CogniFlow
offline-first · wellbeing · AI
AI Project · Mental Health · Productivity
CogniFlow is an intelligent AI assistant running locally on your device, which detects early signs of cognitive overload and helps you regain focus before frustration, performance drops, or burnout appear.
CogniFlow was designed as a predictive system that identifies early signals of cognitive overload based on anonymous behavioral patterns — without content analysis and without recording images.
The system does not record camera images or keyboard content. Only aggregated behavioral indicators are processed.
Analysis is performed directly on the user’s device, without network latency or dependence on the cloud.
The user decides whether to participate in the voluntary pilot mode and can disable it at any time.
CogniFlow’s architecture was designed in the spirit of privacy-by-design and ethical AI, with a focus on the user’s long-term wellbeing.
The system was designed to support focus and mental health without violating user privacy.
CogniFlow observes subtle work patterns — typing speed, mouse movements, and shifts in attention — without recording content or images.
A local AI model detects early signs of cognitive overload and declining concentration before the user notices them.
The system suggests short breaks, rhythm adjustments, or micro-interventions, helping maintain focus without interrupting flow.
CogniFlow was designed with privacy, transparency, and full user control over data at its core.
All analysis is performed directly on the user’s device. Biometric and behavioral data is not sent to the cloud or stored on external servers in standard operation mode.
The system does not record camera images, keyboard content, or detailed personal data. Only anonymous behavioral patterns are analyzed.
The user decides which information is processed and whether anonymous statistics may be optionally synchronized across devices.
The project complies with the European approach to data protection, principles of ethical artificial intelligence, and the idea of technology that supports human wellbeing.
CogniFlow can operate in two modes. By default, it protects full user privacy, and optionally allows participation in a pilot program that helps develop future versions of the system.
In pilot mode, only anonymous system performance indicators are used (e.g. fatigue level, focus duration, responses to interventions). The data contains no content, images, or personal information and is used exclusively for research and development purposes.
The user can disable pilot mode at any time and delete locally stored data.
Participation in the pilot program and any optional synchronization of anonymous metrics with cloud infrastructure are entirely optional and disabled by default.
CogniFlow was designed as an offline-first system, where the cloud plays a strictly auxiliary role.
Artificial intelligence models operate directly on the user’s device, analyzing behavioral and physiological signals in real time.
This approach minimizes latency, increases reliability, and eliminates the need to transmit sensitive data.
Microsoft Azure services are used exclusively as a supporting environment during the development process and optional pilot phase — without participating in real-time processing of user data.
The system architecture ensures full offline functionality, while cloud integration is optional and strictly aligned with privacy principles and informed user consent.
CogniFlow is developed by a four-person, interdisciplinary team combining expertise in artificial intelligence, software engineering, and user experience design.
UI / UX · Interface design
Responsible for interface design and user experience, with a strong focus on ergonomics, clarity, and principles of mental wellbeing. Ensures that CogniFlow remains intuitive, non-intrusive, and friendly in everyday use.
Machine Learning · Product concept
Responsible for the CogniFlow system concept and the design of machine learning models that detect early signs of declining focus and cognitive overload. Focuses on prediction, personalization, and privacy-preserving Edge AI architecture.
Backend · Python · Server architecture
Responsible for the design and implementation of the Python backend, including data processing logic, integrations, and preparation of the architecture for pilot mode and further system development. Ensures stability, scalability, and security of the solutions.
Frontend · Integrations · Desktop application
Responsible for communication between the frontend and backend as well as the implementation of the desktop application based on Tauri. Ensures smooth application performance, module integration, and reliable operation in offline-first mode.
The team develops the project iteratively, based on pilot programs, beta testing, and collaboration with users as well as academic and technological communities.
CogniFlow is developed through pilot programs and beta testing. If you are interested in collaboration, participating in tests, or simply want to learn more — feel free to contact us.
Direct contact:
inoutbajt@gmail.com