Your AI chats shouldn’t start from zero every time

The hardest part for me is not storing more context, it is deciding what should become durable memory versus just chat exhaust. I would make the onboarding opinionated: ask for one workflow, one repo/docs source, and one "never make me repeat this again" rule, then show exactly what Lumi remembered after the next session.

This is a real pain point. The biggest friction with AI tools now is not only model quality, it is continuity. Every new chat resets context, and every tool switch makes the user rebuild the same working memory again.

I’d probably position Lumi less as “memory for AI chats” and more as a persistent context layer for serious AI workflows. Vaults, document retrieval, cross-session memory, and support across ChatGPT, Claude, and Gemini CLI make it feel broader than a simple chat add-on.

The one thing I’d pressure-test is the brand/domain frame. Lumi is friendly, but the category you are entering is going to be trust-heavy because people are storing documents, work context, and long-term AI memory. llmmemory.ai explains the function, but it may also make the product feel more like a technical utility than a durable workflow platform.

Xevoa .com would fit that broader direction better as a clean AI workflow and context platform brand. Same product, but with a name that gives more room if this expands into team memory, project context, RAG workspaces, or agent workflows later.

This feels like one of those products where the memory layer is valuable, but the brand has to make people trust it before they put important context inside.