended.
