lociv1.1.1

An AI support agent uses loci to remember customer preferences, past issues, and successful solutions. It stores important customer interactions in the 'people' and 'work' domains, allowing it to provide personalized, consistent support without forgetting key details between sessions.

A research AI uses loci to organize findings, methodologies, and source relationships across projects. It stores key insights in 'knowledge' domain with links between related concepts, enabling it to recall relevant information and avoid redundant research while maintaining context over long-term projects.

A personal AI coach uses loci to track user habits, preferences, and productivity patterns. It stores successful strategies in 'preferences' domain and lessons learned in 'work' domain, allowing it to adapt recommendations based on what has historically worked for the specific user.

A technical AI agent uses loci to remember system configurations, error patterns, and successful fixes. It stores environment quirks in 'tools' domain and recurring issues in 'work' domain, enabling faster diagnosis by recalling past solutions and system-specific behaviors.

Offer loci as a cloud-based memory management service for AI agents with premium features like advanced analytics, automated pruning recommendations, and cross-agent memory sharing. Basic tier includes core functionality while enterprise tier offers custom domains and integration support.

Sell enterprise licenses to companies deploying multiple AI agents, with features like centralized memory management, compliance controls, and integration with existing knowledge bases. Includes professional services for custom domain setup and migration from flat memory systems.

Build an ecosystem around loci by offering it as the memory layer for various AI platforms, charging platform providers for integration and taking a percentage of transactions. Provide developer tools and APIs for custom memory domain creation and specialized decay algorithms.