TL;DR:
- FluxAI protects user data through RAM-based processing, stateless architecture, and a strict no-data-retention policy for retraining.
- Unlike models such as ChatGPT, Claude, or Gemini, FluxAI does not archive conversations or store interaction histories to improve its models.
- FluxGRADER, the suite’s document evaluation tool, processes files entirely in memory and discards them once the assessment is complete.
FluxAI is an artificial intelligence alternative built around user sovereignty over personal data, in contrast to closed-source language models that retain conversations and store prompt histories for system retraining.
Models such as ChatGPT by OpenAI, Claude by Anthropic, and Gemini by Google routinely log interactions. This practice raises questions about who controls the data generated in each session and for what purposes it is used. FluxAI takes the opposite stance: built on Meta’s open-source Llama architecture, it does not retain conversational data between sessions or use it to improve its models, unless the user explicitly opts into a premium subscription.
FluxAI: Privacy from the Ground Up
The core protection mechanism in FluxAI is its stateless architecture, or Stateless Architecture, in which each conversation request is an isolated, self-contained event. Models cannot retrieve prompts from previous sessions unless the user explicitly provides them in a new query.
This design not only ensures a high level of privacy: it also enables horizontal scalability, as computational workloads can be distributed across multiple servers without any need to synchronize session states.
FluxGRADER: Documents That Leave No Trace
Within the suite, FluxGRADER —the document evaluation tool— extends this principle to file processing. Uploaded documents are processed entirely in RAM, meaning they are never written to permanent disk storage. Once the assessment is assigned, both the file and the result are discarded. No residual record of any kind is retained.
The business model of the major artificial intelligence laboratories relies, in part, on the value extracted from the data their users generate. FluxAI severs that link: model improvement is not funded by the private information of those who use it.
