AI Context & Memory System

The problem

AI conversations are stateless by default. Every session starts from zero, which breaks the experience of ongoing collaboration. Existing solutions — RAG pipelines, vector databases, long context windows — add complexity and cost without necessarily feeling natural.

What we designed

A bookmark-based DSL that captures just enough context for seamless conversation resumption. Each bookmark is a single parseable line encoding timestamp, context, references, state, and next actions — auto-updating as conversations progress. The system integrates with a document library for deeper reference when needed, without loading everything into context by default.

The design prioritized reliability over complexity: a system that works every time over one that’s impressive sometimes.

What it demonstrated

• 23% improvement in continuity scores during user testing
• Conversational context management without database infrastructure
• The interaction design principle that less information, better structured, outperforms more information poorly organized