InfoQJerald Selvaraj19 min readintermediate
Article: Beyond Relevance: A Governance-First Architecture for Enterprise Personalization
Summary
The article proposes a governance‑first architecture for enterprise personalization, where policy‑driven steps (memory, journey graph, AI routing, scoring, trust checks, outcome simulation) shape the recommendation before it is returned. A reference FastAPI implementation demonstrates the pattern with external YAML policies and optional LLM assistance.
- Separate relevance, consent, journey memory, AI routing, and outcome estimation into distinct, inspectable pipeline stages.
- Use an Experience Memory Layer (SQLite) and Temporal Knowledge Graph to retain cross‑session context for each customer.
- Hybrid AI Orchestration Engine selects the cheapest reliable inference tier (rules, SLM, ML, or LLM) based on policy.
- Trust‑Aware Personalization Layer applies YAML‑defined governance (consent, fatigue, channel sensitivity) to modify scores or suppress offers.
Teams building regulated recommendation systems need a reproducible, auditable way to enforce consent, fatigue, and channel policies without sacrificing relevance.
6/10
