The MPP Database is the final serving layer for governed AI context.
The MPP database stores analytics-ready facts, profiles, cohorts and metrics for low-latency serving. It lets operators, dashboards and Agency AI query the same governed customer intelligence instead of each building a separate copy of truth.
Serves customer 360, behavior detail, transaction facts, product affinity, cohorts, BI, attribution and lifecycle analytics at low latency.
The value is operational, not just technical.
Agency AI can retrieve customer 360, product affinity, lifecycle state, attribution and cohort context with analytical speed. That makes reasoning faster, more consistent and easier to audit.
Serves high-cardinality customer and event data at analytical speed.
Unifies detail, aggregate and cohort data for AI and BI consumers.
Keeps governed metrics queryable without hitting OLTP systems.
Provides the final data layer before AI tools, dashboards and operational workflows.
What this layer is responsible for
Receives processed event detail, transaction facts, tags, cohorts and aggregates.
Optimizes serving for customer 360, BI, attribution and lifecycle analytics.
Separates analytical reads from transactional writes and stream computation.
Acts as the governed query surface for AI tools and human operators.
What it makes usable
This is how the layer improves AI decisions.
Without a strong MPP serving layer, AI context fragments across dashboards, caches and exports. That makes answers slower, less consistent and harder to govern.
An AI agent can fetch a customer's current value, preference and eligibility in one governed query path.
A marketing analyst and AI workflow can use the same cohort definition.
A dashboard can drill from campaign performance into customer-level facts without querying OLTP.