Scribular - 2026 Pricing, Features, Reviews & Alternatives

Scribular is an AI powered clinical documentation application designed for therapists, counselors and mental health professionals requiring secure and privacy focused session note generation. It provides automated transcription and summarization of therapy sessions through on device processing rather than relying on cloud based servers. The application enables professionals to generate structured clinical documentation ready for integration into electronic health record systems while maintaining client confidentiality.

The software captures audio from sessions conducted across multiple telehealth and video meeting platforms such as Zoom, Google Meet, Microsoft Teams, Skype, SimplePractice and VSee without requiring integration setup. A transcription engine converts spoken dialogue into text and the artificial intelligence processes the text into professionally structured clinical notes. Users can customize generated summaries by adding or removing details and by modifying the tone to match documentation preferences. Session management functionality enables encrypted storage and review of past notes on the user device with the option to delete entries at any time without account creation.

Scribular uses a local processing architecture to execute all transcription and summarization functions directly on the practitioner device. No client data is transmitted to external servers or stored in cloud infrastructure. This design approach removes the requirement for traditional compliance frameworks since data is never handled by third party processors. The software does not collect usage patterns or session content for model training purposes. The application operates as a standalone solution that does not require internet connectivity for core functionality.

The product supports both Mac and Windows operating systems as a self contained application. It is developed and maintained independently without corporate ownership structures or investor backing that might influence data handling practices or feature development priorities.