Closed-source AI models are ineffective at securing user data. Closed-source AI models retain user data, but not FluxAI—we secure your data through RAM processing, no natural data persistence, and isolated model interactions!
LLMs like OpenAI’s ChatGPT, Anthropic’s Claude, or Google’s Gemini retain user data by logging conversations and storing prompt histories for model retraining. These practices raise concerns about data retention and ownership.
Privacy should always come first. So, rather than treating user data as fuel for model improvement, the solutions in the FluxAI product suite prioritize user-centric data sovereignty, keeping you in control of your data.
Using Meta’s open-source Llama architecture, FluxAI secures user data through isolated model interactions, no natural data persistence, in-memory input processing, and no retention of user data for model training. Let’s take a closer look!
What are isolated interactions?
Every interaction with a FluxAI solution is siloed; each session is an isolated event, and models cannot recall prompts from previous sessions. This is because FluxAI leverages a Stateless Architecture in which each conversation request is self-contained. For FluxAI to remember something from an earlier interaction, users must explicitly provide that context in a new request.
Aside from the privacy it provides, a benefit of Stateless Architecture is that its design isolates computations, enabling more focused task execution.
Stateless Architecture also simplifies horizontal scaling—in which computational workloads are distributed across multiple servers—by routing user requests to other servers without requiring session state synchronization.
What is zero natural data persistence?
Data persistence occurs when data is retained after the program that curated it has ended. In this case, it occurs when closed-source LLMs track and store conversations to reference for context in new user engagements—previous interaction data “persists” even after an exchange ends.
FluxAI does not retain conversational data from one chat to another unless opted into through a premium subscription. While data persistence enables continuous learning for models, there is no silent archiving of interaction data. Fluxers can use models knowing their inputs won’t be stored for model retraining.
What is in-memory processing?
FluxGRADER, a FluxAI document evaluation tool, utilizes in-memory processing to secure user data. This tool processes documents entirely in RAM, meaning uploaded files will never be written to permanent disk space and will be discarded once processed (and a grade is assigned). The grades that documents receive are also discarded after they are assigned.
Why does this matter?
Feeding user data into models to improve them is a double-edged sword. While retaining data can enhance model contextual outputs and indirectly reduce computational energy usage, it infringes on users’ ownership of their data.
By embedding privacy at the architectural level, FluxAI provides users with reliable trust, valuing data ownership not just as a feature but as a fundamental right. For more info on FluxAI, be sure to follow us on X and check out our blog!