Oracle

Deep expertise across Oracle AI Database 26ai, 19c, Exadata, ODA, RAC, Data Guard, and GoldenGate. We have managed Oracle estates at every scale.

26ai19cRACData GuardExadata

PostgreSQL

Production PostgreSQL engineering with pgvector for AI workloads, logical replication, and high availability through Patroni.

pgvectorPatroniReplicationPgBouncer

MySQL

MySQL and MariaDB performance tuning, InnoDB Cluster high availability, Group Replication, and migration planning at production scale.

InnoDB ClusterGroup ReplicationMariaDB

SQL Server

SQL Server performance optimization, Always On Availability Groups, Azure SQL migrations, and cross-platform moves to PostgreSQL or Oracle.

Always OnAzure SQLSSIS

AI Platform

AI belongs inside your database. We put it there.

The Database Ninja team builds vector search, retrieval-augmented generation pipelines, and semantic indexing directly into your existing database platform. No separate vector store. No data duplication. Production-ready architecture from day one.

Learn more

Overview

Native AI infrastructure built on the platform you already run.

Every vendor wants to sell you a new vector database. The reality is that the databases you already run can support production AI workloads today. Oracle AI Database 26ai ships with native vector search. PostgreSQL has pgvector. SQL Server integrates with Azure AI. MySQL HeatWave has machine learning built in.

The Database Ninja team builds production vector infrastructure inside the database you already operate. That means no new system to secure, no new system to back up, no ETL pipeline to maintain, and no duplication of the source data that your AI model needs.

We design the embedding strategy, size the vector indexes, tune the similarity search queries, and integrate the whole pipeline with your existing application layer. The result is a RAG system that actually performs under load.

What's Included

What we build for you.

Vector Index Architecture

HNSW, IVFFlat, or native Oracle indexes sized and tuned for your query latency and recall requirements.

Embedding Pipeline

Automated embedding generation with OpenAI, Cohere, Voyage, or local models. Batched, cached, and idempotent.

RAG Query Design

Hybrid search combining vector similarity with traditional filters, full-text search, and metadata predicates.

Performance Benchmarking

We load-test the vector pipeline against your real query volume and tune it until it meets your latency SLA.

Security and Access Control

Row-level security on vector results, encrypted embeddings, and audit logging on every similarity query.

Application Integration

Clean SDK patterns for your backend team. Python, Java, Node.js, whatever you run. Production-grade code, not demos.

Why Database Ninja

Why a database-native approach wins.

One system to operate

Your AI data lives next to your transactional data. Same backups, same replication, same security model. One system, not two.

No data duplication

The vectors sit beside the rows they describe. No sync job, no eventual consistency, no stale embeddings when the source data changes.

Production performance

Modern database vector indexes match or beat dedicated vector stores on most workloads. We prove it with benchmarks against your own data.

Ready to start?

Start building AI on what you already have.

Tell us what you are trying to retrieve, what embedding model you want to use, and what latency your users expect. We will show you how to build it inside your current database.