Why should you care?

PostgresVerse AI analytics platform serves real-time recommendations from same tenant data analysts query — architecture avoids three divergent copies.

See it live — copy this example

Run in pgAdmin or psql.

-- Layered roles in PostgresVerse
CREATE SCHEMA ai;
CREATE TABLE ai.user_features (
  user_id bigint PRIMARY KEY,
  feature_vector vector(8),
  updated_at timestamptz DEFAULT now()
);
GRANT USAGE ON SCHEMA ai TO api_reader;
GRANT SELECT ON ai.user_features TO api_reader;

What happened?

• Dedicated ai schema isolates ML tables.
• Vectors live beside user_id FK world.
• api_reader gets read on features for serving; writers separate.

Practice next

1. Create ai schema and user_features table.
2. GRANT schema-scoped permissions to API role.
3. Document data flow: OLTP → feature job → ai.user_features.
4. Foreign key user_features.user_id → customers.customer_id.
5. Materialized view joining orders aggregates + feature_vector for batch.

Remember

Schema separation clarifies OLTP vs AI assets. Replicate or ETL features on schedule. Same PostgresVerse cluster or dedicated vector replica — measure load.

PostgresVerse ML platform

Tenant RLS applies to ai.user_features; serving API and dashboard share one source.

Outcome: Compliance sign-off faster than siloed vector DB without ACLs.