Why this matters
Context Windows restraints x Test Time Compute – if you know, you know.

Hallucination Risk H ≈ τ × [ 1 + (P / W) × (1 − ρ) ] × (1 − g) × 1/√(1 + K)

If you don’t – LLMs simply cannot store and conduct long, multi-trunk conversations and iterate accurately over time. And just when you think you are making progress, the content window breaks and you are left with inaccurate, hallucinated answers.

We parse separate memory by time horizon—instant context in an in-memory key-value store, durable learning in a relational store with JSON documents and native vector search, and relationships in a concept graph. Interactions feel natural now, and personalization compounds over time.

Technically (a snippet about snippets) — A summary ladder (turn → session → concept) plus hybrid retrieval (semantic + recency + diversity) pulls the right snippets fast, not hundreds of noisy ones—cutting inference cost and improving answer quality by [+Δ metric].

Proof & safety — We prevent duplicate records at intake, use fast semantic search to retrieve the right context, let session data auto-expire while safely writing key stats to long-term storage, and keep personal data isolated and encrypted. We honor “forget-me” requests within our SLA [≤24h], and every decision is logged for audit.