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Chapter 21 Companion

Distinction
Infrastructure

The Jarvis Memory Layer, not a contact list, but a structured knowledge base of every meaningful relationship and interaction. Query "Tell me everything you know about Sarah Chen" and receive a synthesized briefing of every interaction, topic, and commitment.

CONTACTS Table

The foundation, who matters to you, how you met, and what makes them memorable. Includes AI-generated mnemonic descriptions and physical characteristics for face recognition assistance.
Schema
- Basic info (name, email, phone, company)
- Relationship context (how we met, shared interests)
- Mnemonic data (AI-generated memorable descriptions)
- Physical characteristics (for face recognition assistance)
- Last interaction, next touch due

INTERACTIONS Table

Every meaningful exchange, meetings, calls, emails, with summaries, key points, and follow-up commitments. The raw material that Jarvis synthesizes into relationship intelligence.
Schema
- Every meaningful exchange (meetings, calls, emails)
- Summaries and key points
- Location data (where we met)
- Follow-up commitments
- Topics discussed

VECTOR_CHUNKS Table

Semantic embeddings of all notes and content, enabling search by meaning, not just keywords. Includes spatial coordinates for location-tagged memories and hierarchical relationships between documents.
Schema
- Semantic embeddings of all my notes and content
- Spatial coordinates (location-tagged memories)
- Hierarchical relationships (parent documents, chunks)
- Cross-references to contacts and interactions
How the three tables work together
CONTACTS stores who matters. INTERACTIONS stores what happened. VECTOR_CHUNKS enables semantic search across everything, including location-aware queries that surface relevant memories based on where you are, not just what you're thinking about.
From AI Strategy for Business Leaders by Sean M. Bair.
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