TECHNOLOGY • Knowledge & RAG

RAG & Vector Embeddings

Hybrid dense and sparse vector retrieval pipelines.

Architectural Overview

High-dimensional vector embeddings paired with BM25 keyword matching for sub-100ms semantic search across enterprise document archives.

Why We Engineer With It

Sub-second semantic lookup across millions of chunks

Hybrid dense-sparse scoring preventing false positives

Chunk re-ranking with cross-encoders for maximal relevance

Zero document leakage with tenant-level namespace isolation

Key Architectural Capabilities

Text and code embedding generation

Cosine and dot-product similarity clustering

Metadata-filtered vector querying

Dynamic chunking with semantic boundary detection

Integration in ZynKode Architecture

Powers the semantic search and historical memory indexing layers in Vareqo.

PostgreSQL pgvectorMongoDB Atlas VectorPython Core

Related Infrastructure & Models

PostgreSQL

MongoDB

Python 3

OpenAI Models & API

Applied Enterprise Solutions

Autonomous AI Agent Systems

We engineer domain-specific autonomous AI agent systems that parse complex goals, query knowledge bases, and execute verified workflows across enterprise tools.

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Enterprise SaaS Platform Engineering

Full-cycle architecture, development, and scaling of multi-tenant enterprise SaaS platforms engineered for high throughput and security.

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Build With RAG & Vector Embeddings & ZynKode

Our engineering squad specializes in deploying production RAG & Vector Embeddings architectures with zero data retention guarantees.

Talk to ZynKode