Fixing AI Memory: A Deterministic, Hallucination-Free Architecture

AI memory is broken. Picture a voice AI agent that confidently calls your spouse by the wrong name while they are sitting right next to you. The popular frameworks do not solve it: one cannot reliably extract facts from transcript chunks, another hallucinates entirely new names for a user's family. The common thread is that every tool routes memory through an LLM, so every memory operation can hallucinate. This talk presents a three-tier memory architecture built from scratch to fix that: a deterministic profile that no LLM touches, an immutable facts table where nothing is deleted (only superseded), and per-exchange vector search that runs on every conversation turn. The core idea is that AI memory fails because LLMs are left to manage it, and the fix is to keep the LLM out of the storage layer entirely. You will leave with the exact architecture, the SQL queries, the failure modes, and a live demo of the system running on stage. This is not theory. It is a real product with real paying users, built because the existing tools were not good enough.