selfActual provides a persistent operator model that captures a user’s work habits, thinking style, and preferences, storing this data in a user‑controlled W3C Solid vault.
Funding
Funding not disclosed
Founders
Product
Problem
Current AI chat systems treat each interaction as a fresh session, requiring users to repeatedly re‑introduce their projects, preferences, and thinking style. This lack of continuity hampers productivity and diminishes the personalization that users expect from intelligent assistants.
Solution
selfActual creates a persistent operator model that captures a user’s work habits, cognitive patterns, and preferences, storing this data in a user‑controlled W3C Solid vault. The model is accessed by AI agents through the MCP protocol, allowing any compatible AI to retrieve the same behavioral context across different platforms. Because the data remains sovereign to the user, the model travels with them, ensuring that each new AI conversation builds on prior knowledge without the AI storing personal conversation history. This approach delivers consistent, personalized interactions while preserving user ownership and privacy of the behavioral layer.
Target Audience
Primary customers are professionals and power users who rely on multiple AI assistants for work, as well as developers building AI‑driven applications that require a consistent, user‑specific behavioral layer.
Features
- Persistent operator model that continuously learns a user’s workflow, thinking style, and preferences
- Storage of the model in a W3C Solid vault, giving users full ownership and control over their data
- MCP protocol bridge enabling AI agents from various providers (e.g., Claude, ChatGPT) to access the same behavioral context
- Cross‑platform portability so the user’s personal model follows them between different AI services
- Sovereign architecture that keeps personal behavioral data out of AI providers’ servers, enhancing privacy and data security