Six months ago you recommended switching your client's invoicing tool. Last week they asked why. You have no idea - the conversation happened in three meetings, a Slack thread, and a spreadsheet comparison no one archived. Your AI assistant is useless here too: it only knows what you paste into the prompt.
This is not a context-window problem. It is a memory architecture problem.
Why vector search alone is not enough
Most "persistent memory" solutions for LLMs work by storing past exchanges as text chunks and retrieving them by cosine similarity. Ask "what did we decide about the invoicing tool?" and a chunk mentioning the decision floats to the top - if your query looks like the answer.
It breaks the moment you ask why. The reason the CFO pushed back on the original tool was buried in a budget meeting note that shares no words with "invoicing decision". Pure vector search is blind to it by construction.
