Stateful Memory & DB Design

I’m building an AI-driven product and need a clear, future-proof design for both stateful in-memory storage and the persistent database layer. At this stage the expected traffic is low—only a handful of users and modest data volumes—so the solution can start lightweight, yet it must be structured so I can scale components later without painful refactoring.

Here’s what I’m looking for:
• A concise architectural overview that shows how conversational or session data is captured in memory, flushed to disk, and later retrieved for context.
• A database schema (or equivalent model) that maps every key entity the AI will touch, with indexing and partitioning notes that anticipate growth.
• Guidance on technology choice: I’m open to SQL, NoSQL, or graph stores—please weigh pros and cons against my current low-load profile and outline migration steps should demand rise.
• A brief set of migration scripts or sample code snippets to illustrate how state transitions from memory to the database layer.
• Clear acceptance criteria so I can test data consistency and retrieval latency on my side.

Keep the language of the documentation simple, include any diagrams in editable formats, and reference best practices relevant to AI applications.