Traditional knowledge graphs fail when applied uniformly to mixed documentation types. Force a well-organized spec through the same extraction pipeline as chaotic Slack threads and you either over-process structured content or under-extract from conversations. AILang's Knowledge Amalgamator solves this by processing documents according to their inherent structure. Well-structured docs (Confluence, specs) get minimal internalization—just outlines and anchors. Why re-serialize what's already navigable? Loosely-structured sources (Slack, email) undergo heavy extraction of decisions, risks, and procedures buried in conversations. The system uses a Person-based memory architecture that mirrors human cognition: separate episodic, semantic, and procedural memory types with natural boundaries between them. The lightweight schema eliminates massive ML costs while enabling production-grade reliability. GitHub: https://github.com/pcoz/ailang/tree/main/examples/knowledge_...