Understanding Our Large Language Model (LLM)

This document aims to provide you with a comprehensive understanding of how our language model operates, helping you to make the most out of its capabilities while ensuring privacy and security.

Security, Encryption, and User Privacy

All of the data transmitted through our system is encrypted to ensure a secure communication channel. In addition, the model does not create user profiles or store personal information, keeping your privacy a top priority.

Access Controls and Data Retention

Our robust access control mechanism protects your data by siloing them into company-specific vector spaces, ensuring that users can only access data associated with their own company/their Threads workspace. The model does not store personal data or user queries long-term. Information is not retained beyond the user's session.

User queries are not retained. Logs, messages, and documents are stored until the user requests deletion. This ensures the user's privacy and data control.

Security Audits

While regular security audits are planned for the future, we are currently focused on internal audits to prevent data leakage between user entities. Additional permissions-based access methods will be implemented.

If you have any further questions or concerns about how or LLM works, feel free to contact our support team.


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