The context architecture for an AI teammate

A proposed architecture for proactive AI teammates organizes durable context into four stores: conversation turns, locally synced inbox and calendar documents, curated memory, and versioned drafts containing text and multimodal files. Drawing on Sauna’s use of Turso databases for isolated sessions and connected data, the discussion maps these functions to Pixeltable tables within a single versioned catalog, using computed columns and embedding indexes to search locally stored content and selectively recompute data after rule, prompt, or model changes. The example uses local MiniLM embeddings without an API key and demonstrates tenant and session filters, intentional memory entries, media-typed draft revisions, and incremental recomputation. It also distinguishes the systems’ security models: Turso supports physical database-per-tenant isolation, while Pixeltable relies on application-enforced tenant filtering and is positioned for applications needing a unified multimodal catalog, versioning, indexing, and data-processing pipelines.

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