Unify structured and semantic retrieval
Keeping SQL, vector search and contextual memory behind one product boundary reduces the integration and synchronization burden common in multi-database RAG stacks.
AI & MACHINE LEARNING PLATFORMS
1️⃣ SochDB – AI-Native Vector + SQL Database Platform
A comprehensive overview of the challenge, our architectural solution, and the highly scalable results delivered.
Technologies Used
Java & Spring BootPython FastAPIVector EmbeddingsDockerRESTful APIs
⚠ The Challenge
Modern AI applications require real-time semantic search and contextual memory. Traditional relational databases struggle with vector embeddings and reindexing performance, slowing down LLM-driven systems.
Architecture Overview
Embedded SQL and vector-query layer
Real-time ingestion and indexing pipeline
Context and agent-memory model
API boundary for application integration
Key Architecture Decisions
Optimize for local-first operation
An embedded deployment model supports privacy-sensitive development, predictable environments and applications that should not depend on a remote database service.
Treat memory as a database concern
Agent memory requires lifecycle, retrieval and relevance controls rather than an unstructured accumulation of prompts and responses.
Reliability and Operations
- ✓Deterministic persistence before derived indexing
- ✓Observable ingestion and query latency
- ✓Recovery-safe index updates
- ✓Clear separation between stored records and generated embeddings
Verification Approach
- ✓SQL and vector retrieval correctness tests
- ✓Ingestion and re-indexing regression tests
- ✓Latency measurements across representative datasets
- ✓Failure recovery and persistence checks
⚙ Our Solution
We engineered SochDB, an AI-native database combining Vector search + SQL querying, Real-time ingestion, Agent memory optimization, and Local-first architecture for AI workloads. Built with a microservices-first approach, SochDB enables high-speed semantic retrieval without constant reindexing.
🏆 The Result
Achieved < 50ms vector query response time, 40% lower indexing overhead, and improved RAG performance for AI agents optimized for scalable enterprise AI systems.