Customer360 Technical Spec

Customer360 is an AI-native intelligence and decision layer that unifies fragmented enterprise customer data, derives intelligence (scores, predictions, recommendations), and runs autonomous agents to recommend or execute next-best actions across the customer lifecycle.

Core capabilities: personalization, churn prevention, segmentation, next-best action, cross-sell, loyalty retention, and campaign optimisation. Differentiation: decisions and actions, not dashboards; agent-driven workflows, not copilots.

• Ingestion: Batch (CDC, daily loads) + streaming (Kafka, Webhooks, CDP events).
• Identity resolution: Deterministic + probabilistic with explainable match scores.
• Canonical Customer360 model: identity, transactions, engagement, service, loyalty, financial, digital.
• Lakehouse: Bronze → Silver → Gold (Databricks / Snowflake / Microsoft Fabric).
• Governance: Catalog, lineage, RBAC, GDPR/CCPA, consent.
• Data Confidence Score: per-attribute confidence rolled up to a profile-level trust score.

• Models: Churn (classification), Propensity, NBO (recommendation), Segmentation (clustering), Health (regression), LLM Narrator.
• Features: RFM, engagement decay, sentiment (LLM), service load, lifecycle, channel fit.
• Temporal snapshots: point-in-time correctness for training and replay.
• Outputs: scores, predictions, ranked recommendations, plain-language explanations, confidence and drift.

• Agents: Churn Prevention, Personalization, Campaign Optimization, Loyalty Retention, Cross-sell.
• Loop: Observe → Reason → Decide → Act, with full trace and replay.
• Tools: SQL, Python, internal APIs (CRM, ESP, POS, BSP).
• Memory: Short-term (session, working scratchpad) + long-term (snapshots, customer histories).
• Orchestration: Multi-step, multi-agent; deterministic policy guardrails.
• HITL: Configurable approval thresholds (value at risk, regulatory class, novelty).

• Philosophy: AI-first, decision-centric, "Ask → Understand → Recommend → Act".
• Paradigm: Chat + workspace + panels; natural language, guided prompts, contextual actions.
• Components: AI Command Center, Insight Cards, Agent Panels, Decision Workspace, 360 Profile, Segmentation Studio, Action Center.
• Trust: Every output shows confidence, "why this matters", and a clear recommended action.

• Decisions API: POST /v1/decide — context in, ranked actions out.
• Scores API: GET /v1/customers/:id/scores
• Segments API: query, materialise, and activate.
• Events: decision.created, action.executed, human.approved.
• Audit log: every decision captured with inputs, model versions, policies, and outcome.