TwinMind provides a personal AI assistant that captures and indexes user audio, meetings, and digital context for recall and summarization. The platform offers high-accuracy, real-time transcription with strong privacy controls, including on-device processing and offline mode. Users gain immediate access to synthesized insights, action items, and proactive suggestions based on their captured memories and data streams.
Funding
Funding not disclosed
Founders
Product
Problem
Individuals struggle to retain and effectively utilize the vast amount of information encountered daily through conversations, meetings, and digital content. This leads to missed action items, reduced productivity, and a reliance on manual note-taking that is often incomplete or inefficient.
Solution
TwinMind provides an AI-powered personal knowledge management system that captures and transcribes audio and digital content, creating a searchable repository of information. It leverages natural language processing to extract key insights, generate automated to-do lists, and offer proactive answers to user queries based on their personal data. The platform is designed for seamless integration into daily workflows, acting as a digital assistant to enhance recall and streamline task management.
Target Audience
The primary users are professionals, students, and knowledge workers who need to manage and leverage large volumes of information to improve personal productivity and recall.
Features
- Real-time, on-device audio transcription for meetings, lectures, and conversations across multiple languages.
- Browser extension for capturing and transcribing content from websites, PDFs, and video calls.
- AI-driven summarization of transcribed content, highlighting key decisions and action items.
- Automated task generation from meeting notes and digital content, with direct integration into calendar and task management tools.
- Deep Memory Search functionality allowing users to query their entire captured knowledge base using natural language.
- Proactive suggestion engine that surfaces relevant information and anticipates user needs based on context and historical data.
- On-device processing and local data storage with optional encrypted cloud backups to ensure user privacy and data sovereignty.
- Support for multiple Large Language Models (LLMs) with auto-selection for optimal performance.