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Reference Blueprints

End-to-end specifications designed for high scalability, fault-tolerance, and zero vendor lock-in.

Blueprint #01 Real-Time AI

Change Data Capture (CDC) to Vector Index

Automatically keeps vector database indices synchronized with transactional database updates (inserts, updates, deletes) in sub-5 seconds without periodic batch re-indexing.

Key Specifications

  • Source: PostgreSQL Write-Ahead Log (WAL) via Debezium
  • Stream Transport: Apache Kafka / Redpanda partitioned by Primary Key
  • Worker Layer: Stateless Python/FastAPI async workers with Dead-Letter Queues (DLQ)
  • Vector Target: Qdrant / pgvector with HNSW indexed vectors
Kafka Debezium Qdrant FastAPI 
┌─────────────────────────┐
│ Postgres OLTP Database  │
└────────────┬────────────┘
             │ (WAL Stream)
             ▼
┌─────────────────────────┐
│ Debezium Kafka Connect  │
└────────────┬────────────┘
             │ (JSON/Avro)
             ▼
┌─────────────────────────┐
│ Kafka Topic (cdc.tables)│
└────────────┬────────────┘
             │
      ┌──────┴──────┐
      ▼             ▼
┌───────────┐ ┌───────────┐
│ Worker 1  │ │ Worker 2  │ (Chunk & Embed)
└─────┬─────┘ └─────┬─────┘
      └──────┬──────┘
             ▼
┌─────────────────────────┐
│ Qdrant Vector Cluster   │
└─────────────────────────┘
Blueprint #02 Open Lakehouse

Decoupled Iceberg Lakehouse Architecture

Stores data in open Parquet files governed by Apache Iceberg metadata on AWS S3 or scalable object storage. Enables dual query compute engines: Snowflake for heavy BI queries and DuckDB for lightweight local/API analytics.

Key Specifications

  • Storage: S3 / Object Storage (zero egress fees)
  • Catalog: Apache Polaris / REST Catalog protocol
  • Transformation: dbt-core with SQLMesh / PyIceberg
  • Query Engines: Snowflake External Tables + DuckDB in-process
Apache Iceberg Object Storage DuckDB dbt 
┌─────────────────────────────────┐
│ Apache Iceberg (Object Store)   │
└────────────────┬────────────────┘
                 │ Open Parquet Data + Metadata
                 ▼
┌─────────────────────────────────┐
│ Apache Polaris / REST Catalog   │
└────────┬───────────────┬────────┘
         │               │
         ▼               ▼
┌────────────────┐ ┌───────────────┐
│ Snowflake / DW │ │ DuckDB Engine │
│ Enterprise BI  │ │ API Workloads │
└────────────────┘ └───────────────┘
Blueprint #03 Multi-Agent System

Multi-Agent Data Analyst with Self-Correction

An autonomous LangGraph supervisory network where specialized agents generate SQL, execute in sandboxed read-only replicas, chart results, and critique answers before presentation.

Key Specifications

  • Supervisor: Central LangGraph router managing shared state
  • SQL Generator: Schema-aware agent with catalog schema injection
  • Sandbox Execution: Read-only database connection with strict timeouts
  • Quality Critic: Reflection agent validating against original query
LangGraph Python Postgres Evals 
        ┌─────────────────────────┐
        │ Human User Query        │
        └────────────┬────────────┘
                     │
                     ▼
        ┌─────────────────────────┐
        │ Supervisor Coordinator  │
        └──────┬───────────┬──────┘
               │           │
       ┌───────┘           └───────┐
       ▼                           ▼
┌──────────────┐            ┌──────────────┐
│ SQL Writer   │            │ Chart Gen    │
│ Agent        │            │ Agent        │
└──────┬───────┘            └──────┬───────┘
       │ Exec Sandbox              │
       ▼                           ▼
┌──────────────┐            ┌──────────────┐
│ Query Exec   │            │ Critic / QA  │
└──────┬───────┘            └──────┬───────┘
       └─────────────┬─────────────┘
                     ▼
        ┌─────────────────────────┐
        │ Final Validated Report  │
        └─────────────────────────┘
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